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PMC9648703 | Tomokazu Konishi,Risako Fujiwara,Tadaaki Saito,Nozomi Satou,Yurie Hayashi,Naoko Crofts,Ikuko Iwasaki,Yoshihisa Abe,Shinpei Kawata,Tatsuya Ishikawa | Human lipoproteins comprise at least 12 different classes that are lognormally distributed | 10-11-2022 | This study presents the results of HPLC, a gentler and rapid separation method in comparison with the conventional ultracentrifugation, for 55 human serum samples. The elution patterns were analysed parametrically, and the attribute of each class was confirmed biochemically. Human samples contained 12 classes of lipoproteins, each of which may consist primarily of proteins. There are three classes of VLDLs. The level of each class was distributed lognormally, and the standard amount and the 95% range were estimated. Some lipoprotein classes with a narrow range could become ideal indicators of specific diseases. This lognormal character suggests that the levels are controlled by the synergy of multiple factors; multiple undesirable lifestyle habits may drastically increase the levels of specific lipoprotein classes. Lipoproteins in medical samples have been measured by enzymatic methods that coincide with conventional ultracentrifugation; however, the high gravity and time required for ultracentrifugation can cause sample degradation. Actually, the enzymatic methods measured the levels of several mixed classes. The targets of enzymatic methods have to be revised. | Human lipoproteins comprise at least 12 different classes that are lognormally distributed
This study presents the results of HPLC, a gentler and rapid separation method in comparison with the conventional ultracentrifugation, for 55 human serum samples. The elution patterns were analysed parametrically, and the attribute of each class was confirmed biochemically. Human samples contained 12 classes of lipoproteins, each of which may consist primarily of proteins. There are three classes of VLDLs. The level of each class was distributed lognormally, and the standard amount and the 95% range were estimated. Some lipoprotein classes with a narrow range could become ideal indicators of specific diseases. This lognormal character suggests that the levels are controlled by the synergy of multiple factors; multiple undesirable lifestyle habits may drastically increase the levels of specific lipoprotein classes. Lipoproteins in medical samples have been measured by enzymatic methods that coincide with conventional ultracentrifugation; however, the high gravity and time required for ultracentrifugation can cause sample degradation. Actually, the enzymatic methods measured the levels of several mixed classes. The targets of enzymatic methods have to be revised.
Lipoproteins are measured in two ways. The first is related to class separation for biochemical purposes [1–5]. This method uses ultracentrifugation, which spontaneously creates a salt density gradient due to centrifugal forces. Although the classes of lipoproteins are separated according to their density, this method is time-consuming and is not suitable for measuring large numbers of medical samples. The other method, which uses enzymatic processes, is used for physical examination [6]. In this method, cholesterol is chemically extracted from a certain class of lipoproteins and then measured. Several kits are available, but all are adjusted to mimic the results of the ultracentrifugation method. In the ultracentrifugation approach, the larger the particle size, the lower the density of lipoproteins; therefore, names such as very low-density lipoprotein (VLDL) and high-density lipoprotein (HDL) are used for large and small particles, respectively [7,8]. The accuracy of ultracentrifugation has been questioned in a study using rat serum [9]. The very high centrifugal force was sufficient to pull hydrophobic proteins out of the membrane [10]; additionally, complete separation can take several days, during which proteins can be degraded. There is a gentler way to separate these classes via HPLC gel filtration. This takes up to 30 min and does not require extra salts [11]. HPLC is not a novel method, but there were a few issues with how the data were analysed, as it was noted that the data were intended to be consistent with standard ultracentrifugation results. Analysis of HPLC results of rat data with parametric analysis (Materials and Methods) showed striking differences from the ultracentrifugation results [9]. All classes of lipoproteins are protein-rich particles, contrary to conventional knowledge [5,7,8]. Two new classes, LDL-antiprotease complexes (LAC), were also discovered.
Blood samples: Samples were collected after obtaining informed consent from all volunteers and approval from the ethics committee of Akita Cerebrospinal and Cardiovascular Center (ID. 19–21). All samples were anonymized prior to analysis. No postmeal time was specified for blood collection. The age of the volunteers were shown in S1 Fig in S1 File. The ratio of men to women was 1:1. The collected blood was sent to a clinical laboratory (SRL Inc., Tokyo, Japan). The serum was separated, and sent to Skylight Biotech Inc. (Akita, Japan) for further analysis using gel filtration HPLC [11]. HDL, LDL, and total cholesterol were measured using conventional enzymatic methods used in the clinical laboratory [12,13]. Forty-four healthy volunteer samples were subjected to analytical HPLC, and TG and cholesterol were monitored sequentially. Furthermore, 6 samples were subjected to preparative HPLC and then fractionated. There were no chylomicronaemic samples. The HPLC monitoring data (TG and cholesterol) were analysed parametrically [9]. This method is a parsimonious way of performing curve fit to maintain the falsifiability of the model using the minimum number of classes assumed (S2 Fig in S1 File) [14]. With many estimated classes, the fitting process will become easier; however, the assumed classes must be verified by reality. Too many assumptions make this verification difficult. The size and range of a class are presented using the position μ and scale σ of the normal distribution. We assumed that a class would contain TG and cholesterol at a certain constant rate regardless of the size differences within the class., with The amount of each TG and cholesterol presented by using another parameter. This assumption was verified through the curve-fitting process. The standard values of TG and cholesterol in each class were estimated from the full data set using a trimmed mean (0.2). Their 95% range was estimated using the median absolute deviation (MAD): the upper and lower limits of the 44 healthy samples were estimated as trimmed mean to two MADs. The standard values of the position or scale parameters of the classes, which were varied, were estimated from the trimmed mean of μ or σ2 found in each sample. In preparative HPLC, the elution was periodically fractionated. Each fraction was subjected to 5%–20% SDS-PAGE, and the proteins were detected using silver staining. Some protein bands were identified using MALDI-TOF MS (Genomine Inc., Kyungbuk, Korea) [15]. In addition, specific proteins were confirmed by western blotting after transfer to PVDF membranes. The antibodies used were as follows: anti-apoB antibody (A-6), sc-393636 AF488; apoA-I antibody (B-10), sc-376818 AF647 (Santa Cruz Biotechnology Inc., Texas, USA); anti-Lipoprotein a antibody, ab27631, (Abcam plc., London, UK). Chemiluminescence of the antibodies and silver-stained gel bands were measured using an Amersham Typhoon Scanner (Cytiva). The concentrations of proteins in the HPLC fractions, which had been diluted inevitably, were determined using the BCA assay (Takara, Shiga Japan), which bases on the same reaction but has better sensitivity than the biuret reaction that is commonly used for serum samples.
At least 12 classes of lipoproteins in normal distribution had to be presumed to fit the human data (Fig 1 and S3 Fig in S1 File). At least 12 classes of lipoproteins in the normal distribution were presumed to fit the human data (Fig 1 and S3 Fig in S1 File). The attributes of the classes were determined based on the elution pattern of the major protein components (Figs 1 and 2, and S4 Fig in S1 File); even a minor class cannot be ignored (S2 Fig in S1 File). In Fig 1 and S4 Fig in S1 File, the position of the bands in the superimposed SDS-PAGE photograph roughly corresponds to the elution time in the background graph. Moreover, the lanes of the SDS-PAGE correspond to the fraction numbers marked directly below. The elution patterns of these proteins coincided exactly with the distribution of the corresponding classes (Fig 2 and S4 Fig in S1 File). Additionally, TG and cholesterol had coincident positions and coincident scales in each class. Those are phenomena that are only observed if the estimated class of particles are actually present and if they maintain a constant content, regardless of size fluctuation. Those also suggest that the curve fitting was performed properly. When two classes with different TG/cholesterol ratios have similar diameters, and if they form a single composite peak, then the TG and cholesterol peak times will be different. This is actually seen in the relationship between LDL2 and LAC2; the CH and TG of these particles form a composite peak, but the Ch peak appeared later (Fig 1). The LAL2 has more cholesterol (Fig 3A) and is slightly smaller, causing a delay in the appearance of the cholesterol peak. CM and VLDL were eluted, consistent with observed peaks of ApoB48 and ApoB100, respectively (Figs 1A and 2A and S4A in S1 File). Most ApoB proteins appear to be degraded during TG removal. In fact, the LDL1 fraction contains less B100 than expected for a large number of particles, and the LDL2 fraction is devoid of B48. HDLs were eluted with ApoA-1 (Fig 2B and S4B Fig in S1 File). Fractions of LAC1 and LAC2 eluted with major antiproteases, Alpha-2-macroglobulin and inter-alpha-trypsin inhibitor, respectively, similar to those in rats (Fig 2C and S4C Fig in S1 File) [9]. Antiproteases, and thus LACs, are synthesised in the liver [16,17]. There were two additional classes of VLDL not observed in the rats: a class with Apolipoprotein (a) and a class composed almost entirely of TG. There were some differences observed between rat and human lipoproteins. There were three classes of VLDL particles in human lipoproteins, which contained ApoB100 (Figs 1 and 2A, and S4A in S1 File), compared to rat lipoprotein which only has one class of VLDL particles. The largest class is denoted as VLDL here; this contains more TG than cholesterol. Slightly smaller than this, very cholesterol-rich class included another lipoprotein denoted as Lp(a) protein [18,19] (Fig 4A). The size of the particles seems to be larger than that previously reported [18]; ultracentrifugation may have removed a portion of the particles. Another smaller class was noted to have a size slightly larger than that of LDL1. Estimating the cholesterol content of this class was difficult as its size is close to that of LDL1. However, as the fitting results suggested low cholesterol (S2 Table in S1 File), this is denoted as the TG-rich class (TR) in this study. These classes showed no relationship (S5A and S5B Fig in S1 File). Rather, the cholesterol carrier, Lp(a), showed a weak negative correlation with the levels of another carrier, LDL2 (S5C Fig in S1 File). Incidentally, there is a positive correlation between LP(a) and LDL1 cholesterol, suggesting that LP(a) is one of the precursors of LDL1 (S5D Fig in S1 File). There were two sizes of larger particles with the largest particles with B48 denoted as CM1. The 50–60 nm particles had B48 (Figs 1 and 2A, S3A Fig in S1 File), and the level showed a higher correlation with CM1 than VLDL (S5E and S5F in S1 File); hence, the class is denoted as CM2. These particles would be intestine-derived [7,8]. The amount of each class varied significantly among the volunteer samples (Fig 3). They were lognormally distributed, as was found from the logarithms of the data quantiles, which showed a linear relationship with the theoretical values of the normal distribution. Linear correlations were confirmed in all classes (S6 Fig in S1 File); The slope and intercept of the regression line represent σ and μ of the normal distribution, respectively; these are the distributions used to fit the curves of each sample. The parameters were estimated by robust methods, specifically MAD and trimmed means; the appropriateness of the estimation can be checked to determine whether the lines fit the plots. By accumulating z-normalized data using these parameters, zi = (log(xi) -μ) / σ for any raw data xi, the distribution can be confirmed in a more exact quantile-quantile (QQ) plot (Fig 4), where the slope is 1 and the intercept is zero. The lower part of the QQ plot bends downward, which is considered to be an artefact during curve fitting because low values are easy to ignore. Knowing that the data is lognormally distributed, the standard value and the 95% range of the classes could be estimated with accuracy, even though they were largely dispersed (S2 Table in S1 File). Note that the interval ranges inevitably become asymmetric to the standards: the higher is always wider. The ratio of TG to cholesterol in each class also fluctuated greatly (Fig 3A). Naturally, these ratios are large in the class synthesised to include TG, such as CM and VLDL. HDL1 and 2 are considered precursors to mature form of HDL (mHDL) (Fig 2B and S4B Fig in S1 File). They mature by receiving cholesterol. LDL1 was the most predominant cholesterol carrier, followed by LAC2 (Fig 3B). Some cholesterol carriers appeared within certain narrow ranges (LDL1, LACs, and HDLs). These classes may maintain cholesterol homeostasis. Conversely, variations in CM and VLDL levels were particularly high (Fig 3B). The former may depend on diet, and the latter may depend on the body’s requirements for TG as a storable energy source. Among the TG-rich classes, TR appeared to be fairly stable. HDLs were minor cholesterol carriers, contradicting the estimations of the previous study, which did not check whether each of the fractions contained ApoA-1 protein [20]. In the study by Gordon et al., they assigned the whole of a major peak of cholesterol as HDL, which seemed to be a reasonable decision, since HDL was believed to be the major cholesterol carrier from ultracentrifugation results [5,7,8]. However, in reality, the peak size is too large to be assigned to HDL alone; the structure of HDL is surrounded by ApoA-1 and therefore has a limitation in size [9,21]. In fact, ApoA-1 was only confirmed at the end of the peak (Fig 2B and S4A Fig in S1 File). Rather, the peak was mainly composed of LDL2 and LAC2 (Fig 1); these classes may have behaved as high-density particles during ultracentrifugation. However, their origins and functions are quite different from those of HDL. The amount of protein was estimated using the bicinchoninic acid assay, and the ratios of the lipids present (TG and cholesterol) were observed (Fig 4D). In both VLDL and CM classes, the TG and cholesterol levels varied considerably when compared to the protein levels; however, they always contained fewer lipids than protein, contradicting the results of ultracentrifugation [7–9]. Serum contains a large amount of albumin, and human serum samples have a larger number of immune-related proteins than rodent samples (S7 Fig in S1 File). However, this fact did not affect the estimation of the CM and VLDL content. Single proteins that did not vary in size were eluted using HPLC with a sharp normal distribution. Albumin was detected in fractions 27 and 28, close to HDL (Fig 1), and did not cross over into other fractions. Some of the immunoglobulins have a comparable size to some lipoproteins; however, as CM and VLDL are very large particles, the corresponding fractions would be free from albumin and immunoglobulins. The amounts of HDL and LDL measured by conventional enzymatic methods were much higher than those of any of the classes. This is not surprising, as those account for the majority of total cholesterol (S8 Fig in S1 File). If conventional methods extract cholesterol from certain classes with high efficiency, the observed data would reflect combinations of some classes. Such combinations were estimated as combinations with the highest correlation and similarity (Fig 5). The conventional measure of LDL would be VLDL derivatives without VLDL, and HDL would be HDLs and CM derivatives without CM1. Of course, we cannot deduce any of the true classes from the conventional measurements. In particular, the estimated HDL differed significantly from the actual amount of HDLs. Moreover, sums of lognormally distributed quantities are not informative at all, as an anomalous class may make up the majority of the sum; in a statistical sense, they do not even show average levels (S9 Fig in S1 File). Unfortunately, it is also true that total TG or cholesterol does not provide useful information. Rather, an exact measurement of each class is desirable.
Many features in human samples were similar to rat results [9]. Each elution pattern was a mixture of normal distributions (Figs 1 and 2, and S2-S4 Figs in S1 File). This shows that each class of lipoprotein is stable in serum, which is inconsistent with the scenario in which CM and VLDL lose TG by gradual degradation [5,7,8], which in turn produces more skewed distributions [9]. Rather, a single degradation of TG should have converted them into the next class at once. Humans also express the LDL-antiprotease complexes (LAC) [9]. Furthermore, HDL was a rather minor cholesterol carrier, and the major component of lipoproteins, as far as can be ascertained, was protein, not lipid. For a detailed discussion of these details, please refer to the previous study in rats [9]. However, humans differ from rats by having three types of VLDL-like particles. These differ greatly in the cholesterol/TG ratio (Fig 3); the most-cholesterol-rich was Lp(a) and the most-TG-rich was TR. Since they were each present in unrelated amounts, their biosynthesis is probably differentially regulated (S5 Fig in S1 File). Humans may be producing the appropriate class according to the body’s requirements. The particle size and ApoB protein distribution suggest that they can be classified into three lineages: CM → LDL2 → LAC2 VLDL, Lp(a) → LDL1 → LAC1 HDL2 → HDL1 → mHDL. These protein distributions were the same as those of the rats [9]. In this study, we focused on determining the attributes; hence, we did not analyse other apoproteins such as ApoC or ApoE. Certainly, we may only identify a fraction of apoproteins [9]. Additionally, it should also be noted that the number of classes presented here was the smallest to perform curve fitting (S2 Fig in S1 File). Systems with better separation may find more classes. The positions and scales of the classes are listed in S1 Table in S1 File. Classes that were initially secreted from the liver or intestine showed a larger scale. The particles may have a larger tolerance for the ratio and amount of lipids, which may have expanded this parameter. However, the elution position of the class did not change significantly between the samples. Therefore, the estimated peak diameters were within a certain range, indicating the physical stability of the particles (S1 Table in S1 File). It is not surprising that each class was lognormally distributed. For example, the transcriptome has the same distribution properties [22]. Levels of lipoproteins, as well as mRNA, are regulated by a balance between synthesis and degradation, both of which are controlled by specific factors in a multiplicative manner; this mechanism determines the distribution (S10 Fig in S1 File). Therefore, lipoprotein levels can easily change due to multiple small causes; differences in multiple lifestyle habits may worsen the medical condition. In contrast, simultaneous clinical efforts may synergistically improve lipoprotein levels. This would explain why a different set of feeds drastically altered lipoprotein levels in a rat study [23]. In contrast to rats, human serum contained higher amounts of immunoglobulins. It should be noted that rats grown in pathogen-free environments did not show detectable levels of these proteins in SDS-PAGE [9]. Some of them were as large as certain classes of lipoproteins (S7 Fig in S1 File), which interfered with the estimation of the protein abundance in these classes, which were able to be estimated in rat samples [9]. When separating lipoproteins by HPLC, it can be difficult to perform curve fitting due to the close size of some classes, depending of course on the column characteristics. In our case, LDL2 and LAC2 were both major and similar in size, and their peaks overlapped to form a composite peak. The minor mHDL, which is similar in size to these, likely leads to a larger measurement error. Nevertheless, they were ultimately measured within a certain range here (Fig 3). However, when the quantitative balance among these classes is disrupted by disease, the estimation of some classes by curve fitting may become difficult. Actually, in Fig 4, the lower side of the lognormal distribution always decreases, which is likely a result of not being able to fit the curves for lesser classes and ignoring them. The data analysis method used in this study is different from the previous methods; we parametrically analysed and divided the peaks and demanded biochemical confirmation for each assignment. This made the results quite different from previous results, which were based on ultracentrifugation analysis. What stands out most clearly is the small amount of HDL. In HPLC, the cholesterol curve has two main peaks; in the past, they were considered to be LDL and HDL. However, the latter peak was a combination of several LDL and HDL peaks. If we assume that all these peaks are HDL by neglecting the evidence displayed in Fig 2B, the results would be similar to the ultracentrifugation results, which is probably why the error occurred. The same phenomenon occurs in NMR, which assigns too many peaks to HDL [24]. In contrast to HPLC, which separates peaks by size, peak assignment in NMR was performed by the fitness of complex spectrum data obtained at various temperatures and pressures to the ultracentrifugation results. Therefore, peak assignment has been influenced by incorrect results. Fundamentally, this is because we do not know the specific spectrum of cholesterol in the presence of various classes of lipoproteins. NMR is still a promising approach, but the peak assignment needs to be revised using accurate information. Lipoproteins were probably degraded during the ultracentrifugation process, which has long been the de facto standard method of analysis but has a greater impact on the specimen than HPLC. Ultracentrifugation has many variations; however, it is basically a sequential combination of three centrifugations [1–5]. In typical density gradient centrifugation, a density gradient is formed in advance using non-ionic dense materials, such as sucrose or colloidal silica solutions. They have the density to float any lipoprotein and can reduce the required time; however, this method has not been used for lipoproteins. In isopycnic centrifugation, a salt concentration gradient is formed spontaneously by centrifugal force. Inevitably, the sample was exposed to a fairly high salt concentration (2–7.7N NaBr) for a long time. This concentration is sufficient to salt-out many proteins [8]. Eventually, the fractions that floated in the solution were sequentially collected to isolate progressively denser classes from CM to HDL. The density gradient takes approximately a day to form when the samples are exposed to endogenous proteolytic enzymes. The centrifugal force was 500,000 g. This is sufficient gravity to sediment the proteins against Brownian agitation, and it is common practice to estimate molecular weight from the sedimentation coefficient measured in this way [8]. On the other hand, HPLC is a method of separation by size; therefore, if particles of the same size are present, they become contaminants. However, CM and VLDL were > 50 nm in diameter (Fig 1). These are very large, which is evident from the fact that ribosomes are less than 30 nm in diameter. The serum does not contain cellular components such as platelets, and the presence of such large protein complexes other than lipoproteins is rare. Another protein, alpha-2-Macroglobulin, is a hydrophilic glycoprotein, and if we assume a sphere with an average density, this 720 kDa protein should be approximately 12 nm in diameter. The size of LAC is 18 nm (Fig 1); few factors other than LDL binding could cause this size increase, and the overlap of the peak with cholesterol (Fig 2B) is probably not coincidental. HPLC and ultracentrifugation differ greatly in the composition and density of CM and VLDL. The amount of protein was nearly 90% in HPLC (Fig 3D), but only trace amounts in ultracentrifugation [8]. As the average protein density is approximately 1.43, the density of the particles, of which 90% is protein, should be approximately the same. However, the densities of CM and VLDL separated by ultracentrifugation are approximately 1 [8]. This difference is understandable if the molecules are decomposed during ultracentrifugation. The centrifugal force from gravity on a 150 nm diameter protein is 12 pN, and the buoyancy force is approximately 8 pN; therefore, this particle will move downward with a force of 4 pN. If the particle remains in place, this force pulls the protein. Aquaporins and rhodopsins, which are typical hydrophobic proteins with transmembrane domains, can be pulled out of the membrane when subjected to forces of tens to 100 pN [25–27]. In addition to hydrophobic proteins such as ApoB, more loosely bound proteins could be released with even less force. Although many of the forces that have been measured with atomic force microscopy so far are limited to fairly strong bonds, such as avidin to biotin or antibody to its target, little is known about the reality of weak protein-protein interactions [28]. Even for a single fluctuation, once the bonds are dissociated, and the objects are captured by gravity, they are pulled apart. This is because the concentration of these materials during centrifugal separation is so low that an equilibrium state cannot be assumed between dissociation and binding. In addition, because salting-out proteins are insoluble in water, they will sediment with even more force. In contrast, HPLC can complete the separation in tens of minutes with a mild buffer, and it does not apply much force that could rupture the molecules. Thus, the perception of lipoproteins should be updated. They are classes of protein-rich particles, each of which has specific functions. Only limited apoproteins were studied here, but the attributes of other apoproteins are important for their functions. Hence, the classes need to be studied extensively, which will provide a deeper understanding of the pathophysiology. The levels of the classes varied among volunteers and were lognormally distributed. Some classes are narrowly controlled and are good candidates for indicators of diseases. Conventional enzymatic methods measure mixtures of multiple classes. We had much less HDL than previously believed. Because the sums of lognormally distributed numbers are not informative (S9 Fig in S1 File), the data do not provide a proper diagnostic criterion. This could be the reason why levels of conventional LDL did not indicate the prognosis of the patient [29]. However, HPLC is not suitable for large numbers of samples. Therefore, simpler methods for measuring specific classes are required. In particular, the targets of enzymatic methods should be completely refined; currently, we are not able to measure the classes for which each is intended (Fig 5). So far, these methods have been used not only in health screening methods but also in many studies. The current knowledge on lipoproteins, such as the levels of healthy HDL or LDL derived from these studies should, unfortunately, be re-evaluated using HPLC since no alternative exists. According to the lognormal distribution characteristics, independent clinical treatments, such as antilipidemic drugs, nutritional therapy, and ergotherapy, may synergistically change the levels of specific classes.
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PMC9648705 | Isabel Bergeri,Mairead G. Whelan,Harriet Ware,Lorenzo Subissi,Anthony Nardone,Hannah C. Lewis,Zihan Li,Xiaomeng Ma,Marta Valenciano,Brianna Cheng,Lubna Al Ariqi,Arash Rashidian,Joseph Okeibunor,Tasnim Azim,Pushpa Wijesinghe,Linh-Vi Le,Aisling Vaughan,Richard Pebody,Andrea Vicari,Tingting Yan,Mercedes Yanes-Lane,Christian Cao,David A. Clifton,Matthew P. Cheng,Jesse Papenburg,David Buckeridge,Niklas Bobrovitz,Rahul K. Arora,Maria D. Van Kerkhove, | Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies | 10-11-2022 | Background Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in the World Health Organization’s Unity protocol (WHO Unity) for general population seroepidemiological studies, to estimate the extent of population infection and seropositivity to the virus 2 years into the pandemic. Methods and findings We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between January 1, 2020 and May 20, 2022. The review protocol is registered with PROSPERO (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studies—those aligned with the WHO Unity protocol—were extracted and critically appraised in duplicate, with risk of bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate underascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% low- and middle-income countries [LMICs]) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/subnational scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] in June 2020 to 95.9% [92.6% to 97.8%] in December 2021, in European high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0 to 9 years and adults 60+ were at lower risk of seropositivity than adults 20 to 29 (p < 0.001 and p = 0.005, respectively). In a multivariable model using prevaccination data, stringent public health and social measures were associated with lower seroprevalence (p = 0.02). The main limitations of our methodology include that some estimates were driven by certain countries or populations being overrepresented. Conclusions In this study, we observed that global seroprevalence has risen considerably over time and with regional variation; however, over one-third of the global population are seronegative to the SARS-CoV-2 virus. Our estimates of infections based on seroprevalence far exceed reported Coronavirus Disease 2019 (COVID-19) cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions. | Global SARS-CoV-2 seroprevalence from January 2020 to April 2022: A systematic review and meta-analysis of standardized population-based studies
Our understanding of the global scale of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remains incomplete: Routine surveillance data underestimate infection and cannot infer on population immunity; there is a predominance of asymptomatic infections, and uneven access to diagnostics. We meta-analyzed SARS-CoV-2 seroprevalence studies, standardized to those described in the World Health Organization’s Unity protocol (WHO Unity) for general population seroepidemiological studies, to estimate the extent of population infection and seropositivity to the virus 2 years into the pandemic.
We conducted a systematic review and meta-analysis, searching MEDLINE, Embase, Web of Science, preprints, and grey literature for SARS-CoV-2 seroprevalence published between January 1, 2020 and May 20, 2022. The review protocol is registered with PROSPERO (CRD42020183634). We included general population cross-sectional and cohort studies meeting an assay quality threshold (90% sensitivity, 97% specificity; exceptions for humanitarian settings). We excluded studies with an unclear or closed population sample frame. Eligible studies—those aligned with the WHO Unity protocol—were extracted and critically appraised in duplicate, with risk of bias evaluated using a modified Joanna Briggs Institute checklist. We meta-analyzed seroprevalence by country and month, pooling to estimate regional and global seroprevalence over time; compared seroprevalence from infection to confirmed cases to estimate underascertainment; meta-analyzed differences in seroprevalence between demographic subgroups such as age and sex; and identified national factors associated with seroprevalence using meta-regression. We identified 513 full texts reporting 965 distinct seroprevalence studies (41% low- and middle-income countries [LMICs]) sampling 5,346,069 participants between January 2020 and April 2022, including 459 low/moderate risk of bias studies with national/subnational scope in further analysis. By September 2021, global SARS-CoV-2 seroprevalence from infection or vaccination was 59.2%, 95% CI [56.1% to 62.2%]. Overall seroprevalence rose steeply in 2021 due to infection in some regions (e.g., 26.6% [24.6 to 28.8] to 86.7% [84.6% to 88.5%] in Africa in December 2021) and vaccination and infection in others (e.g., 9.6% [8.3% to 11.0%] in June 2020 to 95.9% [92.6% to 97.8%] in December 2021, in European high-income countries [HICs]). After the emergence of Omicron in March 2022, infection-induced seroprevalence rose to 47.9% [41.0% to 54.9%] in Europe HIC and 33.7% [31.6% to 36.0%] in Americas HIC. In 2021 Quarter Three (July to September), median seroprevalence to cumulative incidence ratios ranged from around 2:1 in the Americas and Europe HICs to over 100:1 in Africa (LMICs). Children 0 to 9 years and adults 60+ were at lower risk of seropositivity than adults 20 to 29 (p < 0.001 and p = 0.005, respectively). In a multivariable model using prevaccination data, stringent public health and social measures were associated with lower seroprevalence (p = 0.02). The main limitations of our methodology include that some estimates were driven by certain countries or populations being overrepresented.
In this study, we observed that global seroprevalence has risen considerably over time and with regional variation; however, over one-third of the global population are seronegative to the SARS-CoV-2 virus. Our estimates of infections based on seroprevalence far exceed reported Coronavirus Disease 2019 (COVID-19) cases. Quality and standardized seroprevalence studies are essential to inform COVID-19 response, particularly in resource-limited regions.
The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus, continues to severely impact population health and healthcare systems. The 604 million cases and 6.5 million deaths reported as of September 7, 2022 [1] underestimate the global burden of this pandemic, particularly in low- and middle-income countries (LMICs) with limited capacity for contact tracing, diagnostic testing, and surveillance [2]. Seroprevalence studies estimate the prevalence of SARS-CoV-2 antibodies. These studies are crucial to understand the true extent of infection overall, by demographic group, and by geographic area, as well as to estimate case underascertainment. As anti-SARS-CoV-2 antibodies are highly predictive of immune protection [3,4], seroprevalence studies are also indicative of population levels of humoral immunity and therefore important to inform scenario modeling, public health planning, and national policies in response to the pandemic. Although seroprevalence provides crucial information on population-level infection dynamics, it is important to note that it does not imply protection against infection and therefore is not an appropriate measure to gauge progress towards herd immunity. During 2021, many regions have experienced third and fourth waves of SARS-CoV-2 infection [1]; concurrently, some countries have vaccinated most residents, while others remain unable to achieve high vaccine coverage due to challenges with supply and uptake [5]. A new wave of well-conducted seroprevalence studies, including many in LMICs, provides robust estimates of seroprevalence in late 2020 and into 2021 [6–8]. Synthesizing these studies is crucial to understand the shifting global dynamics and true extent of SARS-CoV-2 infection and humoral immunity. While previous global systematic reviews of seroprevalence have been conducted [9–12], these have included only studies that sampled participants in 2020 and pooled seroprevalence across all time points. These meta-analyses also highlight the importance of improved standardization and study quality to enable more robust analysis [9–11]. Estimates of seroprevalence can be difficult to compare systematically across different settings due to variations in design aspects including sampled populations, testing and analytical methods, timing in relation to waves of infection, and study quality and reporting. The World Health Organization’s Unity Initiative (henceforth “WHO Unity”) aims to help produce harmonized and representative seroprevalence study results in accordance with global equity principles [2]. The WHO Unity population-based, age-stratified seroepidemiological investigation protocol (the SEROPREV protocol) [2] provides a standard study design and laboratory approach to general population seroprevalence studies. WHO Unity and its partners have supported the implementation of SEROPREV by providing financial and technical resources, including a well-performing serologic assay. SEROPREV has been implemented in 74 countries globally and in 51 LMICs as of September 2021 [2]. Synthesizing results aligned with the standard SEROPREV protocol improves study comparability, enabling further analysis of these comparable studies to answer key questions about the progress of the pandemic globally. This systematic review and meta-analysis synthesized seroprevalence studies worldwide aligned with the SEROPREV protocol, regardless of whether the study received support from WHO. Our objectives were to (i) estimate changes in global and regional seroprevalence over time by WHO region and country income level; (ii) assess the level of undetected infection, by global and regional case ascertainment over time by calculating the ratio of seroprevalence to cumulative incidence of confirmed cases; and (iii) identify factors associated with seropositivity including demographic differences by 10-year age band and sex through meta-analysis, and study design and country-level differences through meta-regression.
We conducted a systematic review of seroprevalence studies (hereafter “studies”) published from January 1, 2020 to May 20, 2022, reported according to the Preferred Reporting Items Systematic review and Meta-Analyses (PRISMA) guideline [13] (File A in S1 Materials). We designed a primary search strategy in consultation with a health sciences librarian in MEDLINE, Embase, Web of Science, and Europe PMC using key terms such as SARS-COV-2, COVID-19, seroprevalence, and serology (full strategy and complete list of search terms in File B in S1 Materials). We attempted to mitigate possible publication bias by including both published articles and unpublished literature such as grey literature, preprints, institutional reports, and media reports. For our secondary search and article capture strategy, we invited submissions to our database through the open-access SeroTracker platform and recommendations from international experts, including literature compiled through the WHO Unity studies initiative. In order to access timely evidence and mitigate challenges with publication delay, we also contacted WHO Unity study collaborators that had not yet made results available to the general public prior to our inclusion dates, to upload their aggregate results to the open access Zenodo research data repository [14]. We accepted these templates up to 20 May 2022 in line with our primary search strategy and screened them according to the same criteria as other sources captured in our primary search. This systematic review and meta-analysis protocol was registered with PROSPERO (CRD42020183634) prior to the conduct of the review (File C in S1 Materials) [15], and searches and extractions conducted per the previously established SeroTracker protocol [16]. Studies were screened, data extracted, and critically appraised in duplicate, with these tasks shared by a team of 13 study authors (listed in Research Contributions section under data curation). We have study team members proficient in English, French, Portuguese, Spanish, and Cyrillic languages—articles in all other languages were translated using Google Translate where possible. Conflicts were resolved by consensus. Inclusion and exclusion criteria aligned with the SEROPREV standardized protocol for general population seroprevalence to minimize possible bias introduced by interstudy heterogeneity and other measures of study quality such as poor assay performance and/or sampling methods (full protocol criteria described in File D and E in S1 Materials). We included cross-sectional or longitudinal cohort studies with the objective of estimating SARS-CoV-2 seroprevalence in the general population. Restricting inclusion to direct population samples such as household surveys would have led to very little data in some regions and times, as these studies are expensive and difficult to conduct. Thus, household and community samples were included, as well as studies where a robust sampling frame was described that approximates to a wider population, such as individuals attending medical services (blood donors, pregnant mothers, primary care attendees) or residual sera taken from patients for a variety of other investigations. Finally, we also included people residing in slum dwellings, and some patient populations in humanitarian settings where the patient population in question was extensive enough to be considered a proxy sample frame (evaluated on a case-by-case basis). Both random and nonrandom (i.e., convenience, sequential, quota) sampling methods were included. Convenience samples must have a clear and defined sampling frame, i.e., studies recruiting volunteers were not included. Studies had to use serological assays with at least 90% sensitivity and 97% specificity as reported by the manufacturer or study authors through an independent evaluation of the test used (File D in S1 Materials), unless conducted in vulnerable countries as defined in the Global Humanitarian Response Plan (HRP) [17]. We employed exception criteria for HRP countries to ensure representativeness in countries where sometimes lower-performing assays were the only accessible option due to ongoing humanitarian emergencies. Studies employing dried blood spots as a specimen type must meet this threshold as determined through study author-conducted sensitivity and specificity validation using dried blood spots. Multi-assay testing algorithms were included if the combined sensitivity and specificity met these performance thresholds, using standard formulas for parallel and serial testing [18]. Complex multiple testing strategies (3+ tests used) were reviewed on a case-by-case basis by 2 study members. To accommodate these limitations and ensure study inclusion equity, we included all assay types from HRP countries regardless of their reported performance values, as long as the authors reported an assay that was independently validated from either an in-house evaluation or a WHO-approved head-to-head evaluation [19–21]. Finally, algorithms employing a commercial or author-designed binding assay followed by confirmatory testing by virus neutralization assay were included as they constitute the gold standard in serological evaluation [22]. We excluded studies sampling specific closed populations (such as prisons, care homes, or other single-institution populations), recruiting participants without a clear sampling frame approximating the target population or testing strategy, and studies that excluded people previously diagnosed with or vaccinated against COVID-19 after initial sampling.
From each study, we extracted seroprevalence estimates for the overall sample and stratified by age, sex, vaccination status, and timing of specimen collection according to the prespecified protocol. We extracted information on study population, laboratory assay used, any corrections made in estimating seroprevalence (e.g., for population or assay performance), seroprevalence, and denominator. Standardized results uploaded to Zenodo by Unity study collaborators additionally included information on the proportion of asymptomatic seropositive individuals. Our procedure for the standardized, aggregate early data results submitted by Unity collaborators was to direct study authors to input their results into a formulated standard Excel template designed to match the same data extracted during routine published study extraction. A blank version of the Excel template is available for reference [23]. These templates were uploaded directly into R for analysis in tandem with other studies included in the meta-analysis. Templates were verified by 2 independent reviewers, and we conducted follow-up to complete information with study investigators where needed. In instances where data from early reporting templates we had received were published prior to May 20, 2022, or a partial dataset was previously published, we ensured to de-duplicate these data for analysis. We evaluated cases of duplicated results on a case-by-case basis, prioritizing the authors published version by default but made exceptions where data were more complete, robust, or up-to-date in the submitted templates. Once authors published or preprinted their results, a link to the full source was added to the Zenodo repository. We critically appraised all studies using a modified version of the Joanna Briggs Institute (JBI) checklist for prevalence studies (File F in S1 Materials) [24]. To assess risk of bias, a decision rule assigned a rating of low, moderate, or high risk of bias based on the specific combination of JBI checklist ratings for that study [25]. This decision rule was developed based on guidance on estimating disease prevalence [26,27] and was validated against overall risk of bias assessments derived manually by 2 independent reviewers for previously collected seroprevalence studies in the SeroTracker database, showing good agreement with manual review (intraclass correlation 0.77, 95% CI 0.74 to 0.80; n = 2,070 studies) [25]. Early results from templates were screened and evaluated for risk of bias using the same criteria as studies captured through routine screening processes. We classified seroprevalence studies by geographical scope (local [i.e., cities, counties], subnational [i.e., provinces or states], or national), sample frame, sampling method, and type of serological assay (File G in S1 Materials). Where an article or source material contains multiple, methodologically distinct serosurveys, we split the article into multiple “studies”—for the purpose of this review, “study” means a distinct estimate. Where multiple summary estimates were available per study, we prioritized estimates based on estimate adjustment, antibody isotypes measured, test type used, and antibody targets measured (full details: File H in S1 Materials). We included multiple estimates per study when broken down by time frame in our analysis over time. Countries were classified according to WHO region [28], vulnerability via HRP status [17], and World Bank income level [29]. We anchored each estimate to the date halfway between sampling start and end (“sampling midpoint date”) to best reflect the time period of the study. To select the most representative and high-quality studies for analysis, we used only subnational or national studies rated low or moderate risk of bias to estimate seroprevalence in the general population over time and identify factors associated with seroprevalence (subdataset 1). We used only national studies rated low or moderate risk of bias to estimate case ascertainment (subdataset 2). To explore possible causes of heterogeneity among study results, we constructed a Poisson generalized linear mixed-effects model with log link function using the glmer function from the lme4 package in R [30–32]. Independent predictors were defined a priori as WHO region, income group, geographic scope, sample frame, pandemic timing, age, cumulative confirmed cases, and average public health and social measure (PHSM) stringency index [33]. To focus on factors associated with seroprevalence from infection, we included studies where less than 5% of the national population was vaccinated 2 weeks before the sampling midpoint date. We included all a priori predictors in the final model, and to evaluate the importance of each relevant predictor, we compared the Akaike information criterion (AIC) of the final model to all models dropping a single predictor at a time (full details on the model and predictor definitions: File H in S1 Materials). To estimate seroprevalence in the general population, we first produced monthly country-level estimates by meta-analyzing seroprevalence in each country, grouping studies in a 12-week rolling window considering the infrequent availability of seroprevalence studies in most countries (rma.glmm from R package metafor) [34,35]. We then produced monthly regional estimates by taking weighted averages of country estimates by population, ensuring that country contributions to these estimates are proportional to country population. We stratified these estimates by the expected key sources of heterogeneity among study results: region, income class, and time. We pooled HIC and LMIC together in the Eastern Mediterranean (EMR) and Western Pacific regions (WPR) due to the lower number of studies, and in the Africa (AFR) and South-East Asia regions (SEAR) (the only 2 HICs in these regions had no studies). We produced monthly global estimates where estimates were available for a majority of regions, calculating global estimates as a population-weighted average of regional estimates to ensure regional representation (full details: File H in S1 Materials). We produced 95% confidence intervals for the mean seroprevalence estimate, reflecting uncertainty in the summary effect size [36], and 95% prediction intervals to give a range for the predicted parameter value in a new study. All numerical results presented are from this stage. To visualize the trend in regional and global estimates over time, we fit a smooth curve to these estimates using nonparametric regression (gam from R package mgcv) [37]. We also summarized the relevant variant genome frequency in each region shared via the GISAID initiative [38]. We also estimated to what extent laboratory confirmed SARS-CoV-2 cases [39] underestimated the full extent of infections based on seroprevalence. For studies that sampled participants in 2021, we used national seroprevalence estimates and vaccination rates [40] to calculate seroprevalence attributable to infection only. In countries administering only vaccines using Spike (S) protein antigens (e.g., mRNA), we calculated the ascertainment ratio using only studies that detected anti-nucleocapsid (N) seroprevalence. In countries administering inactivated vaccines that may generate both anti-S and anti-N responses, we adjusted the reported seroprevalence using a standard formula [41]. We then produced regional and global estimates of seroprevalence using the 2-stage process described above and computed the ratio to the corresponding cumulative incidence of confirmed SARS-CoV-2 cases in the region or globally. We stratified by HIC versus LMIC in all regions. Aggregated results shared by Unity collaborators reported the proportion of seropositives that were symptomatic at some time point prior to sampling, summarized using the median and interquartile range, and tested for differences in distribution across age and sex groups using analysis of variance (ANOVA). To quantify population differences in SARS-CoV-2 seroprevalence, we identified studies with seroprevalence estimates for sex and age subgroups. We calculated the ratio in seroprevalence between groups within each study, comparing each age group to adults 20 to 29 and males to females. We then aggregated the ratios across studies using inverse variance-weighted random-effects meta-analysis. The amount of variation attributable to between-study heterogeneity versus within-study variance was quantified using the I2 statistic. Our main analysis used seroprevalence estimates uncorrected for test characteristics. As a sensitivity analysis, we also produced global and regional estimates adjusting for test characteristics through Bayesian measurement error models, with binomial sensitivity and specificity distributions. The sensitivity and specificity values for correction were prioritized from the WHO SARS-CoV-2 Test Kit Comparative Study conducted at the NRL Australia [19], followed by a multicenter evaluation of 47 commercial SARS-CoV-2 immunoassays by 41 Dutch laboratories [42], and from independent evaluations by study authors where author-designed assays were used. Data were analyzed using R statistical software version 4.1.2 [32].
We identified 173,430 titles and abstracts in our search spanning from January 1, 2020 to May 20, 2022 (Fig 1). Of these, 5,281 full-text articles were included in full text screening. A total of 513 seroprevalence data sources containing studies aligned with the SEROPREV protocol were identified, 480 published (94%) and 33 aggregated results from collaborators (6%), of which 12 sources were not in one of our main languages and translated via Google Translate. The 513 sources contained a total of 965 unique seroprevalence studies (detailed references and information available in Table A to C in S1 Materials). Over 1,500 full-text articles were excluded due to not containing studies compatible with the SEROPREV protocol; the main reasons for articles’ exclusion at this stage was having an incorrect sample frame for this analyses’ scope (i.e., we focused on seroprevalence in the general population and therefore excluded 1,073 articles of exclusively healthcare workers, close contacts of confirmed cases, or other specific closed populations) or not meeting our predefined assay quality performance threshold (374 articles). A total of 52% (100/194) of WHO Member States (MS) and 4 WHO countries, areas, and territories, across all 6 WHO regions, were represented among the seroprevalence studies included in the descriptive analysis (Fig A in S1 Materials). Of 47 MS, 23 were represented in AFR; 11 of 21 MS and 1 territory in EMR; 13 of 35 MS and 1 territory in AMR; 39 of 53 MS and 2 territories in EUR; 6 of 11 MS in SEAR; and 8 of 27 MS in WPR (Fig A in S1 Materials). Data from 61 of 134 LMICs and from 36 of 63 vulnerable HRP countries were included. A large proportion of studies included in the descriptive analysis were conducted in LMIC (397/965, 41%) and in vulnerable HRP countries (206/965, 21%) (Table 1). Of studies included in the meta-analysis and meta-regression, these proportions were 30% (137/459) and 14% (66/459), respectively. Of the 66 (30%) meta-analyzed studies in HRP countries, 20 had test performance values below 90% sensitivity or 97% specificity and were included due to exception criteria. Among the 965 studies included in the descriptive analysis, 42% (402/965) reported results at a local level, 36% (345/965) at a national level, and 23% (218/965) at a subnational level. The most common sampling frame and method was household and communities (52%, 500/965) and probability sampling (53%, 515/965), respectively. Within household-based samples, only 86/500 studies (17.2%) used convenience sampling. Among the testing strategies used to measure seroprevalence, most studies used ELISA (37%, 360/965) or CLIA assays (37%, 355/965), and few studies used a lateral flow immunoassay (9.4%, 91/965) or multiple assay testing algorithm (7.4%, 72/965). The majority of studies (734/965, 76%) had no vaccination at the sampling midpoint date in the country of the study (Table 1). Very few studies (14/965, 1.5%) in 4 countries (Canada, Japan, United Kingdom, United States of America) sampled participants during 2022. Most (50%, 483/965) studies were rated moderate risk of bias. A summary of overall risk of bias ratings and breakdown of each risk of bias indicator for all studies is available (Fig B and Table D in S1 Materials, respectively). Subnational and national studies at low or moderate risk were included in the subsequent results.
We estimated weighted seroprevalence in a series of separate meta-analyses each month and found in September 2021, global seroprevalence from infection or vaccination (overall seroprevalence) was 59.2%, [95% CI 56.1% to 62.2%, 95% prediction interval 51.2% to 66.7%]—an increase since the June 2020 estimate of 7.7% [CI 5.7% to 10.3%, prediction interval 4.2 to 13.8] (Table E in S1 Materials). In September 2021, global seroprevalence attributable to infection was 35.9% [CI 29.5% to 42.7%, prediction interval 22.8% to 51.4%] (Fig 2 and Table E in S1 Materials). Regional analyses began in January 2020 and ended in February 2021 through March 2022 depending on when seroprevalence studies in each region sampled participants. Overall seroprevalence in February 2021 was 42.7% [37.6% to 48.0%] in EMR (compared to 33.6% [32.8% to 34.4%] in June 2020). In April 2021, overall seroprevalence was 20.0% [18.8% to 21.2%] in AMR LMIC (2.3× since June 2020). In June 2021, overall seroprevalence was 48.7% [47.7% to 49.7%] in EUR LMIC, compared to 22.4% [21.1% to 23.8%] in July 2020. In September 2021, overall seroprevalence was 82.2% [75.9% to 87.2%] in SEAR (8.9× since June 2020). In December 2021, overall seroprevalence was 86.7% [84.6% to 88.5%] in AFR (3.5% [2.9% to 4.2%] in June 2020) and 30.3% [25.3% to 35.9%] in WPR (0.2% [0.1% to 0.4%] in June 2020). Finally, in March 2022, overall seroprevalence was 95.9% [92.3% to 97.8%] in EUR HIC (4.3% [3.4% to 5.5%] in June 2020), and 99.8% [99.7% to 99.9%] in AMR (HIC) (3.6% [2.5% to 5.2%] in June 2020). (Fig 2, middle panel, and Table E in S1 Materials). Infection-induced seroprevalence is reported in Table E in S1 Materials; for example, 47.9% [41.0% to 54.9%] of the population in EUR HIC (UK studies only) and 33.7% [31.6% to 36.0%] of the population in AMR HIC (Canada studies only) had infection-induced antibodies in March 2022. In the meta-analyses by country with at least 2 studies, 75% (188/250) showed considerable heterogeneity from 75% to 100% [36].
Snapshots of seroprevalence to confirmed case ratios, based on estimated weighted seroprevalence using national studies, are shown in Table 2. Globally, the median ratio was 51.3 infections derived from seroprevalence to 1 reported case (51.3:1) in 2020 Quarter Three, suggesting that around 1.9% of cases were reported, and 10.5:1 in 2021 Quarter Three, suggesting that around 9.5% of cases were reported. In 2020 Quarter Three, the median ratio ranged from 3.4:1 in AMR (HIC) (29.4% of cases reported) to 219.6:1 in EMR (0.5% of cases reported). In 2021 Quarter Three, this ranged from 1.8:1 in AMR (HIC) (55.6% of cases reported) to 176.7:1 in AFR (0.6% of cases reported) (Table 2).
Asymptomatic seroprevalence by age and sex subgroups for studies reporting subgroups on symptoms are shown in Fig C in S1 Materials. Median asymptomatic prevalence was similar across age groups (ANOVA p = 0.28). Median asymptomatic prevalence in males was 64.6% compared to 58.6% in females (ANOVA p = 0.47). Within studies, compared to the reference category of 20 to 29 years old, seroprevalence was significantly lower for children 0 to 9 years (prevalence ratio 0.75, 95% CI [0.67 to 0.84], p < 0.001) and adults 60+ years (0.87 [0.80 to 0.96], p = 0.005). There were no differences between other age groups nor between males and females. (Full results: Fig 3)
In the multivariable meta-regression, 329 studies remained after applying our inclusion criteria for prevaccination studies. The full model is reported in Fig 4 (model comparison and diagnostics: Table G in S1 Materials). Subnational studies reported higher seroprevalence estimates compared to national studies (PR 1.27 [1.02 to 1.59], p = 0.03). Compared to HIC, higher seroprevalence estimates were reported by low-income (PR 7.33 [3.49 to 15.41], p < 0.001), lower middle-income (PR 7.33 [3.49 to 15.41], p < 0.001), and upper middle-income countries (PR 3.97 [2.88 to 5.49], p < 0.001). Higher cumulative incidence of reported cases was associated with higher seroprevalence (PR 1.39 [1.30 to 1.49], p < 0.001), while more stringent PHSM measures up to the sampling midpoint date, continuous from 0 to 10, were associated with lower seroprevalence (PR 0.89 [0.81 to 0.98], p = 0.02). Much of the heterogeneity in effect sizes was explained by WHO region, income class, and cumulative confirmed cases. By contrast, sample frame was the least important predictor based on the AIC criterion (Table G in S1 Materials), and compared to studies that sampled households and communities, there were no differences between seroprevalence in studies that sampled blood donors (PR 1.04 [0.77 to 1.40], p = 0.79) nor residual sera (PR 1.08 [0.83 to 1.41], p = 0.55).
Regional and global estimates of seroprevalence accounting for serological test performance from independent test kit evaluations showed no qualitative differences from the primary results (Table F in S1 Materials). For example, overall global seroprevalence in September 2021 using corrected estimates was 61.5% [56.7% to 66.1%], compared to 59.2% [56.1% to 62.2%] using uncorrected estimates.
We synthesized data from over 800 seroprevalence studies worldwide (43% from LMICs) published up to May 2022 (search dates: January 1, 2020 to May 20, 2022), providing global and regional estimates of SARS-CoV-2 seroprevalence over time with substantial representation of regions with limited available seroprevalence data. We estimate that approximately 59.2% of the global population had antibodies against SARS-CoV-2 in September 2021 (35.9% when excluding vaccination). Global seroprevalence has risen considerably over time, from 7.7% a year before, in June 2020. Our findings provide evidence of regional and temporal variation in the overall seroprevalence, over 80% in SEAR and AFR in late 2021 and over 90% in AMR HIC and EUR HIC in early 2022. In WPR, there was a paucity of high-quality population-based studies in 2021, and estimated seroprevalence was as low as 30.3% in December 2021, though 1 study in Japan suggests that this has increased to over 90% in February 2022 [43]. Regional variation is driven by differences in the extent of SARS-CoV-2 infection and vaccination. This is exemplified by our monthly timeline of seroprevalence by region, 2020 to 2021, which provides estimates of evolving temporal changes of the global pandemic. We observed increases in seroprevalence following the emergence of variants in regions with available data (e.g., 6% (July 2020) to 41% (April 2021) in AFR following the Beta variant and 12% (February 2021) to 75% (August 2021) in SEAR following the Delta variant), demonstrating the substantial number of infections caused by more transmissible variants. In HIC regions, the increases in overall seroprevalence were driven by increased vaccine coverage in early 2021 (e.g., 6% (January 2021) to 95% (August 2021) in AMR HIC and 7% (January 2021) to 72% (August 2021) in EUR HIC), while we also observed increases in infection-induced seroprevalence following the Omicron variant (e.g., 7% (December 2021) to 34% (March 2022) in AMR HIC and 18% (October 2021) to 48% (March 2022) in EUR HIC). Another possibility for regional variation is the potential cross-reactivity of antibodies against P. falciparum or other common cold coronaviruses, which has been remarked upon in the literature [44–46], which may impact seroprevalence estimates in areas of Africa or elsewhere with a high incidence of malaria. Our results add global representation and principled estimation of changes in seroprevalence over time as compared to previous evidence syntheses [9,11,12]. These estimates are similar to estimates of true infections by global epidemiological models. For example, our global estimate of seroprevalence attributable to infection (35.9%) is similar to the Institute of Health Metrics and Evaluation cumulative infection incidence estimate of 42.8% on 15 September 2021 [47]. Our analysis provides an orthogonal estimate based solely on seroprevalence data, using a method that has the added value of being easily interpretable and with fewer assumed parameters. Our results provide evidence of considerable case underascertainment, indicating that many cases of SARS-CoV-2, including subclinical cases, are not captured by surveillance systems, which in many countries are based on testing of symptomatic patients, have varying sensitivity in their definitions of positive cases, or simply have limited access to testing [48]. There was wide variation in underascertainment (as estimated through seroprevalence–case ratios) in all regions, income groups, and over time, with higher ratios consistently observed in LMICs compared to HICs. Our ratios of seroprevalence to reported cases in late 2020 were comparable to other studies for AMR, EUR, and SEAR [9–12]. Our estimates of seroprevalence to cumulative incidence ratios for AFR, WPR, and EMR are novel, with no other analyses we found having systematically estimated ascertainment through seroprevalence in these regions; moreover, estimates of true infections from epidemiological models suggest that the high levels of underascertainment suggested by this study are plausible [47]. We also provide more granular evidence of significant variation in infection by age by 10-year band. Children aged <10 years, but not children aged 10 to 19, were less likely to be seropositive compared to adults aged 20 to 29 years; similarly, adults aged >60 years, but not those aged 30 to 39, 40 to 49, or 50 to 59, were less likely to be seropositive than adults 20 to 29. These findings add nuance and granularity to differences in seroprevalence by age observed by other studies [10]. Lower seroprevalence in adults 60+ could be explained by immunosenescence that can lead to quicker seroreversion [49] higher mortality and hence a lower proportion of individuals with evidence of past infection, gaps in vaccine access, or more cautious behaviour resulting in fewer infections in this age group. There are several possible explanations for lower seroprevalence in children: milder infections, which are generally associated with lower antibody titers [50]; school closures; and ineligibility for vaccination. Our multivariable model suggests that higher seroprevalence estimates were reported by low- and lower middle-income countries compared to high-income countries, with the highest seroprevalence in lower middle-income countries (prevaccination). Potential explanations for this result are multifaceted and include weaker health system functionality and performance, lower capacity to isolate, and less stringent use of and ability to effectively implement PHSM. This is also consistent with findings by Rostami and colleagues [11]. Our results suggest that an increase in overall PHSM stringency was associated with lower seroprevalence. This and other work have shown that the use of PHSM was associated with reduced SARS-CoV-2 infections, especially when implemented early and limiting population mobility [51–53]. Our model also found that subnational studies have higher estimates than national studies; one hypothesis for this is that subnational studies are often concentrated on cities or areas with denser populations, which may contribute to increased transmission of the virus. Further research is needed to validate this hypothesis. Finally, our results suggest that blood donors and residual sera studies may be good proxies for the general population, as there was no statistical difference between seroprevalence estimates in these sample frames compared to household and community samples. In line with the equity principles of the Unity initiative, our dataset had global coverage, including a broad range of LMICs (one-third of studies included in our dataset 1, n = 177) and vulnerable HRP countries (14% of included studies). Related other global meta-analyses of seroprevalence had 23% and 35% LMIC coverage, respectively [9,11]. Unity study collaborators shared timely evidence by uploading their aggregated and standardized early results to an open data repository, enabling geographic coverage and reducing publication bias.
Our regional and global meta-analysis estimates are timely, robust, and geographically diverse with estimates from all WHO regions. The laboratory and epidemiological standardization enabled by the SEROPREV protocol, as well as the analysis of only studies assessed to have low or moderate risk of bias using a validated risk of bias tool [25], enabled high-quality and comparable data. Despite this effort, there are still methodological differences between the meta-analyzed studies that may reduce their comparability. For example, 14% of studies in our analysis dataset (66/459, 18 of which were household based) were convenience samples, which are less representative than population-based probability samples. To limit this bias, we required Unity-aligned convenience samples to have a clearly defined sample frame (i.e., sampling of volunteers excluded). Our risk of bias evaluation also included subjective review of the demographic breakdown in the study, coverage of subgroup estimates, and author comments on representativeness of the sample, such that the most nonrepresentative studies were rated high risk of bias and excluded from analysis. A few limitations should be described. First, although we conducted meta-regression to explore heterogeneity of the included studies, there remained some residual heterogeneity that could not be explained quantitatively—likely driven by differences in disease transmission in the different countries and time points that serosurveys were conducted. Second, we did not account for waning of population immunity, so the present work likely underestimates the extent of past infection and case ascertainment. Third, seroprevalence studies are cumulative, meaning that results reflect all COVID-19 countermeasures implemented up to the time of participant sampling, and, thus, we cannot isolate the contributions of particular PHSM. Fourth, while we screened study eligibility based on high assay performance criteria, different serological assays may yield varying results, which should be taken into account when interpreting seroprevalence data. Some argue against combining studies using different assays, because assay performances can vary considerably leading to potential bias in the results. With the moderate seroprevalence values generally observed in our results (roughly 20% to 80%), we expect limited bias to be introduced by the different assays. Nevertheless, we conducted a sensitivity analysis adjusting estimates from individual studies with assay performance whenever available and found that global and regional estimates remained similar. Finally, at certain points in time, our meta-analysis estimates were driven by studies from specific countries—either very populous countries (i.e., SEAR: India, AMR HIC: USA, AMR LMIC: Brazil, WPR: China), or countries in regions with scarce data during the time in question (e.g., EMR: 2 countries in early 2021). We also could not produce global estimates for late 2021/early 2022 due to the delays between when studies conducted their sampling (we extracted from the “sampling midpoint”), and when these results were later published or released within our search dates.
Population-based seroprevalence studies primarily give a reliable estimate of the exposure to infection. In cases where antibodies can be measured quantitatively, it may also be possible to use them to assess the level of protection in a population, although there is currently no consensus on antibody-based correlates of protection for SARS-CoV-2 [4]. While antibodies persist in most infected individuals for up to year (with early evidence pointing at up to 18 months) [54–57], the reinfection risk with the immune-escaping Omicron variant is reported to be much higher than in previous variant of concerns (VOCs) in both vaccinated and previously infected individuals, indicating that the presence of antibodies is less indicative of a level of protection against infection. However, seroprevalence estimates remain indicative of protection against severe disease, as cellular immunity is unlikely to be disrupted even with an immune escaping VOCs. Seroprevalence studies have been invaluable throughout the COVID-19 pandemic to understand the true extent and dynamics over time of SARS-CoV-2 infection and, to some extent, immunity. Serosurveillance provides key epidemiologic information that crucially supplements other routine data sources in populations. In populations with reported high vaccine coverage, seroprevalence studies provide a supplement to vaccine coverage data and are an important tool for the evaluation of vaccination programs. In populations with low vaccine coverage, it provides an estimate of cumulative incidence of past SARS-CoV-2 infection (including asymptomatic and mild disease), true case fatality ratio, and avoid many of the limitations of passive disease reporting systems, which can be unreliable due to underdiagnosis and undernotification. Seroprevalence data can be used to compare seropositivity between different groups (age, sex geography, etc.) to identify vulnerable populations and thus inform decisions regarding the implementation of countermeasures such as vaccination programs and PHSM [58]. A key challenge in implementing serosurveillance has been timeliness of study implementation, data analysis, and reporting—as such, it will be important for public health decision-makers to prioritize investment and establish emergency-mode procedures to facilitate timely study implementation early on in future outbreak or variant emergence responses as part of overall surveillance strategy. There is also a need to continue to build national capacity with WHO and other partners to rapidly enable high-quality study implementation and communication of findings in a format friendly to decision-makers. The pandemic persists in large because of inequitable access to countermeasures tools such as vaccines; emphasizing the importance of equitable vaccine deployment globally, the strengthening of health systems and of tailored PHSM to mitigate disease transmission until high population protection is achieved. Globally standardized and quality seroprevalence data continue to be essential to inform health policy decision-making around COVID-19 control measures, particularly in capacity-limited regions with low testing capacity and vaccination rates.
In conclusion, our results show that seroprevalence has increased considerably over time, particularly from late 2021, due to mainly infection in some regions and vaccination in others. Nevertheless, there is regional variation and over one-third of the global population are seronegative to the SARS-CoV-2 virus. As our understanding of SARS-CoV-2 develops, the role of seroprevalence studies may change including the adaptation of study objectives and methodology to the epidemiological context. Currently, our global estimates of infections based on seroprevalence far exceed reported cases captured by surveillance systems. As we enter the third year of the COVID-19 pandemic, implementation of a global system or network for targeted, multi-pathogen, high-quality, and standardized collaborative serosurveillance [59,60] is a crucial next step to monitor the COVID-19 pandemic and contribute to preparedness for other emerging respiratory pathogens.
Click here for additional data file. Click here for additional data file. |
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PMC9648710 | Mary Naguib,Wael Abdel-Razek,Suzanne Estaphan,Eman Abdelsameea,Mohamed Abdel-Samiee,Nevine F. Shafik | Impact of prothrombin and factor V Leiden mutations on the progression of fibrosis in patients with chronic hepatitis C | 10-11-2022 | Background The role of thrombotic factors in the pathogenesis and progression of liver fibrosis remains obscure. We aimed to study the relationship between prothrombin G20210A (PT20210) and factor V Leiden (FVL) mutations and the progression of fibrosis and liver function in chronic HCV patients. Methods The study included 100 subjects, 88 patients with HCV-related cirrhosis (compensated: 38, decompensated: 50), and 12 controls. Patients with other viral hepatitis or coinfection, inherited metabolic disease, autoimmune hepatitis, hepatic or extrahepatic malignancy, in addition to patients with causes of hypoalbuminemia, elevated bilirubin or prolonged INR not related to cirrhosis were excluded from the study. Relevant clinical data were collected and basic laboratory tests were performed. Liver fibrosis was assessed using APRI and FIB-4 scores. FVL and PT20210 mutations were analyzed. Results FVL and PT20210 mutations were significantly higher in decompensated vs. compensated patients (32% vs. 5.3%, P = 0.001; 20% vs. 5.3%, 0.043, respectively) and absent in controls. Both mutations significantly correlated to the duration of infection, platelet count and fibrosis scores. PT20210 mutation significantly correlated to serum albumin and INR. Both mutations significantly predicted fibrosis scores, especially PT20210 (AUROC: 0.833 for APRI and 0.895 for FIB-4). Conclusions Both mutations are significantly correlated to fibrosis progression and liver profile and could be considered as markers predicting the need for early and different intervention. | Impact of prothrombin and factor V Leiden mutations on the progression of fibrosis in patients with chronic hepatitis C
The role of thrombotic factors in the pathogenesis and progression of liver fibrosis remains obscure. We aimed to study the relationship between prothrombin G20210A (PT20210) and factor V Leiden (FVL) mutations and the progression of fibrosis and liver function in chronic HCV patients.
The study included 100 subjects, 88 patients with HCV-related cirrhosis (compensated: 38, decompensated: 50), and 12 controls. Patients with other viral hepatitis or coinfection, inherited metabolic disease, autoimmune hepatitis, hepatic or extrahepatic malignancy, in addition to patients with causes of hypoalbuminemia, elevated bilirubin or prolonged INR not related to cirrhosis were excluded from the study. Relevant clinical data were collected and basic laboratory tests were performed. Liver fibrosis was assessed using APRI and FIB-4 scores. FVL and PT20210 mutations were analyzed.
FVL and PT20210 mutations were significantly higher in decompensated vs. compensated patients (32% vs. 5.3%, P = 0.001; 20% vs. 5.3%, 0.043, respectively) and absent in controls. Both mutations significantly correlated to the duration of infection, platelet count and fibrosis scores. PT20210 mutation significantly correlated to serum albumin and INR. Both mutations significantly predicted fibrosis scores, especially PT20210 (AUROC: 0.833 for APRI and 0.895 for FIB-4).
Both mutations are significantly correlated to fibrosis progression and liver profile and could be considered as markers predicting the need for early and different intervention.
Hepatitis C virus (HCV) infection is a major worldwide health problem. The highest prevalence of HCV in 2015 was detected in the Eastern Mediterranean region [1, 2]. In Egypt, the Health Issues Survey (HIS) showed a prevalence of HCV seropositivity of 10% in 2015 [3]. However, screening of about 50 million adult citizens, which started in October 2019, revealed that 4.61% had positive HCV antibodies [4]. Chronic hepatitis C occurs in a range of 70 to 80% of cases who contract HCV, which causes damage and progression to cirrhosis within 2–3 decades in 20% of patients; 25% of these patients will develop complications, such as portal hypertension, liver decompensation and hepatocellular carcinoma (HCC), with an average 5-year survival rate of 50% [5]. On the other hand, many HCV-infected patients do not develop liver-related complications even after many years of infection [6]. Therefore, it is essential to study diverse factors that might affect disease progression to early approach the patients with expected complications. Hypercoagulability is assumed to be a contributing factor for development of liver fibrosis and its progression in chronic liver disease [7]. Thrombin as the end product of coagulation cascade [8] was thought to play a critical role in activating hepatic stellate cells (HSCs) either through binding to protease activated receptor 1 (PAR1) [9] or through induction of activation of TGF- β [10] which is a potent activator of HSCs [11]. Another possible theory depicts formation of occlusive thrombi through thrombin production which results in ischemia, parenchymal destruction ending in liver fibrosis and cirrhosis [12]. FV Leiden (G1691A) and prothrombin (G20210A) mutations are well-known genetic primary hypercoagulable risk factors [13]. Single nucleotide polymorphism of FV Leiden (G1691A) results in amino acid substitution of arginine for glutamine at position 506 of the protein (R506Q), which is one of the cleavage sites of activated protein C (APC). The mutated protein is more resistant to APC cleavage impairing the negative feedback on the coagulation cascade [14]. In addition, it hinders its role in inactivation of FVIIIa [15]. On the other hand, G→A transition in nucleotide 20210 in the 3’-untranslated region leads to an impaired 3’-end cleavage signal, resulting in RNA accumulation and thus, increased prothrombin synthesis [16]. Thus, carriers of these mutations are at high risk of developing venous thromboembolisms [17]. Previous studies have reported the role of thrombotic factors in the development and progression of liver fibrogenesis [18, 19]. However, this role is not clear enough especially with mutations of the thrombotic factors. Our study aimed to examine the association between prothrombin and factor V Leiden mutations and the progression of fibrosis and liver function in patients infected with HCV.
This case-control study included 100 subjects between October 2018 and April 2019. A total of 88 adult patients with HCV-related liver cirrhosis were enrolled from the Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University. Cirrhosis was confirmed by clinical examination and radiological findings. Cirrhosis is classified in 2 prognostic stages, compensated and decompensated cirrhosis, based on presence or absence of clinically evident decompensating events especially variceal hemorrhage, ascites and hepatic encephalopathy [HE] [20]. Child-Pugh class A patients were categorized as compensated and Child-Pugh class B or C as decompensated cirrhosis [21]. Duration of infection was assessed in years from the presumed date of infection. Nearly all the patients received tartar emetic injection which was the mass anti-schistosomal treatment in Egypt between 1950s and 80s. The date of tartar emetic injection was presumed as the date of HCV infection [4]. Patients were assessed for antiviral therapy and sample withdrawal was done before starting treatment. Patients with other viral hepatitis coinfection, as hepatitis B virus (HBV), inherited metabolic disease, autoimmune hepatitis, or hepatic or extrahepatic malignancy were excluded from the study. Patients with causes of hypoalbuminemia (nephrotic syndrome or diabetic nephropathy), elevated bilirubin (hemolysis or biliary obstruction) or prolonged INR (use of anticoagulants) not related to cirrhosis were also excluded. Twelve apparently healthy subjects were included as controls from the blood donation unit of National Liver Institute, Menoufia University. They had normal liver tests and were seronegative for viral hepatitis B and C. This study was approved by the local institutional review board. Written informed consents were obtained from all eligible subjects before they were enrolled into the study. The study conforms to The Code of Ethics of the World Medical Association (Declaration of Helsinki), printed in the British Medical Journal (18 July 1964).
All the included subjects had a thorough history taking, clinical examination and abdominal ultrasonography. Blood samples were obtained for complete blood count [Sysmex XT-1800i (Sysmex Corporation, Kobe, Japan)], liver and renal tests, anti-HCV, HB surface Ag (HBsAg), anti HB core IgG (anti-HBc IgG) and alpha-fetoprotein [Cobas 6000 (Roche Diagnostics GmbH, Mannheim, Germany)]. Prothrombin time and international normalized ration (INR) were assessed using Sysmex CS-1600 (Sysmex Europe GmbH, Norderstedt, Germany).
Stage of fibrosis was assessed noninvasively using the FIB-4 [22] and aspartate aminotransferase (AST) to platelet ratio index (APRI) [23] scores as follows:
The genomic DNA was isolated from the peripheral blood of all subjects using QIAamp DNA blood Mini Kit (Qiagen, Hilden, Germany) following standard procedures according to the manufacturer’s instructions. Genomic DNA was examined for quality and quantity using the Nano Drop®-1000 spectrophotometer (Nanodrop Technologies, Inc., Wilmington, NC). Analyses of gene mutations of FVL and PT20210 were performed using hydrolysis probes via snpsig real-time PCR mutation detection/ allelic discrimination kit (Applied Biosystems Inc., CA, USA), in a lightCycler instrument (Roche Diagnostics, Mannheim, Germany). Genotyping reaction mix included 20 ng of whole blood genomic extracted DNA and the following reagents: 1 μL of primer / probe mix (Genotyping primer/probe mix contains two labelled probes homologous to the wild and mutant genotypes under investigation.), 10 μL Taqman universal master mix II No UNG (Catalog no. 4440040), and complete volume to 25 μL using RNAse/DNase free water. A negative control was included in each reaction to exclude DNA contamination. The thermal cycling profile was 2 min at 95°C for enzyme activation followed by 10 cycles of 10 s DNA denaturation at 95°C, 60 s extension at 60°C, followed by 50 cycles of 10 s DNA denaturation at 95°C, 60 s extension at 66°C where the fluorogenic data were collected through the ROX and VIC channels.
Controls and patients with compensated and decompensated cirrhosis were compared using ANOVA then post-hoc Tukey’s test for intergroup comparisons regarding quantitative variables and Chi-square test for categorical variables. Spearman’s non-parametric correlations between FVL and PT20210 mutations and other variables were assessed. The ability of FVL and PT20210 mutations to predict fibrosis as assessed by FIB-4 and APRI was tested with the receiver-operating characteristics (ROC) curve and area under the ROC curve (AUROC). Data were analyzed using IBM SPSS Statistics for Macintosh, Version 22.0 (IBM Corp, Armonk, NY, USA). All analyses were two-sided and P less than 0.05 was considered statistically significant.
This study included 88 patients with HCV-related liver cirrhosis, 38 (43.2%) with compensated, 50 (56.8%) with decompensated cirrhosis, and 12 controls. Controls and patients with compensated and decompensated cirrhosis had similar gender distribution as they were mostly males (66.7%, 57.9%, 64% respectively, P = 0.792). Controls had significantly younger age than both the compensated and decompensated groups (48 ± 4.1, 56.3 ± 6.5, 59.6 ± 7.6 years, respectively). Patients with compensated and decompensated cirrhosis were comparable regarding age (Table 1). When compared to patients with compensated cirrhosis, those with decompensated cirrhosis had significantly higher AST, alkaline phosphatase, serum total bilirubin, urea and creatinine, and INR, and more advanced fibrosis as reflected by the APRI and FIB-4 scores. They also had significantly shorter duration of infection, lower prothrombin concentration, albumin, hemoglobin, white blood cell and platelet count (Table 1).
Both mutations could not be detected in the control subjects. In the studied patients with cirrhosis, the prevalence of FVL and PT20210 mutant polymorphisms was 20.5% and 13.6% respectively. The difference between patients and controls in the distribution of FVL or PT20210 mutations failed to reach statistical significance (P = 0.576 and 1.000, respectively). No homozygous mutations were detected for FVL or PT20210 mutation. The mutant forms of FVL and PT20210 polymorphism were significantly higher in the decompensated group compared to the compensated group (P = 0.001and 0.043, respectively, Table 1).
Both FVL and PT20210 polymorphisms had significant positive correlation with APRI score (P = 0.049 and 0.004, respectively) and FIB-4 (P = 0.027 and 0.001, respectively), and negative correlation with platelet count (P = 0.036 and 0.001, respectively) and duration of infection (P = 0.008 and 0.010 respectively). PT20210 polymorphism was significantly positively correlated with INR (P = 0.016) and significantly negatively correlated with GGT, prothrombin concentration and serum albumin (P = 0.030, 0.025 and 0.003, respectively) (Table 2).
The ROC curve was used to assess the ability of FVL and PT20210 polymorphisms to predict FIB-4 and APRI scores, non-invasive indices of fibrosis, in patients with HCV-related cirrhosis. Both polymorphisms predicted significantly FIB-4 and APRI scores. However, the polymorphism of PT20210 had a higher AUROC when compared to that of FVL in prediction of both APRI (0.833 vs. 0.660, respectively) and FIB-4 (0.895 vs. was 0.689, respectively) (Table 3 and Fig 1).
Chronic infection with HCV has been an extensive health problem in Egypt [24]. Liver fibrosis is an inevitable consequence of chronic hepatitis C, which may result in development of liver cirrhosis and portal hypertension if left untreated [25]. With the revolutionary treatment of HCV with oral direct acting antivirals (DAAs), high cure rates in different clinical settings are achieved, including elderly patients, end-stage renal disease patients and those with cirrhosis [26]. However, still several disputes remain including hard to- cure cirrhosis [27]. Thus studying different factors that affect progression of fibrosis could help to identify high risk patients for development of complication of fibrosis to be recruited in early antiviral treatment and continually followed up. Also, it might provide data for development of new therapies for those patients. Thrombosis might play an influential role in the progression of liver fibrosis [28]. Thrombin not only leads to thrombosis, but also activates liver stellate cells and promotes fibrogenesis through activation of fibrogenic factors such as transforming growth factor TGF-β. Also, ischemia associated with local thrombosis of portal sinusoids up-regulates the expression and secretion of profibrogenic factors including platelet derived growth factor (PDGF). FVL polymorphism prevents normal anti-thrombin activity of activated protein C and can accelerate the fibrogenetic process [29]. Therefore, we aimed to study the potential effect of PT20210 and FVL mutations on progression of fibrosis and liver function. The FVL and PT20210 mutant polymorphisms could not be detected in the control subjects. Moreover, no homozygous mutations could be detected for FVL or PT20210 mutation in the studied controls or patients. These findings are similar to previous reports studying thrombophilic tendency among Egyptian population. Both Yaman et al [30] and El Baz et al [31] couldn`t detect FVL mutation in control and Fawzy et al [32] and Essa et al [33] reported no PT 20210 mutation in control. Jadaon [34] stated that Prothrombin G20210A mutation was found to be very rare or even absent in Asian and African populations. Besides, Yaman et al [30] and Fawzy et al [32] could not detect homozygous mutation of FVL in patient or control groups, however, El Baz et al [31] and Essa et al [33], described homozygosity of both FVL G1691A and PT 20210 in patient groups. These differences could be due to small sample sizes and studying different presentations of thrombophilic tendency. There was significant positive correlation between FVL and PT20210 mutations and the non-invasive markers of fibrosis, APRI and FIB-4. Previous studies reported increased fibrosis progression with both mutations [35, 36]. Wright et al. reported the significant correlation between FVL mutation, specifically, and the rate of progression of fibrosis in chronic HCV patients [36]. However, they did not find significant association between prothrombin mutation and rate of fibrosis. Poujol-Robert et al. [37] highlighted the importance of APC resistance as an independent risk factor for cirrhosis progression. On the other hand, Maharshak et al. found that the presence of the PT20210 mutation caused fast progression of liver fibrosis [38], however, they could not detect significant association between FVL mutation and rate of fibrosis progression, which they explained could be due to their relatively small sample size [38]. In contrast, Dik et al. [28] could not detect correlation between PT20210 or FVL mutations and fibrosis progression rate. This could be due to differences in studied population. They noticed that highest fibrosis progression rate was in Caucasian and Black patients than in Asians. Also they reported that rate of fibrosis progression was higher in chronic HCV patients than chronic HBV patients. However, they stated that factor XIII Val34Leu mutation was a risk factor for an increased rate of liver fibrogenesis in patients with chronic hepatitis B or C infection [28]. Goulding et al., [39] denied the impact of FVL or PT20210 polymorphisms on fibrosis scores in HCV-infected patients as well. This could be due to the low overall fibrosis stage in their study making it difficult to observe any difference. In addition, the mean interval time of his study was too short to definitively assess the rate of fibrosis progression. Both mutations were significantly correlated to the platelet count which was significantly lower in patients with decompensated cirrhosis compared to the compensated group. Together with the significantly higher prevalence of both mutations in the decompensated vs. compensated groups, we could deduce that FVL and PT20210 mutations are associated with the portal pressure, for which the platelet count is an indirect marker, and which is another complication of liver fibrosis. Interestingly, patients with decompensated cirrhosis had a significantly shorter presumed duration of infection and higher prevalence of both mutations when compared to those to compensated cirrhosis. These findings could imply that patients with decompensated cirrhosis are rapid fibrosers. This could be partly explained by the higher prevalence of the mutations of these thrombotic factors. We further studied the effect of both mutations on the synthetic function of the liver. We noticed that PT20210 and FVL were significantly associated with liver decompensation. PT20210 mutation significantly correlated to synthetic functions of the liver, as reflected by serum albumin, prothrombin concentration and INR. Both mutations correlated to platelet count and duration of infection. Previously, Goulding et al. [39] detected significantly lower ALT levels for factor V wild type compared to heterozygotes. However, to the best of our knowledge, this is the first study to assess the correlation of coagulation factor mutations with the progression to decompensation. Clinical implication of our findings is strict follow up of patients with cirrhosis who have such mutations due to high risk of developing complications and also it will be beneficial to know their potential role in the pathogenesis of fibrosis that may be a target of therapy in the future. Screening of hepatocellular carcinoma in rapid fibrosers with such mutation may be warranted. Limitations of our study include that it was a single center study, which included only one etiology of liver disease, HCV-related liver cirrhosis, and with no prospective follow-up.
Our study has demonstrated that factor V Leiden (G1691A) and prothrombin G20210 mutant polymorphisms are significantly correlated with the liver fibrosis, synthetic function. Notably, both mutations, especially PT20210 significantly predicted liver fibrosis. This could reflect the role of thrombotic mutations in the pathogenesis of fibrosis and could be the target of future antifibrotic therapies. Further longitudinal studies are recommended. |
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PMC9648730 | Chunxia Chen,Wan Chen,Xing Zhou,Yaoxuan Li,Xiaorong Pan,Xiaoyu Chen | Hyperbaric oxygen protects HT22 cells and PC12 cells from damage caused by oxygen-glucose deprivation/reperfusion via the inhibition of Nrf2/System Xc-/GPX4 axis-mediated ferroptosis | 10-11-2022 | This study was to investigate the protective effect of hyperbaric oxygen (HBO) on HT22 and PC12 cell damage caused by oxygen-glucose deprivation/reperfusion-induced ferroptosis. A 2-h oxygen-glucose deprivation and 24-h reperfusion model on HT22 and PC12 cells was used to simulate cerebral ischemia-reperfusion injury. Cell viabilities were detected by Cell Counting Kit-8 (CCK-8) method. The levels of reactive oxygen species (ROS) and lipid reactive oxygen species (Lipid ROS) were detected by fluorescent probes Dihydroethidium (DHE) and C11 BODIPY 581/591. Iron Colorimetric Assay Kit, malondialdehyde (MDA) and glutathione (GSH) activity assay kits were used to detect intracellular iron ion, MDA and GSHcontent. Cell ferroptosis-related ultrastructures were visualized using transmission electron microscopy (TEM). Furthermore, PCR and Western blot analyses were used to detect the expressions of ferroptosis-related genes and proteins. After receiving oxygen-glucose deprivation/reperfusion, the viabilities of HT22 and PC12 cells were significantly decreased; ROS, Lipid ROS, iron ions and MDA accumulation occurred in the cells; GSH contents decreased; TEM showed that cells were ruptured and blebbed, mitochondria atrophied and became smaller, mitochondrial ridges were reduced or even disappeared, and apoptotic bodies appeared. And the expressions of Nrf2, SLC7A11 and GPX4 genes were reduced; the expressions of p-Nrf2/Nrf2, xCT and GPX4 proteins were reduced. Notably, these parameters were significantly reversed by HBO, indicating that HBO can protect HT22 cells and PC12 cells from damage caused by oxygen-glucosedeprivation/reperfusion via the inhibition of Nrf2/System Xc-/GPX4 axis-mediated ferroptosis. | Hyperbaric oxygen protects HT22 cells and PC12 cells from damage caused by oxygen-glucose deprivation/reperfusion via the inhibition of Nrf2/System Xc-/GPX4 axis-mediated ferroptosis
This study was to investigate the protective effect of hyperbaric oxygen (HBO) on HT22 and PC12 cell damage caused by oxygen-glucose deprivation/reperfusion-induced ferroptosis. A 2-h oxygen-glucose deprivation and 24-h reperfusion model on HT22 and PC12 cells was used to simulate cerebral ischemia-reperfusion injury. Cell viabilities were detected by Cell Counting Kit-8 (CCK-8) method. The levels of reactive oxygen species (ROS) and lipid reactive oxygen species (Lipid ROS) were detected by fluorescent probes Dihydroethidium (DHE) and C11 BODIPY 581/591. Iron Colorimetric Assay Kit, malondialdehyde (MDA) and glutathione (GSH) activity assay kits were used to detect intracellular iron ion, MDA and GSHcontent. Cell ferroptosis-related ultrastructures were visualized using transmission electron microscopy (TEM). Furthermore, PCR and Western blot analyses were used to detect the expressions of ferroptosis-related genes and proteins. After receiving oxygen-glucose deprivation/reperfusion, the viabilities of HT22 and PC12 cells were significantly decreased; ROS, Lipid ROS, iron ions and MDA accumulation occurred in the cells; GSH contents decreased; TEM showed that cells were ruptured and blebbed, mitochondria atrophied and became smaller, mitochondrial ridges were reduced or even disappeared, and apoptotic bodies appeared. And the expressions of Nrf2, SLC7A11 and GPX4 genes were reduced; the expressions of p-Nrf2/Nrf2, xCT and GPX4 proteins were reduced. Notably, these parameters were significantly reversed by HBO, indicating that HBO can protect HT22 cells and PC12 cells from damage caused by oxygen-glucosedeprivation/reperfusion via the inhibition of Nrf2/System Xc-/GPX4 axis-mediated ferroptosis.
Adequate cerebral blood perfusion is a key factor in maintaining normal brain function. chronic cerebral hypoperfusion (CCH) also known as chronic cerebral ischemia (CCI), prolonged ischemia of the brain can trigger neurodegeneration and eventually lead to progressive cognitive impairment. Currently, reperfusion is an effective treatment for patients with acute cerebral ischemia. However, reperfusion under severe cerebral ischemia is a double-edged sword, which often induces cerebral ischemia-reperfusion injury while restoring blood supply [1], which seriously threatens the life of patients. Recent studies have found that cell ferroptosis caused by abnormal iron metabolism may be an important pathophysiological mechanism of cerebral ischemia-reperfusion injury [2,3]. Ferroptosis was first proposed by Dixon et al. [4] in 2012. It is an iron-dependent, a new form of cell death caused by the formation of lipid peroxides. Ferroptosis is significantly different from apoptosis, necrosis, and autophagy in terms of morphology, biochemical characteristics, and gene expression [5,6]. In recent years, studies have found that ferroptosis is closely related to the occurrence and development of neurodegenerative diseases and traumatic nervous system injury diseases [7–10], and nerve cell death is the main pathological event of many neurological diseases [11–13]. Brain tissue is prone to attack by hydroxyl radicals due to the presence of a large amount of unsaturated fatty acids and low levels of antioxidant enzymes, such as glutathione peroxidase (GPX4) [14]. Therefore, neurological diseases are closely related to ferroptosis. Hyperbaric oxygen (HBO) therapy is a clinical use of pure oxygen in the environment of 2–3 times atmospheric pressure to treat ischemic and hypoxic diseases, such as carbon monoxide poisoning, cerebral infarction, decompression sickness and coronary heart disease, etc., and has obtained certain curative effects. According to the US FDA and the International Society of Underwater Hyperbaric Medicine, 13 clinical indications including decompression sickness, carbon monoxide poisoning, gas embolism, acute craniocerebral injury, and post-radiotherapy tissue damage can be treated with HBO [15]. HBO treatment has a strong effect of reducing oxidative stress, a strong anti-inflammatory effect, protecting cholinergic nerves, and delaying neuronal apoptosis [16,17]. Our previous study [18] also found that hyperbaric oxygen can protect PC12 cells from damage caused by oxygen-glucose deprivation/reperfusion. However, whether this effect is related to the inhibition of ferroptosis remains unknown. Therefore, this study used the oxygen-glucose deprivation/reperfusion injury model of HT22 cells and PC12 cells to simulate cerebral ischemia-reperfusion in vitro to explore the effect of HBO on the ferroptosis pathway.
HT22 cells were obtained from American Type Culture Collection (ATCC, Maryland, USA) and cultured in medium consisting of Dulbecco’s modified Eagle’s medium (DMEM, Gibco, USA), 10% fetal bovine serum (FBS, Gibco, USA) and 1% penicillin-streptomycin (Solarbio, Beijing). PC12 cells (ATCC, Maryland, USA) were maintained in RPMI 1640 medium (Gibco, USA) supplemented with 10% horse serum (Merck, USA), 5% FBS and 1% penicillin-streptomycin. Cells were divided into control group, model group, Hyperbaric oxygen group (HBO) group and HBO+Ferrostatin-1 (HBO+F)group. Cells of the control group were untreated. Cells of the model group were cultured in medium without sugar and serum and then put into a 37°C incubator with a mixture of 95% N2 and 5% CO2 gas for 2 h for oxygen sugar deprivation. Then, the cells were cultured with complete medium in a 37°C incubator with a mixture of 95% O2 and 5% CO2 for 24 h. Cells of the HBO group were placed in a hyperbaric chamber (Yantai Hongyuan CO., Ltd) and received pure oxygen (0.25 MPa, 60 min). Cells of the HBO+F group were received pure oxygen and then cultured in medium with Ferrostatin-1 (5 μmol/L, ferroptosis inhibitor, MedChemExpresss, USA) for 24h. This study was conducted in accordance with the Declaration of Helsinki and approved by Ethics Committee of People’s Hospital of Guangxi Zhuang Autonomous Region.
Cell viability was determined using a Cell Counting Kit-8 (CCK8, Dojindo, Japan) assay. Briefly, after cells in each group received treatment accordingly, 10 μL of CCK-8 solution was added to each well and then the cells were incubated at 37°C in a 95% air/5% CO2 atmosphere for 1 h. The optical density of the plate was then measured at 450 nm by using a microplate reader.
Intracellular iron content was measured by Iron Colorimetric Assay Kit (APPLYGEN, Beijing). Cells in each group were received treatment accordingly and washed twice with cold PBS. 200 μl of Mixture A (prepared according to the instructions) was added to each well and the cells were incubated at a 60°C water bath for 1 h. 60 μl iron ion detection reagent was added to each well and the cells were incubated at room temperature for 30 min. The optical density of the plate was then measured at 570 nm by using a microplate reader.
Intracellular reactive oxygen species (ROS) level was measured by superoxide anion fluorescent probe (Dihydroethidium, DHE, Beyotime). Cells in each group were received treatment accordingly and washed twice with PBS. 1 ml of DHE diluted in DMEM was added to each well and the cells were incubated at 37°C in the dark for 20 min and then were washed with DMEM 3 times. The red fluorescence intensity was observed under a fluorescent inverted microscope and was calculated by Image J software.
Lipid ROS level was determined using BODIPYTM 581 /591 C11 dye (GLPBIO). Cells in each group were received treatment accordingly and washed twice with PBS. The cells were added with 10μmol /L BODIPYTM 581 /591 C11 and incubated at 37°C in the dark for 30 min and then were washed with PBS 3 times. The green fluorescence intensity was observed under a fluorescent inverted microscope and was calculated by Image J software.
Cells in each group were received treatment accordingly. Then, individual levels of MDA and GSH in cells were measured using malondialdehyde (MDA) and glutathione (GSH) activity assay kits (Beyotime, China) respectively according to the Kit Instructions. Individual contents of MDA and GSH were measured at 450 and 405 nm, respectively, with a microplate reader.
Cell ferroptosis-related ultrastructures were visualized using TEM. Briefly, cells were treated accordingly and fixed with cold 3% glutaraldehyde for 2 hours. Cells were then embedded in 1% osmium tetroxide and dehydrated. Subsequently, they were soaked in acetone and embedding medium overnight. Finally, they were stained with uranyl acetate and lead citrate. The ultrastructures of cells were examined using a transmission electron microscope (Hitachi H-7650, Japan).
Real-time qPCR was performed to detect the levels of ferroptosis-related genes. Total RNA was extracted from cells using a total RNA rapid extraction kit (Beyotime, China). Next, cDNA was synthesized from the RNA with a cDNA synthesis kit (ES Science, China). The quantity of the mRNA was measured using a Super SYBR qPCR Master Mix kit (ES Science, China) and was performed in an ABI Prism 7300 real-time thermocycler (Applied Biosystems, Foster City, CA, USA). GADPH was used as an internal reference. Results were calculated using the 2−ΔΔCt method. The primers sequence are shown in Table 1.
Western blot was used to detect the expressions of ferroptosis-related proteins. After the interventional cells were collected, a cell protein lysate (Solarbio, Beijing, China) containing PMSF (Solarbio, Beijing, China) and a phosphatase inhibitor (CWBIO, Beijing, China) was added into the cells to extract cellular proteins, and BCA protein detection kit (Beyotime, Shanghai, China) was used to detect protein concentration. The protein samples were mixed with 4 × loading buffer at a volume of 3:1, and denatured by boiling at 100°C for 8 min. After electrophoresis on SDS-PAGE gel (10%), the proteins was transferred to PVDF membranes (ISEQ00010; Millipore, Billerica, MA, USA). Subsequently, the membranes were blocked with 5% nonfat dry milk for 2 h and then incubated with p-Nrf2, Nrf2, xCT and GPX4 antibody (1: 1 000) at 4 ℃ overnight. After washed with TBST, the membranes were incubated with the anti-rabbit IgG (H+L) secondary antibodies (1: 5 000) at room temperature for 1 h. The membranes were detected using Infrared dual-color fluorescence imaging detection system (LI-COR Odyssey CLx). GADPH is used as an internal reference.
SPSS 20.0 Statistical software was used for data analysis. Measurement data were expressed as mean ± standard deviation (n = 3). One-way analysis of variance (ANOVA) was performed to compare the differences among multiple groups and P<0.05 was deemed a statistically significant difference.
The CCK-8 assay showed that HBO enhances the viabilities of oxygen-glucose deprivation-reperfusion-injured HT22 cells and PC12 cells (Fig 1). Compared with the control group, the activities of HT22 cells and PC12 cells in the model group were significantly reduced. The activities of HT22 cells and PC12 cells in the HBO group and HBO+F group were significantly higher than the model group.
The iron ion detection experiment showed that compared with the control group, iron content accumulation occurred in HT22 cells and PC12 cells of the model group, while the iron ion content in the cells of the HBO group and HBO+F group was lower than that of the model group. The results showed that HBO could reverse oxygen-glucose deprivation-reperfusion-injured-induced increase in intracellular iron content (Fig 2).
Fluorescent probe assay showed that compared with the control group, the ROS and Lipid ROS in the HT22 cells and PC12 cells in the model group were significantly increased, while the levels of ROS and Lipid ROS in the HBO group and HBO+F group were significantly lower than those in the model group. The result suggested that HBO can inhibit the generation of ROS and Lipid ROS in cells (Figs 3 and 4).
The experimental results showed that compared with the control group, the content of DMA in HT22 cells and PC12 cells in the model group was significantly increased, and the content of GSH in the cells was significantly decreased. After HBO treatment, compared with the model group, the content of MDA in the cells was significantly decreased, and the content of GSH in the cells was significantly increased (Fig 5).
The TEM results showed that compared with the control group, the cell membranes of HT22 cells and PC12 cells in the model group were ruptured and blebbed, mitochondria atrophied and became smaller, mitochondrial ridges were reduced or even disappeared, and apoptotic bodies appeared. Compared with the model group, the HT22 cells and PC12 cells in the HBO group and HBO+F group were significantly improved (Figs 6 and 7).
PCR results showed that compared with the control group, the expression levels of Nrf2, SLC7A11 and GPX4 genes in the HT22 cells and PC12 cells of the model group were significantly decreased; while the gene expressions of Nrf2, SLC7A11 and GPX4 genes in the cells of HBO group and HBO+F group were significantly higher than those in the model group (Figs 8 and 9).
WB results showed that compared with the control group, the protein expression levels of p-Nrf2/Nrf2, xCT and GPX4 proteins in the HT22 cells of the model group were significantly decreased and the expression levels of p-Nrf2/Nrf2, xCT and GPX4 proteins in the cells of HBO group and HBO+F group were significantly higher than that in the model group (Fig 10).
The process of ferroptosis is accompanied by the accumulation of a large amount of iron ions, and iron is an important cofactor. The balance of iron is essential for the proper functioning of the brain. Studies have found that iron accumulation exists in the brain of patients with many neurodegenerative diseases, and iron accumulation can lead to neurotoxicity through various mechanisms, including the production of oxygen free radicals that lead to oxidative stress, excitotoxicity, and promote inflammatory responses [19]. This study found that HBO can significantly reverse the oxygen-glucose deprivation-reperfusion-injury-induced decrease in cell viability and increase in intracellular iron content of HT22 cells and PC12 cells. It has been reported that cerebral ischemia-reperfusion can cause iron metabolism disorders in brain tissue, mainly manifested as increased iron content in brain tissue, resulting in iron overload. The increase of iron content in brain tissue can catalyze the production of free radicals to increase the force, thereby promoting lipid peroxidation and aggravating brain tissue damage [20,21]. Ferroptosis has its unique biochemical characteristics and morphology. Besides iron overload, accumulation of lipid ROS is also a major biochemical feature [22]. Morphological features include mitochondrial shrinkage, reduction or disappearance of mitochondrial cristae, increased mitochondrial membrane density, mitochondrial membrane rupture, and normal nuclear morphology, but lack of chromatin condensation [23]. This study showed that ROS, Lip-ROS and MDA were significantly increased, and GSH was significantly decreased in the cells with oxygen-glucose deprivation/reperfusion. TEM showed that the cell membrane was ruptured and blistered, mitochondria atrophied and became smaller, mitochondrial cristae were reduced or even disappeared, and apoptotic bodies appeared in the model groups. And HBO can reverse these changes. The results suggest that oxygen-glucose deprivation/reperfusion can induce ferroptosis in cells, and HBO can inhibit ferroptosis. Iron metabolism and lipid peroxidation are key factors mediating ferroptosis. Iron ions in the body are endocytosed into cells through the binding of TFR1 on the cell membrane. Iron ions are reduced to ferrous ions in the cells, and the excessive accumulation of ferrous ions in the cells will promote the generation of oxidative free radicals and lipid peroxidation product MDA through the Fenton reaction, thereby aggravating the damage of nerve cells [24,25]. Combined with our previous and current findings, we found that ferroptosis in the oxygen-glucose deprivation/reperfusion group was accompanied by apoptosis. Apoptosis is programmed series of events dependent on energy, as well as morphological features such as cell shrinkage, chromatin condensation, and presence of apoptotic bodies without inflammatory reactions [26,27]. Caspases are key molecules involved in the transduction of the apoptosis signal, and all of the pathways converge to the executioner caspase-3 [28]. The extrinsic pathway is initiated by the tumor necrosis factor (TNF) receptor family interacting with a ligand and then binds with procaspase-8 following ligand-receptor interaction to activation of caspase-3 which leads to execution of apoptosis [29,30]. The intrinsic pathway (mitochondrial pathway) employs alterations of inner mitochondrial membrane for induction of apoptosis. Apoptosis is triggered when the Bcl2-family proapoptotic proteins cause the opening of mitochondrial permeability transition pore and proapoptotic proteins into cytoplasm by interacting with apoptotic protease-activating factor 1 (Apaf-1) and procaspase-9 to constitute apoptosome [31]. An assembly of apoptosome leads to caspase-9 activation, which further activates caspase-3, for apoptotic execution [32]. TEM showed that apoptotic bodies appeared in the model groups. It suggested that apoptosis and ferroptosis coexist under certain conditions. Su et al. [33] found that reactive oxygen species can simultaneously induce apoptosis, autophagy and ferroptosis. Ye et al. [34] found that FBW7-NRA41-SCD1 axis synchronously regulates apoptosis and ferroptosis in pancreatic cancer cells. As an important intracellular antioxidant molecule, cystine/glutamate transporter (System Xc-) is the upstream node molecule in the process of ferroptosis. Its main function is to maintain the balance of cystine (Cys) intake and glutamate (Glu) excretion. After being taken up by System Xc-, cystine is reduced to cysteine in cells and is involved in the synthesis of glutathione (GSH). Glutathione can reduce reactive oxygen species and reactive nitrogen species under the action of GPX4 [35]. When System Xc- is inhibited, the introduction of cystine (Cys) into cells is hindered and the cysteine necessary for the synthesis of GSH is reduced. Due to glutathione (GSH) depletion, the activity of glutathione peroxidase 4 (GPX4) is decreased or even inactivated. Intracellular lipid oxides (ROOH) cannot be metabolized to ROH and H2O2 without oxidative toxicity. The Fenton reaction then occurs, resulting in the production of a large amount of ROS, which severely disrupts the intracellular redox balance, causes cellular lipid peroxidative damage, and attacks biological macromolecules, thereby initiating ferroptosis [36,37]. SLC7A11 (also known as xCT) is the substrate-specific subunit that constitutes System Xc- and is responsible for the transport of cystine from the extracellular to the intracellular. When cells are under oxidative stress and cysteine deficiency, nuclear factor erythroid 2 like 2 (NRF2) and activating transcription factor 4 (ATF4) can induce SLC7A11 expression [38]. The System Xc-/GSH/GPX4 pathway, as one of the main regulatory axes of ferroptosis, plays an important role in cerebral ischemia-reperfusion injury [20,21]. PCR results showed that expressions of Nrf2, SLC7A11 and GPX4 genes were decreased, especially in HT22 cells. WB experiment showed that the expressions of p-Nrf2/Nrf2, xCT and GPX4 proteins in HT22 cells in the model group were significantly decreased. Nrf2-induced decreased SLC7A11 (xCT) expression, decreased glutamate-cysteine exchange, decreased GSH content, decreased GPX4 activity, and insufficient cellular ability to scavenge lipid peroxides resulted in the accumulation of a large number of oxidative products such as MDA. At the mitochondrial membrane, the mitochondrial membrane potential decreases, ferroptosis occurs, and nerve function is impaired. The expression of these genes and proteins can be reversed after HBO treatment. This suggested that HBO can inhibit ferroptosis through the System Xc-/GSH/GPX4 pathway. In summary, our findings indicate that HBO can protect HT22 cells and PC12 cells from damage caused by oxygen-glucose deprivation/reperfusion via the inhibition of Nrf2/System Xc-/GPX4 axis-mediated ferroptosis. Our findings provide a basis for further research on the mechanism of HBO in cerebral ischemia-reperfusion.
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PMC9648731 | Margherita Coccia,Wivine Burny,Marie-Ange Demoitié,Paul Gillard,Robert A. van den Berg,Robbert van der Most | Subsequent AS01-adjuvanted vaccinations induce similar transcriptional responses in populations with different disease statuses | 10-11-2022 | Transcriptional responses to adjuvanted vaccines can vary substantially among populations. Interindividual diversity in levels of pathogen exposure, and thus of cell-mediated immunological memory at baseline, may be an important determinant of population differences in vaccine responses. Adjuvant System AS01 is used in licensed or candidate vaccines for several diseases and populations, yet the impact of pre-existing immunity on its adjuvanticity remains to be elucidated. In this exploratory post-hoc analysis of clinical trial samples (clinicalTrials.gov: NCT01424501), we compared gene expression patterns elicited by two immunizations with the candidate tuberculosis (TB) vaccine M72/AS01, between three groups of individuals with different levels of memory responses to TB antigens before vaccination. Analyzed were one group of TB-disease-treated individuals, and two groups of TB-disease-naïve individuals who were (based on purified protein derivative [PPD] skin-test results) stratified into PPD-positive and PPD-negative groups. Although TB-disease-treated individuals displayed slightly stronger transcriptional responses after each vaccine dose, functional gene signatures were overall not distinctly different between groups. Considering the similarities with the signatures found previously for other AS01-adjuvanted vaccines, many features of the response appeared to be adjuvant-driven. Across groups, cell proliferation-related signals at 7 days post-dose 1 were associated with increased anti-M72 antibody response magnitudes. These early signals were stronger in the TB-disease-treated group as compared to both TB-disease-naïve groups. Interindividual homogeneity in gene expression levels was also higher for TB-disease-treated individuals post-dose 1, but increased in all groups post-dose 2 to attain similar levels between the three groups. Altogether, strong cell-mediated memory responses at baseline accelerated and amplified transcriptional responses to a single dose of this AS01-adjuvanted vaccine, resulting in more homogenous gene expression levels among the highly-primed individuals as compared to the disease-naïve individuals. However, after a second vaccination, response heterogeneity decreased and was similar across groups, irrespective of the degree of immune memory acquired at baseline. This information can support the design and analysis of future clinical trials evaluating AS01-adjuvanted vaccines. | Subsequent AS01-adjuvanted vaccinations induce similar transcriptional responses in populations with different disease statuses
Transcriptional responses to adjuvanted vaccines can vary substantially among populations. Interindividual diversity in levels of pathogen exposure, and thus of cell-mediated immunological memory at baseline, may be an important determinant of population differences in vaccine responses. Adjuvant System AS01 is used in licensed or candidate vaccines for several diseases and populations, yet the impact of pre-existing immunity on its adjuvanticity remains to be elucidated. In this exploratory post-hoc analysis of clinical trial samples (clinicalTrials.gov: NCT01424501), we compared gene expression patterns elicited by two immunizations with the candidate tuberculosis (TB) vaccine M72/AS01, between three groups of individuals with different levels of memory responses to TB antigens before vaccination. Analyzed were one group of TB-disease-treated individuals, and two groups of TB-disease-naïve individuals who were (based on purified protein derivative [PPD] skin-test results) stratified into PPD-positive and PPD-negative groups. Although TB-disease-treated individuals displayed slightly stronger transcriptional responses after each vaccine dose, functional gene signatures were overall not distinctly different between groups. Considering the similarities with the signatures found previously for other AS01-adjuvanted vaccines, many features of the response appeared to be adjuvant-driven. Across groups, cell proliferation-related signals at 7 days post-dose 1 were associated with increased anti-M72 antibody response magnitudes. These early signals were stronger in the TB-disease-treated group as compared to both TB-disease-naïve groups. Interindividual homogeneity in gene expression levels was also higher for TB-disease-treated individuals post-dose 1, but increased in all groups post-dose 2 to attain similar levels between the three groups. Altogether, strong cell-mediated memory responses at baseline accelerated and amplified transcriptional responses to a single dose of this AS01-adjuvanted vaccine, resulting in more homogenous gene expression levels among the highly-primed individuals as compared to the disease-naïve individuals. However, after a second vaccination, response heterogeneity decreased and was similar across groups, irrespective of the degree of immune memory acquired at baseline. This information can support the design and analysis of future clinical trials evaluating AS01-adjuvanted vaccines.
The biologic understanding of a vaccine’s immunogenicity, or lack thereof, within a given population is frequently limited by the numerous host characteristics that represent a correlate of vaccine responsiveness. These factors can include, amongst others, race, sex, polymorphisms of the human leukocyte antigen or toll-like receptors (TLRs), and/or differences in the level of pathogen priming incurred by the host [1–3]. Collectively these factors can lead to adaptive vaccine responses that vary significantly across individuals, even if the vaccinees are healthy. Since vaccine adjuvants are used to selectively stimulate different routes of innate signaling, which then translate into enhanced adaptive immunity, the choice of adjuvant can affect a vaccine’s ability to consistently elicit robust immune responses across populations with different levels of responsiveness [4]. Adjuvant System AS01 contains the TLR-4 ligand MPL (3-O-desacyl-4′-monophosphoryl lipid A) and QS-21, a saponin extracted from the bark of the Quillaja saponaria Molina tree, and liposomes [5, 6]. Multiple studies evaluating licensed or candidate AS01-adjuvanted vaccines demonstrated the potential of these vaccines to provide critical public health benefits in populations with different exposure histories [7, 8]. These studies demonstrated a high (≥ 90%) efficacy in older adults who received the licensed AS01-adjuvanted recombinant herpes zoster vaccine, and partial efficacy in naïve infants and primed older children who received the licensed malaria vaccine RTS,S/AS01 [9–11]. Partial efficacy was also recently reported for the M72/AS01 candidate tuberculosis (TB) vaccine, when administered to adults in TB-endemic regions who had been infected with TB’s etiologic agent Mycobacterium tuberculosis (Mtb), as determined by interferon [IFN]-γ release assay [12, 13]. Furthermore, AS01-adjuvanted hepatitis B surface antigen (HBs) vaccines (HBs/AS01) were found able to elicit robust innate and adaptive immune responses in HBs-naïve adults [14–18]. Collectively, these studies conducted in populations with variable degrees of baseline immune priming indicated, first, that AS01-adjuvanted vaccines were immunogenic in these populations independent of the immune background, and second, that qualitative and quantitative differences in the immune responses may exist between populations with diverse levels of immune priming, including naïve individuals. There is thus a need to increase our understanding of the effect of pre-existing immunological memory on the variability in the immune responses to these vaccines, to inform future immunization strategies for AS01-adjuvanted vaccines in post-exposure settings. Systems vaccinology represents a valuable tool for such assessments, as it can provide deep insight into mechanisms controling vaccine immunogenicity, by quantifying the levels of engagement of specific pathways or gene signatures that are associated with adaptive immunity. Intriguingly, we previously detected several similarities in whole-blood (WB) or peripheral blood mononuclear cell (PBMC)-derived gene signatures elicited by either HBs/AS01 or RTS,S/AS01 in HBs- or malaria-naïve adults, respectively, or by M72/AS01 in Bacille Calmette-Guérin (BCG)-positive adults [17, 19, 20]. These signatures were characterized by positive regulation of IFN- and innate-cell-related genes, and negative regulation of natural killer (NK)-associated genes, and were detectable one day after the first or second vaccination. We therefore hypothesized that the gene signatures elicited by any AS01-adjuvanted vaccine were at least partially determined by the transcriptional responses to the immune stimulants contained in this Adjuvant System, and that transcriptional responses found for one AS01-adjuvanted vaccine might thus to some extent be generalizable to other similarly formulated vaccines. We evaluated the effects of pre-existing immunological memory on the magnitudes, functionalities, and inter-subject variability of the transcriptional responses to a clinically relevant AS01-adjuvanted vaccine. As a working model, we focused on responses to M72/AS01 observed in adults who had acquired diverse degrees of Mtb priming at baseline. A driver for this selection was the unique availability of PBMC samples previously collected in the context of a Phase II trial, which evaluated the safety and adaptive immunogenicity of M72/AS01 in adult populations with different histories of TB-disease exposure [21]. TB represents a relevant disease model for such studies due to its complex host response spectrum, which translates into diverse clinical statuses within infected populations. This spectrum spans from clinically asymptomatic and non-transmissible latent TB infection (LTBI) to active TB disease that is transmissible in active pulmonary TB [22]. The bulk of the transmission is mediated by adolescents and adults. Given that one quarter of the global population is Mtb-infected, with approximately 6 million new cases and 1.5 million deaths in 2020 [23], TB constitutes a vast medical need. Though several transcriptomic biomarkers of TB disease stages and/or risks have been found [24–28], any protective gene signatures or other correlates of protection have yet to be identified [29]. Still, recent data of the Phase IIb M72/AS01 trial demonstrated 54% efficacy in preventing active pulmonary TB in adults with LTBI [13], which is the first evidence of vaccine-mediated protection against the progression from LTBI to active disease. This is hopeful considering that the expected public health impact of a disease-preventing TB vaccine targeted at adults and adolescents is considered superior (assuming equal vaccine efficacy) to that of a new vaccine replacing neonatal BCG administration [30]. Currently, BCG is still the only licensed TB vaccine. The present exploratory post-hoc analysis utilized samples from M72/AS01 vaccinees only, derived from two of the three initial study cohorts of the Phase II trial [21]. A third, incompletely recruited cohort of subjects receiving treatment for active TB was not analyzed due to its small sample size (Fig 1). Analyzed subsets comprised M72/AS01 vaccinees who were either successfully treated for pulmonary TB disease, or TB-disease-naïve (i.e., expected to have had no encounters with symptomatic TB disease as inferred by a negative chest X-ray [21]). The TB-disease-naïve vaccinees were further stratified into two groups based on their purified protein derivative (PPD) tuberculin skin test (TST) results [21], lacking widely available, more sensitive and specific diagnostic methods to detect LTBI [31, 32]. The transcriptional responses to M72/AS01 were then compared across the resulting three groups of subjects. The TST detects Mtb sensitization via a delayed-type hypersensitivity response provoked by cell-mediated immunity (CMI) to Mtb antigens, including the Mtb39A and Mtb32A components of the M72 recombinant antigen. Thus, this design allowed us to investigate the effect of CMI-mediated immunological memory on AS01-induced gene expression patterns. Evaluations versus baseline were performed 7 or 30 days after the first vaccine dose, and 7 days after the second dose. The rationale for selecting these timepoints rather than the transcriptional response peaks—which for this and other AS01-adjuvanted vaccines typically occur 6–24 h after vaccination [14, 15, 17]—was based on observations made for BCG-primed, Mtb-infection-naïve and TB-disease-naïve M72/AS01 vaccinees [19]. These data showed that a combination of a timepoint before vaccination with a timepoint 7 days after each vaccination best captures the vaccine-induced gene expression, including both the tail-ends of the signals driven by the adjuvant AS01, and the signals reflecting the initiation of the adaptive immune response. This allowed the identification of potential clinically relevant transcriptional responses to the vaccine. For instance, analysis at D37 in that study enabled the detection of a key gene signature driven by AS01 [19]. Moreover, inter-subject variability in gene expression elicited by this vaccine was greater at ~1 week vs 1 day postvaccination [10], suggesting that the granularity of data informing on potentially protective signatures may be higher when derived from such later timepoints as compared to innate immunity peak timepoints. The presented results will inform downstream research and development of AS01-adjuvanted vaccines, including selection of immunization regimens, to benefit future vaccine studies in populations with different levels of baseline exposure.
Analyzed subsets of the primary study participants [21] comprised TB-treated (‘TB-TRT’) and TB-disease-naïve (‘TBDN’) M72/AS01 vaccinees who were seronegative for HIV-1 and HIV-2 antibodies (N = 24 and 40, respectively; Fig 1). Per-protocol immunization was at Day (D)0 and D30, and all but three of these participants received two vaccine doses. Baseline skin test (PPD) data showed that although most of the 80 TBDN placebo or vaccine recipients had indurations < 20 mm, the induration size range was wide (0 to 41 mm; Fig 2A). Therefore, using a ≥ 10 mm cut-off [31, 33], we assigned the TBDN M72/AS01 recipients to PPD-negative (TBDN-NEG) or PPD-positive (TBDN-POS) groups (N = 15 and 25, respectively; Fig 1). Demographic and pre-exposure-related group characteristics are summarized in Table 1. The groups were overall age- and gender-balanced. However, participants in the TB-TRT group were from White-Caucasian/European or Asian–East Asian heritages, while the other two groups were composed completely of participants from Asian–East Asian heritage. This imbalance across groups was addressed in the data analysis strategy by including ethnicity as a cofactor in the linear mixed models. All TBDN participants, and the vast majority of TB-TRT participants were BCG-primed. We assumed that lack of immunological memory (as defined by PPD induration < 10 mm) in TBDN-NEG participants was most likely due to lack of any contracted Mtb infections, though occurrence of prior, strictly innate immunity-controlled asymptomatic infections [34, 35] could not be excluded. Given that the TBDN-POS participants presented immunological memory and thus LTBI at screening, it was assumed that their adaptive immunity had been productive in clearing symptomatic Mtb infection [34]. While no PPD data were available for the TB-TRT cohort, presumably the immune response at the time of infection had been insufficient in controlling symptoms, but immunological memory had been developed. Since we previously detected associations between transcriptional and antibody responses in recipients of AS01-adjuvanted vaccines [14, 17, 20], we first investigated whether different levels of pre-existing immunity translated into differential anti-M72 antibody levels. Of note, these levels are used mainly as a metric for vaccine responsiveness, as the role of humoral immunity in protection against TB remains unclear [36]. All subjects were seronegative at D0, except for one TBDN subject who had a baseline antibody level that slightly exceeded the assay cutoff (3.1 vs 2.8 EU/mL), and was classified as TBDN-NEG based on the PPD skin test results. Each vaccination induced a significant increase in levels across the initial cohorts [21]. Though the original study did not present any statistical comparison between its cohorts, levels in that study tended to be higher in the TB-TRT cohort at 1 month post-dose 1 (D30), but similar between cohorts at 1 or 6 month(s) post-dose 2 (D60 and D210, respectively) [21]. Trends in the data for the present subsets were similar to those in the original study, with a tendency of higher levels in the TB-TRT subset vs the TBDN-NEG subset at D30, but similar levels between these groups at D60 and D210 (Fig 2B). We used a linear mixed model to investigate the overall effect of the TB status on the antibody concentration. To account for the longitudinal nature of the data, we introduced subject identifier as a random effect. We found a significant effect of time on antibody levels (p < 2−16), as expected, while there was no significant effect of group baseline. In contrast, a significant effect of the interaction between timepoint and group on antibody profiles was observed (p = 0.01), suggesting that the effect of TB exposure on antibody levels varies by timepoint. Nevertheless, at D210, all participants reached similar antibody levels. Overall, this suggested that the differences in baseline immunological memory across the groups—at least to the extent that could be derived from exposure histories and PPD data—did not translate in different antibody responses. This is consistent with previous observations for this vaccine [37, 38].
We next assessed the impact of the level of pre-exposure to Mtb antigens on the vaccination-induced gene expression. Transcriptional responses in PBMCs collected at D7, D30, and D37 (expressed in fold-changes [FC] over D0), were analyzed using linear modeling for microarrays (limma) as described [19]. Signals were modest (20 differentially expressed genes [DEG]; TB-TRT group) or absent (other groups) at D7, and negligible (≤ 7 DEG) across groups at D30 (Fig 3A). All groups displayed stronger responses at D37, with the highest levels for the TB-TRT group and then the TBDN-POS and TBDN-NEG groups (133, 72 and 35 DEG, respectively), following the expected trend in CMI memory response levels between the groups based on exposure histories and PPD data. Direct statistical testing of changes in gene expression over baseline across the different participant groups did not yield any DEG except for SPATA5 (Entrez Gene ID: 166378), the expression of which was significantly different between the TBDN-NEG and TB-TRT groups at D37. The encoded SPATA5 protein is a member of the AAA (ATPase associated with diverse activities) protein family, with no known direct or indirect effect on immunity. Results of a qualitative analysis of gene expression at D37 over baseline across different groups are presented in S1 Fig and S1 Table. Enrichment analysis using predefined blood transcription modules (BTMs [39]) did not reveal any significant over-representation of specific functions in qualitatively different genes (data not shown). However, 67% of the genes differentially expressed in all three groups were expressed in B cells (S2 Table). Further investigation using multilevel principal component (PC) analysis (PCA) of the by-subject values confirmed that transcriptional responses varied by timepoint, as evidenced by the separation of D37 data along the PC1 axis, but did not vary clearly by group (Fig 3B; left and right respectively). Subsequent gene-set enrichment analyses of the data in each of the first three PCs was performed as previously described [19] to identify significant associations (false discovery rate [FDR] q < 0.05) between transcriptional response patterns and predefined BTMs (described in [39]). The data revealed enrichments in PC1 and PC2, pertaining mostly to genes in the B-cell and plasma-cell related BTMs, while only minor or no enrichment was seen in the PC3 (Fig 3C). To further dissect the results, we performed hierarchical clustering of the timed group data, and observed that clustering patterns were generally based on timepoints rather than on groups (Fig 4), consistent with Fig 3B. No marked effect of PPD status was seen in the D7 profiles of the two TBDN subsets, which both clustered with the D30 profiles generated for all three groups. While post-dose-1 (D7, D30) and post-dose-2 (D37) profiles were typically clearly separated, the D7 profile of the TB-TRT group clustered with the joint D37 profiles of all three groups. This suggests that the disease-experienced individuals exhibited, already after the first vaccination, a transcriptional response which resembled the recall response observed in the disease-naïve individuals. The effect was mediated by differences in the levels of CMI memory responses displayed by these participant groups.
To facilitate interpretation of any functional differences in gene expression between timepoints and participant groups, we then performed gene-set enrichment analyses as described for Fig 3. Thirty BTMs were identified as being significantly enriched (Fig 5). They contained genes related to either general cellular mechanisms, cell-proliferation regulation, innate mechanisms, or plasma-cell and B-cell functions (4, 8, 13 or 5 BTMs, nos. 1–4 in Fig 5, respectively; see S3 Table for individual genes). Most of the innate-immunity-related BTMs (i.e., LI.M127, LI.M75, LI.M150 and LI.M68) were dominated by IFN-inducible genes. Of note, we here classified three BTMs annotated as regulating CD4+ T-cell cycling (LI.M46, LI.M4.5 and LI.M4.6) among the modules regulating cell proliferation. This was done because gene ontology analysis (http://geneontology.org), performed to identify enriched pathways of functionally-related gene groups, demonstrated that the genes in these BTMs were mainly involved in general cell-cycle processes, without clear attribution to a specific cell type. Aligned with the group’s accelerated and stronger transcriptional response (Fig 3), only the TB-TRT subset displayed enrichments at D7, comprising upregulated responses for nearly all (4/5) general cellular-mechanism-related BTMs and all cell-proliferation-related BTMs. The minor DEG responses at D30 (Fig 3A) did not enrich any BTM. As expected, most enrichments were seen at D37 (9, 18 and 25 BTMs in the TBDN-NEG, TBDN-POS and TB-TRT groups, respectively). At this timepoint, general cellular-mechanism-related modules were only enriched in the TBDN-POS and TB-TRT groups (5/5 BTMs upregulated). By contrast, innate immunity-related BTMs were only enriched in the TBDN-NEG group, and, at lower DEG frequencies (‘intensities’; see key in Fig 3), in the TB-TRT group (7/7 and 6/7 downregulated BTMs, respectively). Across the groups, nearly all (TBDN-NEG group; 4/5 BTMs) or all (other groups) B-cell or plasma cell-associated BTMs were enriched at D37, and all were upregulated. However, due to just-below-threshold values for many of the associated genes, intensities in the TBDN-NEG group were lower than in the other groups (≤ 20% vs ≤ 75%, respectively). Finally, the most prominent signals in terms of enriched BTMs were the uniformly upregulated cell-proliferation-related modules detected only for the TBDN-POS and TB-TRT groups (8/9 BTMs each), though most of these signals were of modest (≤25%) intensity. Overall, the nature and kinetic patterns were consistent with time-matched data previously generated for this and other AS01-adjuvanted vaccine(s) [19, 20].
We next investigated whether the B-cell signals at D37 were associated with the subsequent anti-M72 antibody concentrations. To that aim, we first summarized, for each group, the D37 expression values of genes belonging to the five identified B-cell modules, using PCA. Score plots of the PC1 and PC2 (corresponding to 50% and 22% of the explained variance, respectively) revealed no clear separation between groups along the PC1 axis, though all of the highest PC1 scores belonged to Mtb-exposed individuals (S2A Fig). When we plotted the PC1 scores against the antibody levels at D60 and D210 (S2B and S2C Fig, respectively), we observed a possible positive association between these parameters across each of the three groups, particularly for D210. This was confirmed by the finding that D37 PC1 scores had a statistically significant effect, as detected when we used a linear mixed model to assess the effects of time (p < 2×10−16) and the PC1 (p = 0.03686) on anti-M72 antibody levels. Timepoint by timepoint analysis showed a stronger association between PC1 and the antibody levels at D210 vs D60, without significant group effect (p time effect: 0.09939 [D60] and 0.01646 [D210]). To interrogate the data underlying the differential BTM enrichment levels between the groups (Fig 4) at the gene-level, we generated heatmaps of the expression values (log2 FC over D0) of the associated genes (S3 Fig). The plots revealed that the apparent inter-group differences were mainly attributable to variations in sample size and intensity of gene perturbation, rather than to distinct patterns. For example, expression values of the 23 genes underlying the upregulated cell proliferation-related modules, revealed that the upregulated D7 signals were not restricted to the TB-TRT group but were, at lower intensities, also detected in the other groups. Except for RIN2 and LAG3, all these genes were most prominently perturbed at D7 in the TB-TRT group, at values that were higher than the D37 values for the same group, or than the D7 or D37 values for both other groups. The nature of the transcriptomic responses was thus overall similar between groups, even though early proliferation-associated signals were more prominently displayed by TB-treated individuals. This prompted the hypothesis that these signals could cue the induction of adaptive immunity.
To test the hypothetical relationship between the early cell proliferation signals and subsequent humoral responses, we first summarized the by-subject expression values (FC D7/D0) of the 23 implicated genes by PCA. The lack of a distinct intergroup separation in the scores of the PC1 and PC2 (representing 59% and 11% of the explained variance, respectively; Fig 6A) suggested that the proliferation signals were relevant for each group, even though all highest PC1 scores were mostly exhibited by TB-TRT participants. This was confirmed by the distribution of PC1 scores, showing slightly higher medians for the latter individuals (Fig 6B), and by the higher PC1 scores for most (21/23) of the genes displayed by this group (Fig 6C). To test the association with the adaptive response, we then plotted anti-M72 antibody concentrations measured at D30, D60 or D210 against the individual PC1 scores by group and timepoint (Fig 6D). While most visible in the TB-TRT group, the regression line slopes suggested the presence of positive associations for all three groups. Linear modeling of the antibody data, as function of either the timepoint or the PC1 score, confirmed a significant effect of the timepoint (p < 2×10−16) and PC1 (p = 0.0040) on antibody levels. The statistical significance of the effect was confirmed by similar modelling performed for each antibody timepoint (p = 0.0171, p = 0.00017 and p = 0.001752, at D30, D60 and D120 respectively; without significant group effect). This result was aligned with the similar distribution of subjects between the groups, as observed when we stratified the individual antibody levels by PC1 score category (S4 Fig). Altogether, the proliferation signals at D7 were in all participant groups associated with the magnitudes of antibody responses detected either 1 month post-dose 1 or 7 days post-dose 2. Although this association was thus observed irrespective of the host’s level of baseline CMI memory responses, it was strongest in the individuals with presumably the most robust immunological memory.
Group average-based gene expression analyses can conceal differences in the level of interindividual diversity of immune responses between separate populations [17]. Given the largely overlapping gene expression patterns seen thus far for the three groups—and considering that a second dose of a different AS01-adjuvanted vaccine can moderate the interindividual response heterogeneity seen after the first dose in baseline-naïve subjects [17]—we next investigated whether the presence of high-level immune memory would give rise to more homogeneous within-group responses to the vaccine antigens. We first compared the response heterogeneity between post-dose 1 (D7) and post-dose 2 (D37) responses, using reverse cumulative distribution analysis of the gene expression by participant group and by post-vaccination timepoint (Fig 7A). Overall, we observed a high level of heterogeneity in the response at both timepoints. Yet, within all three groups, responses at D7 were more heterogeneous as compared to D37. These trends were further illustrated by heatmap analysis of the individual (by-subject) expression values (FC vs D0) for all DEG identified at these timepoints, while excluding the negligible D30 responses (S5 Fig). We then refined the quantification of within-group response heterogeneity, using Jaccard similarity index (JSI) data computed between all possible pairs of subjects of the respective group (Fig 7B). As JSI values are proportional to the fraction of DEG shared between two subjects (ranging from 0 [no overlap or homogeneity] to 1 [maximum overlap or homogeneity]), the obtained JSI distributions revealed that the homogeneity levels were proportional to the levels of pre-existing immunity (TB-TRT > TBDN-POS > TBDN-NEG) after the first dose, but comparable between the groups after the second dose. Similarly, the proportions of subjects with completely distinct gene expression profiles (i.e., JSI = 0) after the first and second vaccination could differ substantially between the groups (see Fig 7B). this trend was also supported by linear modelling (unbalanced ANOVA) of JSI values, demonstrating that not only the parameters ‘group’ and ‘timepoint’, but also their interaction (representing the group response patterns), significantly affected the homogeneity levels (p < 2×10−16, p = 1.3×10−8, and p < 2×10−16, respectively). Altogether, this indicates that low-level immunological memory at baseline promoted an increased response heterogeneity upon the first immunization, an effect that is negated by a second immunization, which then equalized the homogeneity levels across the strata of immunological memory of the vaccinees.
The paucity of data describing the effect of pre-existing immunological memory, or diversity therein, on functional transcriptional responses to adjuvanted vaccines, can limit optimal design of vaccine trials in different populations. In this post-hoc analysis of clinical trial samples, we compared the molecular signatures elicited by two immunizations with a relevant AS01-adjuvanted vaccine, between three groups of adults with variable levels of pre-exposure and baseline immune memory. Key observations deduced from these analyses comprised that (i) signatures were not distinctly different between the groups; (ii) the disease-treated individuals displayed a stronger proliferation-related signal upon the first dose as compared to the less-exposed individuals, and across the groups these early signals were associated with an increase in antibody responses after each immunization; (iii) having a history of TB resulted in more homogenous gene expression between individuals after the first dose, but this effect was leveled after a second dose, when the response homogeneity was similar between the three groups. Overall, the transcriptional responses to M72/AS01 vaccination observed here in antigen-experienced individuals are well aligned with previous reports describing the responses to this or other AS01-adjuvanted vaccines in naïve recipients [17, 19, 20]. In particular, the post-peak B-cell and proliferation-related signatures seen at 1 week post-dose 2 (D37) in each study group (though most prominently in both infected groups) overlapped with previous time-matched observations in naïve recipients of either this vaccine, RTS,S/AS01 or HBs/AS01. Such signatures were also detected 7 days after a single dose of influenza or yellow fever (YF-17D) vaccine, in primed or naïve individuals respectively, though for the latter (live-attenuated) vaccine this represented the peak response [40–42]. In addition, the currently observed D7/D37 proliferation signals were also detected in naïve RTS,S/AS01 recipients [20]. Although we have no data to support that these D7/D37 proliferation signals were driven by plasmablasts, activated plasmablasts were in the cited transcriptional studies reported at D7 [20] and/or D37 [17, 19, 20]. This suggests a potential link between this cell type and the proliferation signals reported here. However, differences with published data were also identified, such as the clearly downregulated innate and IFN signatures seen at D37 in the naïve group. Indeed, equivalent modules were previously found to be upregulated in time-matched signatures from naïve M72/AS01 or RTS,S/AS01 recipients [19, 20]. The disparity in M72/AS01 data might be due to the sample type (purified PBMC here, vs the aggregate of cell types in WB analyzed previously), while differences with RTS,S/AS01 data may be due to the adjuvant dose (AS01E for M72 vaccine, vs AS01B [containing twice the MPL and QS-21 quantities of AS01E] in the cited RTS,S study). The latter assumption on adjuvant dose is supported by recent WB-derived data comparing different HBs vaccines in naïve subjects, showing more numerous and mostly (25/32) downregulated innate- or IFN-related BTMs at D37 for a formulation with AS01B, and fewer and universally (13/13) upregulated BTMs with AS01E [17]. The current lack of NK-cell signatures was another notable difference with published data for other AS01-adjuvanted vaccines, which demonstrated NK enrichments persisting at D37 (following downregulated responses 1 day post-vaccination) [17, 19, 20]. Though the lack of a non-adjuvanted M72 vaccine group precluded full discrimination of strictly AS01-mediated effects, the gene expression patterns identified in the current study were consistent with those induced by other AS01-adjuvanted vaccines, indicating that a generic gene expression signature for AS01-adjuvanted vaccines may exist. Most notably, this gene expression signature identified the B-cell responses induced by AS01-adjuvanted vaccines [19]. The more robust signals in the disease-treated individuals, seen both in early (D7) proliferation-related modules and in subsequent overall gene expression, suggested that strong cell-mediated immunological memory, such as generated after an uncontrolled infection, stimulated and accelerated the transcriptional response to vaccination. Likewise, for other AS01-adjuvanted vaccines we found that gene expression for overlapping or equivalent proliferation modules in naïve individuals was undetectable or negligible 3–6 h (HBs) or 1 day (RTS,S) after the first dose, but could be detected at corresponding timepoints after the second dose, with typically higher intensities at subsequent timepoints after the second vs after the first dose [17, 20]. Interestingly, an association between molecular signatures—here, D7 proliferation-related or D37 B-cell-related signatures—and the magnitudes of subsequent antibody responses, was also observed with other vaccines. For example, such associations were seen in HBs/AS01, H5N1/AS03 and seasonal influenza vaccinees, involving mainly IFN-related signals, as well as in RTS,S/AS01 and YF-17D vaccinees, involving distinct B cell-related signals [17, 20, 40, 43, 44]. However, translation of the trend in gene expression intensity seen here (TB-TRT > TBDN-POS > TBDN-NEG), into the antibody response magnitudes was only ambiguous after the first dose (for D7 proliferation signals), and absent after the second dose (for D37 proliferation and/or B-cell signals). Apparently, infection-induced memory responses did not affect baseline or post-vaccination antibody levels, consistent with previous observations in M72/AS01 recipients. Indeed, similar antibody levels between infected and uninfected subjects were previously reported at pre-vaccination baseline for adolescent and adult vaccinees, as well as after vaccination, for adolescent vaccinees [37, 38]. Relative to the disease-treated individuals, both disease-naïve populations displayed lower-level gene expression after the first dose. The latter two groups likely also had weaker Mtb-specific CMI memory responses at baseline as compared to the disease-treated individuals, due to less pre-exposure (as supported by the TST results for TBDN-NEG participants). The lower-level gene expression in disease-naïve participants may therefore be aligned with data showing that memory CD4+ T cells can stimulate innate responses [17, 45]. In addition, the relatively high level of interindividual heterogeneity in the transcriptional responses after the first dose in the disease-naïve individuals may be explained by this weaker innate stimulation, which was apparently unable to fully moderate any pre-existing immunological diversity among these vaccinees. The latter was also observed after a single dose of either seasonal influenza vaccine or alum-adjuvanted HBs vaccine [3, 17, 42, 46]. These inter-group differences were abrogated after the second vaccination, which increased the response homogeneity in all groups. Similar changes in inter-individual variability in gene expression between successive doses have been reported for HBs/AS01 in naïve adults [17]. The more homogeneous response in the disease-treated individuals did not appear to translate into higher mean antibody responses. However, such translation might still be observed for the vaccine-induced T-cell response, which would be encouraging even in the absence of an immune correlate of protection. Indeed, as compared to TB-naïve subjects, disease-treated individuals can have a higher incidence of recurring TB disease, either due to endogenous reactivation of the initial infecting strain, reinfection with a different strain, and/or genetic background [47]. Finally, in the setting of this complex, dynamic disease with its continuum of manifestations, no definitive participant segregation based on strata of pre-exposure levels could be made. This is illustrated by the case of incipient TB disease, during which active disease can remain asymptomatic and without radiographic abnormalities, which could have caused erroneous assignment of participants to the TBDN cohort. In addition, spontaneous reversion from a PPD-positive to PPD-negative status, possibly provoked by trained innate immunity that successfully eradicated the Mtb infection [35, 48, 49], could have taken place at any time during the study. PPD reversion is often multifactorial (i.e., depending on BCG status, the time-span elapsed since infection, and/or exposure to nontuberculous mycobacteria cross-reacting with PPD antigens [50, 51]), therefore its occurrence is unpredictable. These uncertainties may be compounded by the relatively low specificity of the TST, at least as compared to IFN-γ release assays [31, 52], though contrasting data exist [32, 53]. In addition, the unexpected finding that the kinetics of the transcriptional response to M72/AS01 vaccination depended on the baseline Mtb exposure status, may have limited our comparison of response qualities between the groups at D7/D37. Nonetheless, while these factors may have complicated the interpretation of results, we argue that our main conclusions are maintained, particularly for the most distinct differences between the opposites in the current spectrum, i.e., TBDN-NEG and TB-TRT individuals. This study of transcriptional responses driven by AS01 also has considerable strengths, as it allows linking with the referenced published studies, and contributes to the ever-increasing datasets generated for this clinically relevant Adjuvant System. Interpretation of the study results was limited by the small sample sizes of the study groups. This precluded correction of the data for putative immune determinants other than the priming status (identified elsewhere [1–4]), and limited the statistical power of our study overall. Due to this, and because we could not establish the stability of the transcriptional features at D0 (for instance by having access to multiple pre-vaccination samples), we were not able to assess the effect of baseline transcriptional statuses on the vaccine responses. Additionally, there was substantial variation in ethnicity across groups, although this factor was taken into account and mitigated in the data analysis strategy. Furthermore, conclusions on functional differences (Fig 4; BTM regulation) between groups were drawn from numbers of perturbed genes within the BTMs, and that analysis did therefore not reflect any intergroup differences in expression intensity (i.e., difference in FC values). Collectively these unknowns prevented us from providing causative explanations of the identified trends in immune responses.
Pre-existing immunity at baseline accelerated and amplified the transcriptional response to a single dose of AS01-adjuvanted vaccine, resulting in gene expression levels that were more homogenous between individuals with similar infection and disease histories. However, irrespective of the level of baseline immune memory, the heterogeneity in transcriptional responses was reduced after a second vaccine dose, and attained comparable levels between groups of individuals with different degrees of pre-exposure to the pathogen. Although further research is needed, the data could be used to guide any future post-hoc analyses of bio-banked samples collected in an independent sub-study (NCT02097095) conducted in parallel with the Phase IIb efficacy trial of M72/AS01 [13]. Ultimately, the identification of mechanisms regulating responsiveness to vaccine regimens, or of a biosignature reflecting a protective response that can be detected shortly after vaccination, could facilitate future trials by reducing required sample sizes and follow-up times. In addition, this could help optimizing specific interventions (e.g., formulation, dosing, or booster regimen) for a given population. The clinical relevance of this study and its impact on the patient population are summarized in Fig 8.
Samples were sourced from the observer-blind, randomized, controlled Phase II trial (NCT01424501) which aimed to compare safety, reactogenicity and immunogenicity of M72/AS01 in populations with different exposure to TB at baseline [21]. The protocol was approved by all institutional Ethics Committees and conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. A summary of the protocol is available at http://www.gsk-clinicalstudyregister.com (GSK study ID 114886). Written informed consent for the primary analysis, as well as for the post-hoc analysis described here, was obtained from each participant before trial participation.
Participants were healthy male or non-pregnant female adults aged 18–59 years, who were seronegative for HIV-1 and HIV-2 antibodies and living in TB-endemic countries (Taiwan and Estonia) [21]. Cohort assignment and exclusion criteria were applied in the primary analysis as described [21]. Briefly, participants with previous history of successfully treated pulmonary TB disease at least one year prior to vaccination, and with no active pulmonary disease on chest X-ray were assigned to the TB-TRT cohort, and participants who had no active pulmonary disease on chest X-ray, no clinical signs or symptoms of TB disease, and no history of chemoprophylaxis or treatment for TB disease were assigned to the TBDN cohort (Fig 1). A third cohort, including participants who had completed the intensive phase of TB treatment, was excluded from the current analysis. TSTs were performed in the TBDN cohort at least 2 weeks before vaccination as described [21], and per applicable US Centers for Disease Control and Preventions recommendations [31]. TST positivity was defined as having a ≥ 10 mm induration. The current post-hoc analysis included all M72/AS01 vaccinees of the TBDN cohort stratified by PPD status into the TBDN-POS and TBDN-NEG groups, as well as all vaccinees of the TB-TRT cohort except for one subject. This individual reported a possibly vaccine-related serious adverse event (grade 3 hypersensitivity) and was thus excluded from the current analyses, as well as all participants of the groups of the original study cohorts receiving the physiological saline placebo (Fig 1). Participants in the vaccine arms received M72/AS01 by intramuscular injection at D0 and D30, and all were followed until six months post-dose 2 (D210). The candidate vaccine M72/AS01E (GSK, Rixensart, Belgium; referred to as M72/AS01 elsewhere in this manuscript) contains the M72 antigen (10 μg/dose) and the AS01E Adjuvant System. M72 is a recombinant fusion protein derived from the Mtb32A and Mtb39A proteins [54] which are both expressed in BCG and present in PPD [55, 56]. AS01E contains 25 μg MPL (produced by GSK), 25 μg QS-21 (Quillaja saponaria Molina, fraction 21; licensed by GSK from Antigenics LLC, a wholly owned subsidiary of Agenus Inc., a DE, USA corporation) and liposomes per dose. The current endpoint was the profiling of RNA expression by transcriptome microarrays, as described below, using PBMC samples obtained from these participants at D0, D7, D30 and D37. Anti-M72 IgG antibody concentrations were previously quantified by ELISA (cutoff ≥ 2.8 mIU/mL) at D0, D30, D60 and D210 [21], and associated descriptive statistics were performed using SAS (SAS Institute Inc., NC, USA).
Total mRNA was isolated from frozen PBMC samples using a standard Qiagen kit. RNA was amplified using the Ovation kit and protocol (NuGEN, CA, USA) and RNA expression levels were determined using the Human Genome-U133 Plus 2.0 arrays of 54120 probe-sets derived from gene transcripts (Affymetrix, OH, USA).
RNA was extracted from isolated PBMC using standard protocols, and RNA expression levels were determined using Affymetrix HG-U133 Plus 2.0 arrays. RNA quality control (QC) included quantification and quality analysis using the Agilent 2100 BioAnalyzer (BA). The RNA concentration was measured using the RiboGreen fluorescence assay. Raw microarray data were quality-controlled using standard methods, and normalized by GC Robust MultiArray Average (GCRMA [57]) as described [19, 58]. Microarray QC included analysis of the scale factor, the percentage present, the GAPDH (glyceraldehyde-3-phosphate dehydrogenase) 3’ to 5’ ratio, the relative log expression (RLE) and the normalized unscaled standard errors (NUSE). One microarray was excluded for failing QC-criteria. After normalization, probe-sets were filtered and retained based on the interquartile range (>0.75) of RNA expression data. In addition, probe-sets referring to the same gene were collapsed to the average gene expression, and probe-sets not mapping to any known gene were eliminated. A total of 2932 of the 54675 probe-sets present on the chip (5.36%) was retained for analysis, interrogating 1607 unique gene symbols. The dataset is accessible at GSE197408.
For this analysis, only the probe-sets with annotated gene names were included. For each sample, and for genes that were represented by more than one probe-set, the average gene expression for all probe-sets was used for the given gene. A linear mixed model (limma, R package [59]) was fitted to the RNA expression data. For each study group and each gene, moderated t-statistics were calculated for the gene’s expression at each post-vaccination timepoint (D7, D30 and D37) as compared to D0. Up- or downregulation were defined as values > 0 and < 0, respectively. The model included a random intercept for each subject and was blocked by gender. The t-statistics were used to calculate p-values and Benjamini-Hochberg FDR-adjusted p-values. Intergroup comparisons made at D7, D30 and D37 were embedded in the model. Genes were deemed differentially expressed if FDR < 0.05. Determination of enrichment of genes belonging to the BTMs [39] (FDR q < 0.05) in the DEG for each contrast was performed by hypergeometric test. The identification of the BTM was confirmed on condition that the RNA expression of the majority of genes within the BTM were significantly different from baseline (using the FDR p-values). Up- or downregulation of a BTM was determined by the relative prevalence of genes with RNA expression significantly higher or lower than baseline, respectively. BTMs were grouped based on prior knowledge and on the analysis of genes underlying their enrichment. The overall gene expression for any particular group of BTM was summarized by applying PCA to a matrix describing the gene expression of all genes underlying the BTM expression (irrespective of whether they were differentially expressed at the group level) for each subject, and then using the scores in PC1. Multilevel PCA was applied in order to take advantage of the repeated measurement structure of the data, and to highlight the treatment effect within the subjects separately from the biological variation between subjects. To this purpose, we first decomposed the within-subject variation in the dataset, then applied PCA on the within-subject variation matrix. Multilevel PCA analysis was performed using the mixOmics package in R.
To assess the relationship between antibody responses (y) and factors such as timepoint post vaccination, or gene expression, we used linear mixed models with random intercept for each subject (package lme4 v. 1.1–21 [60]). The Akaike information criterion was used for model selection. In all cases tested, including an interaction term did not increase the model performance, hence no interaction term was included. P-values for fixed effects were obtained using the ANOVA function from the R package lmerTest v.3.1–0 [61]. For the analysis of the relationship between antibody responses and gene expression, we did not include values below LOD. Assumption of linearity of the response, homoscedasticity and normality of the residuals were assessed visually, and met. Qualitative analysis refers to set analysis using Venn diagrams without direct hypothesis testing supported by statistical analysis. Transcriptional response heterogeneity within a participant group by post-vaccination timepoint was visualized by reverse cumulative distribution analysis performed in R, as well as evaluated in terms of Jaccard similarity by a method adapted from ref. [62]. Briefly, using FC > 2 as cut-off for up/down-regulation, we calculated for all possible pairs of individuals within a single group, the proportion of genes that were commonly up- or down-regulated. The JSI value was calculated as ratio between the genes commonly up- or down-regulated, and the genes up- or down-regulated in either of the two considered subjects (i.e., JSI value = ).
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PMC9648739 | Sarah Greaves,Katherine S. Marsay,Peter N. Monk,Henry Roehl,Lynda J. Partridge | Tetraspanin Cd9b plays a role in fertility in zebrafish | 10-11-2022 | In mice, CD9 expression on the egg is required for efficient sperm-egg fusion and no effects on ovulation or male fertility are observed in CD9 null animals. Here we show that cd9b knockout zebrafish also appear to have fertility defects. In contrast to mice, fewer eggs were laid by cd9b knockout zebrafish pairs and, of the eggs laid, a lower percentage were fertilised. These effects could not be linked to primordial germ cell numbers or migration as these were not altered in the cd9b mutants. The decrease in egg numbers could be rescued by exchanging either cd9b knockout partner, male or female, for a wildtype partner. However, the fertilisation defect was only rescued by crossing a cd9b knockout female with a wildtype male. To exclude effects of mating behaviour we analysed clutch size and fertilisation using in vitro fertilisation techniques. Number of eggs and fertilisation rates were significantly reduced in the cd9b mutants suggesting the fertility defects are not solely due to courtship behaviours. Our results indicate that CD9 plays a more complex role in fish fertility than in mammals, with effects in both males and females. | Tetraspanin Cd9b plays a role in fertility in zebrafish
In mice, CD9 expression on the egg is required for efficient sperm-egg fusion and no effects on ovulation or male fertility are observed in CD9 null animals. Here we show that cd9b knockout zebrafish also appear to have fertility defects. In contrast to mice, fewer eggs were laid by cd9b knockout zebrafish pairs and, of the eggs laid, a lower percentage were fertilised. These effects could not be linked to primordial germ cell numbers or migration as these were not altered in the cd9b mutants. The decrease in egg numbers could be rescued by exchanging either cd9b knockout partner, male or female, for a wildtype partner. However, the fertilisation defect was only rescued by crossing a cd9b knockout female with a wildtype male. To exclude effects of mating behaviour we analysed clutch size and fertilisation using in vitro fertilisation techniques. Number of eggs and fertilisation rates were significantly reduced in the cd9b mutants suggesting the fertility defects are not solely due to courtship behaviours. Our results indicate that CD9 plays a more complex role in fish fertility than in mammals, with effects in both males and females.
CD9 is a member of the tetraspanin superfamily of proteins that function as organisers of other membrane proteins [1]. CD9 is involved in a wide range of cell functions, including adhesion, motility, signalling and cell fusion [2]. Knockout (KO) of CD9 in female mice results in infertility due to a defect in sperm/oocyte fusion (reviewed in [3]). In a mechanism that appears conserved in mammals, CD9 is suggested to partner Juno, the egg receptor for sperm ligand Izumo1, thus facilitating the formation of adhesion sites prior to fusion [4]. CD9 concentrates at the interaction site on the oocyte in response to sperm oscillations immediately before fusion [5]. Whilst male CD9 KO mice appear fertile, CD9 is expressed on mouse sperm and male germline stem cells [6, 7] and is present at various stages of spermatogenesis, suggesting a role in this process [8]. Tetraspanins are widely expressed in teleosts [9] but there are no reports of roles in fish fertility. Several tetraspanins have roles during zebrafish development, including pigment cell interactions [10], hatching [11], vascularisation [12, 13] migrasome formation [14] and primordium migration [15]. However, the role of Cd9 in fertilisation has not yet been investigated. There are two paralogues of cd9 in zebrafish (cd9a and cd9b), which have 63% amino acid identity and similar mRNA expression patterns [15]. In this report, we investigate the role of Cd9 in zebrafish fertility. Two zebrafish cd9b alleles were used, and homozygous in-crosses of both alleles exhibited defects in fertilisation rates. The number of eggs produced per female (clutch size) was also significantly reduced. The defect in fertilisation was not further exacerbated by the additional KO of the paralogue, cd9a. Reduced clutch size could be rescued by crossing either cd9b KO male or female fish with a wildtype (WT) partner. In contrast, reduced fertilisation was only rescuable by crossing a KO female with a WT male. Our results indicate that Cd9 plays a more complex role in fish fertility than in mammals, with effects on both male and female fertility.
Adult wildtype AB zebrafish (WT) and cd9a/b/dKO mutants were housed and bred in a regulated 14:10 hour light: dark cycle under UK Home Office project licence 40/3459 in Bateson Centre aquaria at the University of Sheffield or project licence IACUC 140924 in the Singapore IMCB zebrafish facility. Zebrafish were raised under the standard conditions at 28°C [16].
cd9b mutants were created from WT embryos using transcription activator-like effector nucleases (TALEN) and maintained on an WT background. TALENs (ZGene Biotech Inc., Taiwan) were provided in a pZGB4L vector, targeting the cd9b sequence 5’ ttgctctttatcttca 3’. Two frameshift mutants, c.46del (cd9bis16 allele) or c.42_49del (cd9bpg15 allele) were selected that caused premature termination in the second transmembrane domain or just after the first transmembrane domain respectively (S1 Fig) (previously described by Marsay et al., 2021). cd9a mutants were created by Marsay et al., 2021 using CRISPR/Cas9. An indel mutation deleting 4bp and inserting 8bp (c.180_187delinsTCGCTATTGTAT; cd9ala61) generated a frameshift mutation resulting in a premature stop codon in exon 3, which was predicted to truncate the protein before the large extracellular domain. cd9 dKO mutants were created by injecting the cd9a gRNA and Cas9 RNA into cd9bpg15 embryos. These fish were screened for germline transmission by sequencing and backcrossed to cd9bpg15 mutants. Heterozygous fish of the same genotype were in-crossed and adult F2 fish were genotyped to identify homozygous cd9bpg15; cd9ala61 (cd9 dKO).
Adult zebrafish male/female pairs were placed in plastic breeding tanks overnight, separated by a divider. The wildtype and mutants were not siblings but fish of similar sizes were paired together when conducting pair mating experiments. All AB fish used were born within the same month and the cd9b mutants were born within 2 months of the AB fish used. The divider was removed the following morning after the lights came on and spontaneous spawning occurred. Embryos were collected every 20 min and collection time was recorded. Zebrafish pairs were allowed to spawn until no more embryos were produced and the number of embryos produced by each pair was recorded. Dead eggs (opaque eggs) were counted and removed on collection and fertilisation was assessed four hours post collection. Embryos that presented a well-developed blastodisc were counted as fertilised. It is known that egg laying is highly variable and so repeats were carried out over several weeks to ensure that the results were robust. Details of repeats are described in each figure legend. In addition, the experiments were conducted in the same environment, with the same protocol and the same equipment to try to minimise any environmental variation or influence.
vasa cDNA was provided by H. Knaut (NYU Medical Center and School of Medicine, USA) in a pBS+ cloning vector. The vector conferred ampicillin resistance and contained M13 primer binding sites flanking the vasa cDNA. vasa cDNA containing plasmid was transformed into NEB 10-beta competent E.coli, and purified using a Miniprep kit (Qiagen, UK). The DNA template for the vasa RNA probe was then produced using a standard PCR protocol with M13 primers (Forward: 5’gtaaaacgcggccagt3’, Reverse: 5’ggaaacagctatgaccatg 3’), and purified using a 50 kDal centrifugal filter unit (Amicon, UK). Anti-sense RNA probes were transcribed from the DNA template using digoxigenin (DIG)-11-UTP Labelling Mix (Roche, UK), cleaned using spin filters (Sigma-Aldrich) and eluted into RNA-later (Sigma-Aldrich, UK) before storing at -20°C.
Embryos were raised at 28°C in petri dishes containing E3 solution. The E3 was changed daily and any dead embryos removed. At 30–32 hours post fertilisation (hpf), embryos were anaesthetised using tricaine, dechorionated and then fixed using 4% (w/v) paraformaldehyde (PFA; Sigma-Aldrich, UK) in PBS. The fixed embryos were left overnight at 4°C in 4% PFA before being washed twice with PBS/0.05% (v/v) Tween 20 (PBST) the following morning. Embryos were then put through a MeOH/PBS series using 30%, 60% and 100% (v/v) MeOH before being stored in 100% MeOH (Sigma-Aldrich) at -20°C. In situ hybridisation was carried out as described (Thisse and Thisse, 2008), except for the embryo digestion with proteinase K, for which 30–32 hpf embryos were digested with 10 mg/ml proteinase K at 20°C for 22 min. The protocol was performed with embryos in 1.5 ml microfuge tubes for the first two days, after which they were placed in 12-well plates for staining before transferring back to microfuge tubes for storage. Stained embryos were stored in the dark in 80% (v/v) glycerol.
Quantitative reverse transcription-PCR (qRT-PCR) was conducted using the Sigma S5193 kit and run on a Stratagene qPCR machine using MXPro software. All reactions were set up with 1 μL of 7.5 μM primer, 2.8 μL 25 mM MgCl2, 0.2 μL ROX reference dye, 10 μL SYBR Green ready mix, (Jumpstart) and 5 μL 1:20 cDNA. All reactions followed the following thermal cycle; 3 minutes initial denaturation at 95°C, 40 cycles of 15 seconds at 95°C, 15 seconds at 57°C and 20 seconds at 72°C, then finally 1 minute at 95°C, 30 seconds at 55°C and 30 seconds at 95°C. Primers were first tested to ensure they did not produce primer dimers or other non-specific products by checking for a sharp peak in the melting curve (S2 Fig). Primers used were β-actin2 F 5’- ggacctgtatgccaacactg-3’, β-actin2 R 5’- tgatctccttctgcatcctg-3’, cd9b F 5’-gaacccgtgacatcgtgtaa-3’ and cd9b R 5’- tacaacaggacaaccactcg-3’. The fold expression was calculated by initially normalising the expression of cd9b to the control gene, β-actin-2, and then differences in fold expression between mutants and WT were calculated by normalising the mutant expression to the wild type.
PGC were stained using a vasa in situ hybridisation and then embryos were imaged in 80% glycerol using a microscope mounted camera and a 5x or 10x objective. The number of PGCs was counted across the whole embryo and PGC migration was analysed by measuring the distance between the most anterior and posterior PGCs, with the measurement following the body axis. Measurements were taken using Image J software.
Adult zebrafish were paired, as described above, 4 days before the IVF procedure and then transferred back to their normal tanks. The fish were then paired again in the afternoon before the IVF procedure. The following morning, fish of the same genotype were placed together in larger tanks as zebrafish will not normally lay when grouped. Individual fish were then anaesthetised using tricaine and dried before gamete extraction. Sperm was extracted from male fish using suction through 10 μl capillaries (Hirschmann Laborgeräte GmbH, Germany), whereas females were gently pressed on the abdomen to release eggs. Gametes from a single pair of individuals were combined and incubated for 30 sec before adding 750 μl aquarium water and incubating for a further 2 min. 9 ml of aquarium water was then added and the gametes incubated for 4 hr at 28°C. The numbers of fertilised and unfertilised eggs were then assessed. Dead eggs were immediately discarded after extraction from the females and therefore not included in the analysis.
Data distribution was first assessed for normality using a D’Agostino-Pearson omnibus K2 normality test on the experimental residuals, as well as creating a histogram of residuals. For normally distributed data, an ANOVA with Dunnet’s or Holms-Sidak multiple comparisons tests were used. For non-normally distributed data non-parametric tests, the Mann-Whitney U test or Kruskal-Wallis with Dunn’s multiple comparisons test, were used.
To test the involvement of Cd9b in zebrafish fertility, we used two alleles of Cd9b, cd9bis16 and cd9bpg15. Both alleles were selected as they caused frameshift mutations in the N-terminus and premature termination in or before the second transmembrane domain (S1 Fig). The homozygous mutant KO fish appeared to develop normally. However, when in-crossed, both the number of eggs per clutch (Fig 1A) and the fertilisation rate of the eggs produced were significantly reduced compared to WT (Fig 1B). While the number of eggs produced appear similarly reduced in both alleles (Fig 1A), the extent of reduction in fertility differed dramatically between the two alleles with cd9bis16 KO mutant pairs producing a markedly lower percentage of fertilised eggs (Fig 1B). The loss of fecundity in both alleles was surprising because the KO of CD9 in mice affects only the fertilisation of ova and not their production [17]. When the fate of the zebrafish eggs was analysed in more detail, the cd9bis16 KOs produced a significantly higher percentage of eggs that were dead at the time of embryo collection (Fig 1C). Dead eggs are opaque and are easily identified. However, this significant increase in the percentage of dead eggs was not replicated in the cd9bpg15 KO line, which produced a significant number of live but unfertilised eggs. (Embryos that presented a well-developed blastodisc after 3 hours were counted as fertilised). The difference in severity between the alleles could be due to the differences in mutations. While both mutations occur in similar codons (15 and 16), the consequent frameshift causes slightly more aberrant amino acids in the cd9bis16 KOs before a stop codon is created (46 in contrast to 22 in cd9bpg15 KO). While ISH and qPCR results showed a downregulation of mRNA suggesting nonsense mediated decay was occurring (S2, S3 Figs) [15], there could be differences in the residual function of the mutated protein. It is known, for example, that synthetic cell permeable peptides corresponding to the termini of CD9 show cellular activity [18]. There could also be differences in the amount of genetic compensation induced in either mutant [19]. Tetraspanins are known to have high levels of redundancy due to high structural relation, complementary roles and similar partner proteins. This increases the likelihood that there is some compensation happening in the cd9b KOs. The most likely candidate is cd9a as they share high protein identity and similar mRNA expression patterns to cd9b [15]. To determine if deletion of both paralogs would result in a complete loss of fertility as seen in the CD9 KO mouse, cd9a was knocked out in the cd9bpg15 background. ISH results show downregulation of cd9b and cd9a mRNA suggesting nonsense mediated decay was occurring [15]. Interestingly, the fertilisation rate of the double KO line was very similar to the cd9bpg15 KO line (Fig 1D), suggesting that only Cd9b is involved in egg production and fertilisation. We then investigated primordial germ cell (PGC) behaviour, to determine if reduced numbers or a delayed migration could result in lowered egg production [20]. However, both cd9b KO lines have the same number of PGC as WT fish (Fig 2A) and migration to the gonadal ridge during early development was not altered in cd9b mutants (Fig 2B). These results suggest that the number and migration of PGCs does not play a causative role in the reduction of fecundity and fertility seen with cd9b mutant pairs. These preliminary results do not, however, eliminate the possibility that mutations in cd9b could impact gonad development and morphology in later development or lead to impaired gametogenesis in mature gonads. Future experiments to elucidate possible roles for Cd9b in these processes could include histological analysis of the gonads at stages throughout development, analysis of gametogenesis and reproductive hormones such as follicular stimulating hormone and luteinizing hormone in sexually mature zebrafish, as well as investigating the expression of genes known to play a role in gonad development (e.g. ar, cyp11c1, cyp17a1, fancl, foxl2, hsf5, piwil1, piwil2) [21]. It would also be beneficial to undertake an analysis of cd9b expression to determine whether cd9b is expressed in the germs cells or the gonads. If cd9b is expressed in the germ cells, it would be interesting to study cd9b expression at different stages of oogenesis and spermatogenesis, given that CD9 has been shown to be expressed on murine oocytes and spermatogonial stem cells, as well as throughout the majority of spermatogenesis in mice [2, 22–24]. It is known that egg release and fertilisation in zebrafish are affected by mating behaviour, as observed previously (reviewed in [25]). To try to exclude this variable, we attempted to fertilise eggs manually using IVF techniques. In the experiment, fish were pair mated overnight by genotype, but the dividers were not removed so the fish were still exposed to the production and sensing of reproductive pheromones required for zebrafish breeding [26, 27]. Fish of the same genotype were then group housed the following morning and individual female fish removed for egg extraction. We found that numbers of eggs obtained from female cd9b mutants was significantly lower than WT and similar numbers of eggs were obtained from both mutant alleles (Fig 3A). To assess fertilisation rates, eggs and sperm from the same genotype were mixed externally. Fertilisation rates using sperm from cd9b KO males to fertilise KO eggs were also significantly reduced compared to using WT sperm to fertilise WT eggs (Fig 3B). The reduction in the percentage of fertilised eggs is again markedly different between the two alleles, which echoes the difference between the alleles seen in Fig 1B. Overall, this suggests the reductions in clutch size and fertilisation in cd9b KO mutants has a non-behavioural element. The reduction in the number of eggs extracted from cd9b mutant females during the IVF protocol might indicate that Cd9b has a role in ovulation, with reduced ovulation induced in cd9b mutant females. Female zebrafish are stimulated to ovulate overnight by steroid glucuronides that are produced by the Leydig cells in the testis of male zebrafish, and then released into the water [26–28]. The IVF protocol required fish of the same genotype to be pair mated overnight and so the decreased numbers of eggs extracted from cd9b mutant females could be due to an impact on steroid glucuronide production or release in the males, or sensing in the females. An alternative role for Cd9b in zebrafish fertility could be in gamete fusion. In mice, CD9 has been shown to be required for sperm-egg fusion and for the correct formation and distribution of microvilli on the oolemma [17, 29–32]. The role of CD9 in gamete fusion is suggested to be a result of this regulation of the microvilli [4, 31]. It would therefore be interesting to study the structure of microvilli and sperm-egg binding in the cd9b zebrafish mutants in future experiments. While these preliminary IVF experiments suggest a non-behavioural element in the reduced fecundity and fertilisation seen in cd9b KO mutants, the protocol does not eliminate any potential anatomical differences in cd9b mutant females that could impede egg laying or investigate possible reductions in sperm production, release, or motility. It would also be useful to investigate these potential mechanisms in future experiments. To investigate whether the phenotypes were due to a difference in the females, as seen in mice with fertilisation, or due to cumulative effects from both parents, we measured clutch size and fertilisation rates using a matrix of crossings. As found previously, mutant females crossed with mutant males had decreased clutch size and fertilisation rates, with the phenotypes seen in both the cd9bis16 in-crosses and the cd9bpg15 in-crosses (Fig 4A and 4B). Crossing cd9b mutants of either gender with WT fish produced normal clutch sizes (Fig 4A), showing that this phenotype can be rescued by both male and female WT fish. This data shows that cd9b mutant females have the ability to ovulate and lay normal numbers of eggs, which suggests that the decrease in clutch size seen with cd9b mutant in-crosses is not due to potential anatomical differences in the cd9b mutant female that could impeding egg laying. Indeed, given that clutch size can be rescued by replacing a cd9b mutant of either gender with a WT, this data suggests that both genders have a role in this phenotype. The requirement of reproductive pheromones for successful zebrafish breeding could be a possible explanation for the role of both genders in the decrease in clutch size seen from cd9b mutant pairs [26, 27]. As mentioned above, female zebrafish ovulate in response to steroid glucuronides released into the water by male zebrafish. Similarly, female zebrafish produce and secrete steroid glucuronides, such as oestradiol-17β-glucuronide and testosterone-glucuronide, which then attract and initiate courtship behaviour in the male zebrafish to facilitate egg laying [26–33]. It would therefore be interesting to investigate the production, release and sensing of steroid glucuronides by cd9b mutants and analyse whether the mutants display any differences in courtship behaviour (e.g. chasing, contact using the nose or tail, approaching, encircling and presenting etc) [34]. In contrast to clutch size, the defect in the percentage of eggs fertilised was only rescued when the cd9b mutant male was substituted for a WT male (Fig 4B and 4C). This suggests that the reduction in fertilisation seen in cd9b mutant pairs is due solely to a difference in the mutant male, which is the opposite to that seen in CD9b KO mice, where CD9 is required for female fertility [17, 29, 30]. Given that the reduction in fertility in cd9b mutants appears to be due to a defect in the mutant male, it would be interesting to investigate whether Cd9b plays a role in sperm release, sperm motility or sperm—egg binding. A reduced quality or quantity of sperm could also result in the reduced fertility seen and so future work could include investigating if the cd9b mutation has an impact on spermatogenesis. This could include looking at the steroid hormones that control spermatogenesis, histological examinations of spermatogenesis in the testis and conducting sperm counts. Furthermore, CD9 is expressed throughout the majority of murine spermatogenesis and it would be interesting to investigate whether this expression is replicated in zebrafish [6, 23, 24]. Although there is no statistically significant difference in the number of dead eggs observed between the two alleles, an increased trend can be seen for cd9bis16(Fig 4C). It would be beneficial to undertake future work to study the egg fate in cd9bis16 fish in more detail. This would include determining if the phenotype can be rescued and, given that eggs were collected every 20 minutes and dead eggs counted and removed on collection, it would also be interesting to determine if the eggs are laid dead or die shortly after laying. In conclusion, as with CD9 KO mice, cd9b homozygous mutant zebrafish showed fertility defects. It was found that cd9b KO zebrafish pairs laid decreased numbers of eggs and cd9b KO males had severely reduced fertility. In mice and human, CD9 appears to facilitate sperm penetration of the oolemma rather than the initial binding to the plasma membrane [35, 36]. CD9 KO female mice display a severe reduction in fertility due to defective sperm-egg fusion, but show no ovulation defects [17, 29, 30]. It was therefore surprising that the cd9b zebrafish mutants laid significantly fewer eggs and that the fertility phenotype appeared to be due to a defect in the cd9b mutant male, unlike CD9 KO mice. Unlike the mammal, it appears that CD9 plays a more complex role in fertility in zebrafish involving both sperm and oocyte. CD9 has, however, been reported to be expressed in male mice throughout spermatogenesis and in mature sperm during fertilisation [6, 23]. It would be interesting to investigate whether cd9b is similarly expressed in zebrafish males and to undertake further studies to elucidate the underlying mechanism behind the fertility phenotype. Perhaps infertility studies would benefit from CD9 investigation, an understudied membrane protein in regards to human fertility, in men in particular.
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PMC9648745 | I’ah Donovan-Banfield,Rachel Milligan,Sophie Hall,Tianyi Gao,Eleanor Murphy,Jack Li,Ghada T. Shawli,Julian Hiscox,Xiaodong Zhuang,Jane A. McKeating,Rachel Fearns,David A. Matthews | Direct RNA sequencing of respiratory syncytial virus infected human cells generates a detailed overview of RSV polycistronic mRNA and transcript abundance | 10-11-2022 | To characterize species of viral mRNA transcripts generated during respiratory syncytial virus (RSV) infection, human fibroblast-like MRC-5 lung cells were infected with subgroup A RSV for 6, 16 and 24 hours. In addition, we characterised the viral transcriptome in infected Calu-3 lung epithelial cells at 48 hours post infection. Total RNA was harvested and polyadenylated mRNA was enriched and sequenced by direct RNA sequencing using an Oxford nanopore device. This platform yielded over 450,000 direct mRNA transcript reads which were mapped to the viral genome and analysed to determine the relative mRNA levels of viral genes using our in-house ORF-centric pipeline. We examined the frequency of polycistronic readthrough mRNAs were generated and assessed the length of the polyadenylated tails for each group of transcripts. We show a general but non-linear decline in gene transcript abundance across the viral genome, as predicted by the model of RSV gene transcription. However, the decline in transcript abundance is not uniform. The polyadenylate tails generated by the viral polymerase are similar in length to those generated by the host polyadenylation machinery and broadly declined in length for most transcripts as the infection progressed. Finally, we observed that the steady state abundance of transcripts with very short polyadenylate tails less than 20 nucleotides is less for N, SH and G transcripts in both cell lines compared to NS1, NS2, P, M, F and M2 which may reflect differences in mRNA stability and/or translation rates within and between the cell lines. | Direct RNA sequencing of respiratory syncytial virus infected human cells generates a detailed overview of RSV polycistronic mRNA and transcript abundance
To characterize species of viral mRNA transcripts generated during respiratory syncytial virus (RSV) infection, human fibroblast-like MRC-5 lung cells were infected with subgroup A RSV for 6, 16 and 24 hours. In addition, we characterised the viral transcriptome in infected Calu-3 lung epithelial cells at 48 hours post infection. Total RNA was harvested and polyadenylated mRNA was enriched and sequenced by direct RNA sequencing using an Oxford nanopore device. This platform yielded over 450,000 direct mRNA transcript reads which were mapped to the viral genome and analysed to determine the relative mRNA levels of viral genes using our in-house ORF-centric pipeline. We examined the frequency of polycistronic readthrough mRNAs were generated and assessed the length of the polyadenylated tails for each group of transcripts. We show a general but non-linear decline in gene transcript abundance across the viral genome, as predicted by the model of RSV gene transcription. However, the decline in transcript abundance is not uniform. The polyadenylate tails generated by the viral polymerase are similar in length to those generated by the host polyadenylation machinery and broadly declined in length for most transcripts as the infection progressed. Finally, we observed that the steady state abundance of transcripts with very short polyadenylate tails less than 20 nucleotides is less for N, SH and G transcripts in both cell lines compared to NS1, NS2, P, M, F and M2 which may reflect differences in mRNA stability and/or translation rates within and between the cell lines.
Respiratory syncytial virus (RSV) causes a respiratory infection that leads to significant levels of morbidities and mortalities in infants and young children across the globe [1, 2]. Recovery from infection does not lead to long term protection and repeat reinfections over an individual’s lifetime are a hallmark of RSV [3, 4]. Thus, hospital admission of elderly patients with life-threatening complications of RSV infection are also common [5, 6]. Typically, RSV represents a global viral respiratory disease burden comparable to that of influenza, highlighting the importance of understanding this virus’ lifecycle [7–9]. The RSV genome is single stranded, negative sense RNA approximately 15,000 nt in length. The genome codes for 10 major capped and polyadenylated mRNAs in the order NS1, NS2, N, P, M, SH, G, F, M2 and L (Fig 1A) [10–12]. The 10 genes code for corresponding proteins except M2 which codes for both M2-1 (the 5′ most open reading frame) and M2-2 which is expressed by an unusual ribosome shunting mechanism and does not have a separate transcript [13]. In virus particles and infected cells, the viral genome exists as a nucleocapsid structure in which the RNA is coated by the nucleocapsid protein (N), and associated with a viral RNA dependent RNA polymerase known as L, a phosphoprotein (P) and viral protein M2-1 [14, 15]. Transcription of the RSV genome depends on the polymerase complex recognizing and responding to cis-acting elements within the viral genome. Each of the genes is flanked with conserved elements referred to as gene start (GS) and gene end (GE) signals [12]. Most of the genes are separated by short intergenic regions, although there is one overlapping gene junction, in which the GS signal of the downstream L gene lies 48 nt upstream of the GE signal of the preceding M2 gene [16]. The 3´ end of the genome contains a promoter referred to as the leader or Le promoter. Studies examining the effect of UV exposure on RSV gene expression showed that genes proximal to the 3´ end of the genome were more resistant to UV damage than genes located in the 5´ end of the genome [17]. This finding established that RSV genes are transcribed sequentially from the 3´ to the 5´ end of the genome. Subsequent minigenome studies confirmed that transcription of a downstream gene is dependent on transcription of an upstream gene, and that none of the intergenic regions contains a promoter [18–20]. Based on these and other studies, the prevailing model for RSV transcription is that the polymerase initiates each cycle of transcription at the Le promoter. Subsequent studies showed that the polymerase initiates transcription opposite position 3C of the Le promoter that generates and releases a short, heterogeneous RNA transcript of ~ 20–25 nt in length. The polymerase then scans to locate the GS signal of the first gene where it reinitiates RNA synthesis and caps the mRNA. The polymerase then elongates the mRNA until it reaches a GE signal where it polyadenylates the mRNA by a reiterative stuttering mechanism, and then releases it (Fig 1B) [21, 22]. Occasionally, the polymerase fails to recognize a GE signal, resulting in the synthesis of a polycistronic mRNA. The possibility also exists that the polymerase could fail to re-initiate RNA synthesis at a GS signal. It is thought that in this case, the polymerase dissociates from the viral genome ending that cycle of mRNA transcription. Because initiation of a new cycle of transcription depends on the polymerase associating with the Le promoter, a transcription gradient is established with genes that are proximal to the 3′ end of the genome molecule being transcribed more than those nearer the 5′ end. Evidence for a transcription gradient was originally obtained by quantifying mRNAs generated in an in vitro transcription system and from studies using a small molecule polymerase inhibitor that prevented initiation at the promoter [23, 24]. However, there have been several attempts to quantify the relative expression of RSV mRNAs which do not fully agree with these studies. Several reports used Illumina based RNA-seq analysis of intracellular viral RNA transcripts, which showed a deviation from the expected transcription gradient [25]. In another study, RT-qPCR analysis of mRNA levels in RSV infected cells revealed genotype-specific variations in mRNA levels, but with G having a higher mRNA level than N in each genotype tested, a finding that could not be accounted for by differential RNA stability [26]. Based on these findings, the authors proposed that the polymerase could scan through upstream genes in a non-transcribing mode and then initiate at a downstream GS signal, and/or that some gene junction signals facilitate polymerase recycling so that certain genes are transcribed reiteratively [26]. In this model, after reaching the GE signal for the G gene, for example, the polymerase might scan upstream to re-initiate mRNA transcription at the G gene GS signal, this would entail almost 1000 nt of upstream scanning. In support of this it has been shown that the polymerase can scan upstream from the M2 GE signal to locate the GS signal for the L gene [19] but this is a relatively small distance of less than 68 nt (in the case of RSV-A). However, these recent studies suffer to one degree or another from confounding factors. For example, reverse transcription and PCR amplification, which are necessary steps in Illumina based RNA-seq experiments, could be variable in efficiency depending on the mRNA transcript sequence and secondary structure. Likewise, RT-qPCR based quantification of different viral genes could be influenced by the selection of standards, used to determine copy number, that do not accurately mimic viral mRNAs. Direct RNA sequencing (dRNAseq) has been used to study transcriptomes from a range of sources including human and viral infections [27–32]. The technique sequences mRNA directly from the polyadenylated tail towards the 5′ cap by feeding the mRNA molecule through a nanopore and measuring changes in current across the pore as nucleotides pass through. Inherently dRNAseq can suffer from a 3′ bias as it is not possible to determine if the molecule sequenced is full length with an authentic cap or if it is degraded. In addition, there is a significant error rate where approximately 10% of nucleotides are wrong or missing. Nonetheless, the technique offers a direct measurement of mRNA abundance directly without the potentially confounding experimental steps (e.g. reverse transcription and PCR) and allows sequencing of the entire mRNA molecule that provides important imformation on its structure, whether it is polycistronic and poly A tail length [33]. We have previously used dRNAseq to examine the transcriptome of human adenoviruses, SARS-CoV-2 and adenovirus vector-based vaccines illustrating the significant advantages this approach offers in a range of different settings [27, 31, 34]. Here we apply the technique to examine the transcriptome of human RSV infected of MRC-5 cells sampled over time. We show that there is a gradient of transcription as proposed in early studies but this is not strictly linear. We also provide insights into the rate of production of viral polycistronic mRNA and show that the poly A tails of RSV mRNA are similar in length to those of human mRNA. To broaden the applicability of our findings we examined the steady state abundance of RSV transcripts in Calu-3 lung epithelial cells sampled at a single time point and show a similar non-linear decline in transcript abundance. These findings cement the utility of dRNAseq for analysing viral transcript frequencies.
Human MRC-5 cells (a genetically normal human lung fibroblast-like line) Calu-3 (a human lung adenocarcinoma cell line) and HEp2 (a derivative of HeLa cells) cells were obtained from European Collection of Authenticated Cell Cultures (#05072101, ECACC). The cells were cultured in DMEM supplemented with 10% foetal bovine serum, 100 U/ml penicillin and 100 μg/ml streptomycin. Stocks of RSV strain A2 were generated by low multiplicity of infection (0.01 TCID50/cell) of Hep2 cells and titrated by TCID50. To generate infected and control cell mRNA for this analysis, MRC-5 cells were infected at the University of Bristol with RSV strain A2 at a multiplicity of 3 TCID50/cell to ensure high rates of infection across the cell sheet. The infected cells were harvested at 6, 16 and 24 hours post infection (hpi) before extensive cell death is observed. Independently, a second stock of RSV, strain A2 was similarly generated and assayed at the University of Oxford site and subsequently used to infect Calu-3 cells for 48 hours at a multiplicity of 0.5 TCID50/cell again before extensive cell death occurs in this cell line.
RNA extraction and sequencing was as described previously [27, 31, 34]. Briefly, total RNA was extracted from the infected cell using TRIzol reagent (#15596026, Ambion) at 1ml of reagent per 107 cells, as per manufacturer’s recommendations except that the final wash of the RNA pellet in 70% ethanol was repeated a further two times (total of 3 washes). Total RNA was enriched for poly A tails using Dynabeads™ mRNA purification kit (#61006, Invitrogen) as per manufacturer’s instructions and we used 350ng of poly A enriched RNA per sequencing reaction. We used the SQK-RNA002 kits and MIN106D R9 version flow cells (Oxford Nanopore Technologies), following the manufacturer’s protocols.
Reads were mapped to a fasta file containing the RSV A2 genome (accession number KT992094.1) using minipmap2 [35] (command line example: minimap2 -a -x map-ont -uf -k14—sam-hit-only RSV_AB.fasta RSV2020_16hpi.fastq > map_2_RSV.sam).
To analyse nanopore transcripts that have a very high error rate we developed a software pipeline that assigns transcripts according to the ORFS they code [27]. Briefly, the pipeline takes the mapping information from the SAM file to group the transcripts mapping to the viral genome according to shared start locations, exons (or equivalent) and stop locations. Once grouped in this manner, a representative transcript is then generated for each transcript group based solely on the genome sequence. User supplied information on the location of known ORFs is employed to assign transcript groups according to what known ORFS would be coded by each transcript group. In this way the pipeline determines the percentage of mRNAs that express each ORF relative to the total number of transcripts that map to the target genome. This pipeline generates tables describing the structure of transcripts, what ORFs are present and how many mRNA molecules belong to each group of transcripts. Allied to this analysis, nanopolish [33] was used to determine the poly A length of each sequenced transcript and in house scripts grouped transcripts according to GE usage before compiling a list of poly A lengths for generating violin plots. For the ORF centric pipeline analysis, only transcripts that were QC-flagged as PASS by nanopolish and had an estimated poly A tail length of 20 or more were considered (approximately 60% of transcripts). For the analysis involving short poly A lengths, again only transcripts QC-flagged as PASS by nanopolish were considered. Visualisation of differences in poly A tail lengths of transcripts sharing the same GE signal was conducted in R 4.1.2 using RStudio, using ggplot2 (v3.3.5) and tidyverse (v1.3.1) packages.
Previously, and in line with other groups using dRNAseq, we had noted that the 5′ most 10 or so nucleotides of an mRNA molecule are lost during nanopore sequencing [27, 28]. To compensate for this in our analysis pipeline we use the genome sequence to programmatically add back the missing 10 nucleotides upstream and this approach works well for transcripts where the authentic initiating methionine is close to the 5′ cap. In the case of the G protein of RSV however this approach leads the software to utilise an out of frame AUG some 8 nt upstream of the authentic AUG. The software pipeline thus reports a large number of transcripts in which the apparent primary ORF is short and unknown but is immediately followed by a second ORF which is correctly identified as the G protein. For the purposes of this analysis, we have counted these as genuine G gene transcripts since manual inspection of the structure of these transcripts is consistent with the GS for G protein mRNA.
Raw fastq files (MRC-5 cells data), associated fasta files and poly A length output files are available from Zenodo, DOI: https://doi.org/10.5281/zenodo.5799655. The raw fastq files and poly A length data from Calu-3 cells can be found at Zenodo, DOI: https://doi.org/10.5281/zenodo.7101886.
The software for the ORF centric pipeline and for segregating the transcripts by poly A site usage and by poly A site length is available from the authors on request or from Zenodo, DOI: https://doi.org/10.5281/zenodo.7101768. Instructions for running this pipeline are included alongside the software at Zenodo but also available from the authors on request.
To examine RSV transcript abundance over time using dRNAseq, MRC-5 cells were infected with RSV strain A2 at an moi of 3 TCID50/ cell. Cells were then harvested at 6, 16 and 24 hpi to encompass the approximate length of time for a single cycle of virus replication. RNA was isolated and subjected to dRNAseq. Table 1 lists the number of sequence reads from each timepoint and the number mapping to either the RSV genome or the human transcriptome in each sample. The number of reads mapping to the RSV genome climbed dramatically over the course of infection, from just over 3,000 reads at 6 hpi to over 147,000 reads by 24 hpi. Broadly, the pattern of RSV reads shows a gradient of transcription, with genes that are proximal to the 3′ Le promoter being more highly represented (Fig 2). In addition, since the sequencing technology reads transcripts poly A tail first, there is greater read depth within each gene near the location of the poly A site. The relative levels of mRNA differed at 6 hpi compared to 16 and 24 hpi, with NS1 and NS2 transcripts being more highly represented at 6 hpi than at later times. This could be due to asynchronous viral entry, such that in some cells, only the first two genes had been transcribed by 6 hpi. In addition we cannot exclude the possibility that this early timepoint is measuring encapsidated mRNAs in the infecting innocula, rather than de novo synthesized transcripts in the infected cells. In contrast, the read patterns at 16 and 24 hpi are similar to each other. We used our previously described ORF centric analysis pipeline [27] to examine the number of mRNA molecules that code for a known gene, and Table 2 shows the relative contribution of mRNA that code for each RSV gene. This data is a subset of the total mapped reads shown in Fig 2 in that it only includes dRNAseq reads with a poly A tail of over 20 nt. This 20 nt cut-off is used to increase the confidence that the transcripts being counted are genuine mRNA molecules. Notably this table indicates that whilst NS1 and NS2 dominate at 6 hpi, only NS1 seems to dominate thereafter as levels of NS2 gene expression are then closer to N gene expression. Moreover, rather than a gene-by-gene step-wise decline in mRNA abundance the more distal the gene is from the 3′ Le promoter there is instead four groups of gene abundance at 16 and 24 hpi (Table 2 –see abundance relative to NS1 transcripts data). Group 1 containing just NS1, group 2 comprising NS2, N, P and M (around half the abundance of NS1) followed by group 3 with SH, G, F and M2-1/M2-2 (each around 20% of the abundance of NS1) with group 4 containing just L. This matches the visual pattern of depth of aligned transcripts shown in Fig 2 and are consistent with a model of obligatorily sequential transcription. As with our previous publications using dRNAseq [27], there is a large number of transcripts which could not be confidently associated with a particular gene (listed as “not identified”) that most likely represent truncated RNA molecules arising from mRNA degradation, mechanical shearing or molecules with short deletions within the body of the transcript. Allied to this observation, whilst we observe patterns of transcript mapping that aligns closely with the expected GS signals, it would be difficult using this approach to identify low level use of any novel GS signals as the technique is insensitive to the 5′ cap structure and thus unable to reliably identify full length transcripts.
Previous studies where RSV mRNAs were analysed by Northern blot analysis had revealed the presence of polycistronic species in addition to monocistronic mRNAs [11]. Polycistronic transcripts are generated when the polymerase fails to recognize a GE signal and continues transcribing into the adjacent downstream gene. The dRNAseq data allows a quantification of the abundance of different polycistronic mRNAs. To visually represent polycistronic messages we used an in-house script to group the alignments according to the mapped location of the 3′ end of individual reads (Fig 3)–the nature of dRNAseq means there is greater confidence that the 3′ end of a molecule has been correctly captured. From this data we can clearly see significant numbers of polycistronic messages, with three or even four genes on the same mRNA molecule in some cases, consistent with previously published Northern blot data. To provide an estimate of the relative rates that a GE signal is ignored to enable the production of a polycistronic message we divided the maximum read depth of the intergenic region by the maximum read depth of the preceding gene (Table 3). The proportion of readthrough at each intergenic region was consistent over time, except for higher levels of readthrough at the NS1-NS2 and G-F intergenic regions at 6 hpi compared to 16 hpi. We note the detection of three broad groups of GE signals, one is characterised by the NS1, M and F GE signals with a relatively high rate of readthrough (9–13%), the second is characterised by the NS2, N, P and G GE signals with a more moderate rate of readthrough (2–4%) with the SH gene GE signal being the most effective at signalling mRNA release (only 0.5% readthrough). Table 4 shows an analysis of polycistronic mRNA detected at 24 hpi in MRC-5 cells where we observe a significant number of polycistronic mRNA produced. This table is calculated from the list of characterised transcripts in S3 Table by counting the number of transcripts that contain one or more genes relative to the number of transcripts that code for just the first gene under consideration. For example, there are 10,324 transcripts containing the NS2 gene only, compared to 212 NS2-N transcripts.
The M2-L gene junction is unusual (see Fig 1A), with the GS signal for the L transcript lying upstream of the M2 transcript GE signal. Thus, to generate full-length L mRNA, the polymerase must ignore the M2 GE signal. We examined how frequently the polymerase that initiates at the L gene start terminates at the M2 GE versus the L GE and observed transcripts that start at nucleotide 8492 (which in this MRC-5 dataset is the start location for authentic L transcripts) but terminate at 8559 (the location of transcription termination for the M2 transcripts in this dataset). However, at both 16 and 24 hpi there was only a single transcript in each dataset with this structure and with a poly A tail greater than 20 nt in length. In contrast, there were 5 and 6 full length L transcripts at 16 and 24 hours respectively. Since we only consider full-length transcripts, this analysis will most likely under-represent the true number of L transcripts. For example, the read depth at 24 hpi near the 3’ end of the L gene suggests that there may be as many as 240 L transcripts at this time point instead of the 6 we report based on full length reads.
The dRNAseq approach captures data on the length of the poly A tail and Fig 4 shows violin plots of the poly A tail length for all the transcripts used to quantitate gene expression (i.e. poly A tail > 20 nt) whose 3’ ends map to the dominant GE signals for each gene. For viral mRNA the interquartile range of the poly A tail length is between 50 and 200 nt. In addition, we note that for all transcripts grouped by GE signal, between 16 and 24 hours the median poly A tail length declines (Fig 4; S1–S3 Tables). Transcripts with short poly A tails are associated with both inefficient translation and low stability [36]. We used an in-house script to identify RSV transcripts from each time point in MRC-5 cells which had short poly A tail lengths (< = 20 nt). When these transcripts were mapped to the RSV genome, we noted that for the most part the distribution of read depth was similar except for N, SH and G transcripts which were notably underrepresented at 16 and 24 hpi (Fig 5). To quantify this further we processed the mapped reads with a poly A tail less than 20 nt using our ORF centric pipeline. The data at 6 hpi is harder to interpret with confidence due to the low overall read depth. Table 5 provides a more quantitative analysis of the data at 24 hpi illustrating the dramatic fall in the relative contributions of N, SH and G transcripts with poly A tails less than or equal to 20 nt in length.
As we have shown in other viral systems there are a large number of variant transcripts reported by our pipeline (S1–S3 Tables). In most cases, these likely arise from mRNA molecules that are incomplete due to nucleases or mechanical shearing. In some cases, there are micro deletions, which could potentially be an artefact of the sequencing technology or of the mapping algorithms. For example, the most frequently reported minor deletion at 24 hpi is at nucleotides 849 to 862 and there is an A-rich region at this location. Nanopore sequencing is recognised to have difficulties in accurately reporting homopolymeric runs [37]. However, there are additional transcripts that appear to result from more substantive skipping events. For example, screening for mRNA transcript groups where at least 10 mRNA molecules were observed reveals over 134 different transcripts with small deletions between 9 and 40 nucleotides long (S3 Table, “Deletion events” spreadsheet). This totals some 4,000 individual polyadenylated mRNA molecules with deletions of at least 9 nucleotides. This includes, for example, a transcript with a 25 nt deletion within the NS2 gene between nucleotides 849 and 874 on the virus genome, leading to an out of frame truncation of the NS2 ORF. For this transcript there were 168 distinct mRNA molecules with an average poly A tail length of 95 nucleotides. This same 25 nt deletion was observed in 77 sequenced transcripts at 16 hpi (S2 Table) with an average poly A tail length of 107 nt. In addition, we also see cases where there are apparently well utilised poly A sites at significant distance from classical GE signals. For example, there are some 459 transcripts that apparently have a poly A tract that begins near or on nucleotide 3090, almost 200 nt away from the dominant poly A site for the P gene transcripts at nucleotide 3244. This group of aberrantly polyadenylated transcripts have the potential to encode full length P protein and have an average poly A tail length of 124 nt. There are also individual transcripts with unusual structures. For example, within the 24 hr post infection dataset there are 59 RSV transcripts with an insertion of over 100 nt (S3 Table). Examining the top 10 insertions reveals a mixture of RSV sequences, unknown sequences, and apparently one region of a human mRNA. In addition, we mapped the transcripts to concatenated RSV genomes to see if any transcripts mapped across two duplicate genomes. We identified just 89 transcripts that were each unique but did indeed apparently map across two genomes concatenated together in the same sense (S1 and S2 Files).
There is the formal possibility that our MRC-5 transcription data is cell type specific and so we sequenced RSV infected Calu-3 lung epithelial cells sampled at 48hpi. Reassuringly, the alignment of RSV reads shows a broad agreement with our MRC-5 data (Fig 6). The average poly A tail length for RSV mRNA transcripts was between 90 and 150nt long (S4 Table). However, analysing the reads with a short poly A tail shows a pattern of under representation that is similar to the MRC-5 derived data but not identical (Fig 5). What is consistent with the MRC-5 data is that N, SH and G transcripts with short (<20 nt) poly A tails are, again, underrepresented (Fig 6 and S4 Table). Next, we analysed the relative contribution of RSV genes to the viral transcriptome (Table 6). In Calu-3 cells there is notably more NS2 mRNA than in MRC-5 cells (comparing Tables 2 and 6) but as with the MRC-5 data we observe that genes can be grouped based on relative abundance. Group 1 comprises NS1 and NS2 (approximately 20%) followed by N, P and M in group 2 (approximately 9%), SH and G in group 3 (approximately 6%) followed by F and M2 (approximately 3–4%) in group 4 and L forming group 5. In the Calu-3 dataset we observed very few variant transcripts with internal deletions but we noted substantial occurrences of apparent aberrant polyA site usage (S5 Table) including, for example, some 306 transcripts with a poly adenylation site at nt 3068, some 160nt away from the dominant P gene poly A site in Calu-3 cells at nt 3228. Analysis of polycistronic mRNA production reveals a pattern of maximum rates of readthrough at the NS1-NS2 boundary, modest readthrough at M-SH and F-M2 boundaries and low readthrough at the other boundaries (S6 Table). This reflects the observations in MRC-5 cells (Table 4). Overall we note that the pattern and abundance of transcripts produced are similar across the three replicates (S5 Table). We did not observe any transcripts corresponding to the M2 GS–L GE transcript found at low abundance in MRC-5 cells (S5 Table).
Our dRNAseq dataset provides a detailed analysis of RSV mRNA expression at three timepoints in MRC-5 cells, a genetically normal lung cell line and subsequently at a single timepoint in triplicate in Calu-3 cells (a human lung adenocarcinoma line). By using dRNAseq we can quantify viral mRNA without the potentially confounding artefacts from, for example, reverse transcription or PCR amplification. In addition, because our analysis is limited to transcripts for which we have quantitated the poly A tail length we can also be confident that we are observing and quantitating only mRNA, and not genomic/antigenome RNA or immature mRNA. Examining the RSV transcriptome in two infected lung epithelial cells suggests that our observations will be applicable to other human cell types. Clearly, our platform can be translated to study other RSV strains and infected primary cells or clinical samples to provide an even broader picture of RSV transcription. Our data shows a stepped gradient of individual transcripts decreasing in abundance from the 3´ to the 5´ end of the genome. However, there is minimal evidence for an attenuation at some gene junctions, rather there are groups of genes with similar transcript levels. In MRC-5 cells at 16 and 24 hpi we see four groups (NS1, followed by NS2/N/P/M, followed by SH/G/F/M2 followed by L) whereas in Calu-3 cells there is a similar but not identical step down of abundance in 5 transcript groups (NS1/NS2 followed by N/P/M then SH/G then F/M2 and finally L). In contrast to previous recent reports using qRT-PCR [26] or our own prior Illumina RNAseq data [25] we do not see elevated levels of G protein transcript abundance in MRC-5 or Calu-3 cells. These discrepancies likely result from reverse transcription and PCR based steps that introduce unintended bias in the quantification of transcripts. Of note, our dRNAseq data is more aligned with early RSV data based on slot blots and UV inactivation studies [17, 23]. Indeed, a recent analysis of transcript abundance in related paramyxoviruses reported that for illumina based approaches at least, the techniques used to enrich for viral mRNA and sequence does affect abundance estimates. They went on to show that techniques which can distinguish the strand sense of the Illumina based sequence data had a significant impact on transcript abundance estimation [38]. This illustrates the potential for genome and antigenome RNA to confound attempts to determine mRNA abundance since they too will be enriched by RNA complementarity in any poly A based enrichment strategy. Nonetheless it is notable that for several gene pairs in both cell lines their abundance does not decline at the gene junction, and in some cases, such as N-P there is marginally more mRNA from the downstream gene. For the P and N transcripts this may be due to the N mRNA being longer (and therefore more prone to degradation) as dRNAseq is more likely to successfully read the full length of shorter mRNA molecules, and indeed the peaks for N and P sequence coverage are similar, indicating that this may well be the case. However, in the infected MRC-5 cells, there are more F then G transcripts since G transcripts are shorter than F transcripts this argument does not explain these results. It is possible that G mRNA is less stable than F mRNA in this cell line, accounting for the difference in steady-state abundance of these transcripts. This highlights the likely role of mRNA stability in determining the steady state levels of mature RSV mRNA species. It is also possible that there is some feature of G mRNA, such as secondary structure, that makes it more difficult to sequence such that it is underrepresented in the read depth analysis. There is also a formal possibility that on occasion the viral polymerase ignores a GS signal but can progress to the subsequent GS and then re-initiate transcription. Evidence that this was reported for a paramyxovirus, SV41, in which M transcripts are exclusively dicistronic M-F mRNAs, but monocistronic F transcripts are also produced, suggesting that some polymerase can scan from the P GE signal to the F GS signal, without transcribing M [39]. Our data shows for the first time the relative levels of polycistronic mRNA by RSV, showing for the first time the relative levels of different polycistronic messages produced by the viral polymerase. An early study where RSV transcripts were analysed by Northern blot revealed polycistronic transcripts containing two or three genes, but their expression levels were not determined [11]. In our data, in both cell lines we find that the SH-G intergenic appears to have the strongest GE signal and therefore the fewest readthrough events with the NS1 GE signal being the weakest. These findings are supported by previous work [11, 25, 40]. However, the significance of the strength of GE signals is unclear as previous work suggests that relatively high level of readthrough at the NS1 and NS2 GE signals does not play a role in viral replication, either in vitro or in vivo [41]. In addition to the canonical transcripts corresponding to the viral genes, we also examined the levels of the M2-L transcript which we only detected in infected MRC-5 cells. The RSV M2-L gene junction is unusual in that the L GS signal lies upstream (rather than downstream) of the M2 GE signal. It was previously shown that the RSV polymerase can access the L GS signal by scanning backwards from the M2 GE signal following M2 mRNA release [19]. However, the M2 GE signal has a similar termination efficiency as other RSV GE signals [42], and so if the polymerase simply responded to signals as it encounters them, it would be expected to release the L transcript at the M2 GE signal, primarily resulting in a 68 nt M2-L transcript (not including the poly A tail) and only producing full-length L mRNA as a readthrough transcript, similar to a polycistronic mRNA. However, our data shows that L mRNA is the major transcript produced from the L GS signal in both cell lines. If we limit our analysis to full-length transcripts, the L mRNA was produced at a 5-6-fold excess over the M2-L transcript in infected MRC-5 cells, and as noted in the results section, this is an under-representation of full-length L mRNA due to its large size. If we consider that the L mRNA 3′ end reads represent full-length L mRNAs then there was a 240-fold excess of L mRNA over M2-L transcripts at 24 hpi in MRC-5 cells. An explanation for why the polymerase usually generates a full-length L mRNA versus the M2-L transcript comes from work with vesicular stomatitis virus (VSV), which showed that the polymerase can only recognize a GE signal positioned beyond a certain distance from the GS signal [43]. This probably reflects the need for the polymerase to cap the mRNA before it can accurately recognize a GE signal [44]. We also examined M2-L transcripts with a limited (<20 nt) poly A tail length, as in another study it was shown that VSV polymerase that does not cap the RNA can recognize a GE signal but fails to properly polyadenylate the mRNA [45]. However, there was no evidence of such M2-L transcripts. Thus, we conclude that is probably insufficient distance before the M2 GE for it to be recognized by a polymerase that initiated at the L GS, allowing the polymerase to disregard this GE signal and synthesize full-length L mRNA, as suggested previously [43]. We did not detect the M2-L transcript in the infected Calu-3 cells which underlines the rarity of such transcripts. We observed a number of unusual transcripts in both infected MRC-5 and Calu-3 datasets, some with minor deletions that could reflect the limitations of nanopore sequencing technology. However, we observed transcripts with more substantial deletions in the infected MRC-5 dataset. In one example we detected the same 25 nt deletion in NS2 transcripts in our datasets at both 16 and 24 hpi. Given the number of distinct transcripts with the same deletion and their presence in independent samples suggests that these deletions are genuine and reasonably frequent. There is the possibility that defective interfering viruses were present in the input virus or they could be generated during the course of the infection in either cell line. However, it is worth noting that defective interfering viruses derived by internal deletions in the genome should generate transcripts reflecting such deletions. But we were only able to detect evidence of small deletions happening in our datasets that were low in numbers (S3 and S5 Tables). We cannot exclude the possibility that some rearrangements we detected could occur as an artefact of the sequencing technique, for example, at the DNA-RNA ligase step of sample preparation. However, we previously reported rare splicing events in the adenovirus transcriptome and went on to validate these transcripts (as few as 10 molecules in over 1.2 million reads) by directed RT-PCR [27]. Additionally, our analysis of the adenovirus transcriptome showed a broad range of mRNA species made amongst a small number of dominant transcripts. This concept was subsequently independently verified for a different serotype of adenovirus, again using dRNA-seq [46]. If the transcripts detected in the current study are genuine, it suggests the RSV polymerase has the potential to skip short distances when transcribing mRNA. Isolates of RSV have been found with small duplications in viral G gene, and defective interfering genomes are frequently generated, indicating that the viral polymerase can dissociate and reassociate with the template during genome replication [47–50]. It is not surprising to see a similar background of unusual mRNAs that do not match the classical transcripts in RSV infected cells. Potentially the higher abundance of transcripts with more substantial deletions in the infected MRC-5 cells compared to the Calu-3 may reflect the slightly higher MOI used for this infection. We also noted the poly A tail lengths of the viral transcripts are similar to human transcripts [33] in both cell lines. To our knowledge this is the first time poly A tail length has been reported for RSV. Notably, these poly A tails are added by the viral polymerase but without any clear mechanism to explain how the length is determined or why they are similar to host mRNA which is polyadenylated in the nucleus. In addition, between 16 and 24 hpi in MRC-5 cells there is a noticeable decline in poly A tail length across all the different mRNA classes. We have previously noted a decline in poly A tail length during adenovirus infections whose mRNA are made by host RNA pol II [27] and even earlier reports noted a similar effect during coronavirus replication [51]. Therefore, a decline in poly A tail length may simply reflect a decline in host cell function as infection progresses. Potentially of greater significance is the observation that transcripts with very short poly A tails are consistently underrepresented for the N, SH and G genes in both infected MRC-5 and Calu-3 cells (Fig 5, Table 5 and S4 Table). We retrospectively analysed our previously published SARS-CoV-2 transcript data [31] and failed to observe any significant differences in the distribution of SARS-CoV-2 transcripts with very short poly A tails. That three RSV genes in two independent MRC-5 datasets (16 hpi and 24 hpi) and all three Calu-3 datasets show the same pattern of underrepresentation is notable and may reflect differences in rates of translation or mRNA stability both of which can be governed by poly A tail length [36]. In principle, the underrepresentation of N, SH and G may reflect higher rates of translation for those transcripts and experiments to examine this possibility are underway. In conclusion, our dataset is the first dRNAseq transcriptomic analysis of RSV mRNAs in two independent lung epithelial cell lines. This dataset provides fine detail of the transcriptomic repertoire of RSV in cell culture. Our findings align closely with early studies that attempted to determine the relative abundance of RSV transcripts and provides a robust quantitative dataset. We believe this analysis highlights the potential for inaccurate measurements when trying to estimate RSV mRNA loads by indirect methods such as qRT-PCR or short-read based RNAseq techniques that is often the end point used to assess vaccine or antiviral drug activities, and that this technical insight is broadly relevant to the study of viral gene transcription.
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PMC9648750 | Yuyuan Li,Zhi Li,Wenying Sun,Meiling Wang,Ming Li | Characteristics of gut microbiota in patients with primary Sjögren’s syndrome in Northern China | 10-11-2022 | This study analyzes and compares the structure and diversity of gut microbiota in patients with primary Sjögren’s syndrome (pSS) in Northern China to healthy individuals to identify clinical features associated with dysbiosis. We included 60 Chinese pSS patients and 50 age- and gender-matched healthy controls. DNA was extracted from stool samples and subjected to 16S ribosomal RNA gene analysis (V3-V4) for intestinal dysbiosis. In addition, patients were examined for laboratory and serological pSS features. A Spearman’s correlation analysis was performed to assess correlations between individual bacteria taxa and clinical characteristics. The alpha-diversity (Chao1 and Shannon Index) and beta-diversity (unweighted UniFrac distances) of the gut microbiota differed significantly between pSS patients and healthy controls. Further analysis showed that several gut opportunistic pathogens (Bacteroides, Megamonas, and Veillonella) were significantly more abundant in pSS patients and positively correlated with their clinical indicators. In contrast, some probiotic genera (Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea) were significantly decreased in pSS patients and negatively correlated with their clinical indicators. Therefore, pSS patients in Northern China showed a dysbiotic intestinal microbiome enriched for potentially pathogenic genera that might be associated with autoimmune disease. | Characteristics of gut microbiota in patients with primary Sjögren’s syndrome in Northern China
This study analyzes and compares the structure and diversity of gut microbiota in patients with primary Sjögren’s syndrome (pSS) in Northern China to healthy individuals to identify clinical features associated with dysbiosis. We included 60 Chinese pSS patients and 50 age- and gender-matched healthy controls. DNA was extracted from stool samples and subjected to 16S ribosomal RNA gene analysis (V3-V4) for intestinal dysbiosis. In addition, patients were examined for laboratory and serological pSS features. A Spearman’s correlation analysis was performed to assess correlations between individual bacteria taxa and clinical characteristics. The alpha-diversity (Chao1 and Shannon Index) and beta-diversity (unweighted UniFrac distances) of the gut microbiota differed significantly between pSS patients and healthy controls. Further analysis showed that several gut opportunistic pathogens (Bacteroides, Megamonas, and Veillonella) were significantly more abundant in pSS patients and positively correlated with their clinical indicators. In contrast, some probiotic genera (Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea) were significantly decreased in pSS patients and negatively correlated with their clinical indicators. Therefore, pSS patients in Northern China showed a dysbiotic intestinal microbiome enriched for potentially pathogenic genera that might be associated with autoimmune disease.
Primary Sjögren’s syndrome (pSS) is a systemic autoimmune disease with a worldwide prevalence of 0.01%-0.09%, characterized by the infiltration of leukocytes into the exocrine glands, particularly the salivary and lachrymal glands [1, 2]. It is much more common in females than in males, particularly in middle-aged women [3]. The pSS prevalence rate in China is approximately 0.33%-0.77%, depending on the diagnostic criteria used, higher than in other countries [4–6]. Its pathogenesis and etiology are poorly understood. Evidence suggests that dysbiosis of the gut microbiome contributes to the pathogenesis of several autoimmune diseases, such as inflammatory bowel disease (IBD), systemic lupus erythematosus (SLE), and rheumatoid arthritis (RA) [7–9]. Dysbiosis of the gut microbiome has also been implicated in pSS. The pilot study by de Paiva et al. [10] showed that gut dysbiosis exacerbated experimental pSS in mice and correlated with disease severity. Similarly, intestinal dysbiosis has been reported in pSS patients, associated with clinical and laboratory markers of disease activity and signs of gastrointestinal inflammation [11]. However, they did not consider or report significant differences in microbiota. Wang et al. [12] reported that the intestinal microbiota of pSS patients differed significantly among high active disease, low active disease, and control groups, especially the Streptococcu genus. Recently, Yang et al. [13] showed that the pSS gut microbiota is characterized by increased pro-inflammatory and decreased anti-inflammatory microbes. However, the correlation between differences in microbiota and clinical indicators remain unknown. To explore the intestinal microbial balance of pSS patients in Northern China, we extracted total DNA from the fresh feces of 60 patients with RA and 50 healthy controls for 16S ribosomal RNA gene sequencing. We found differences in the gut microbiota. In addition, we correlated the gut microbiota of pSS patients with candidate biomarkers, including autoantibodies (antinuclear [ANA], anti-Ro52, anti-Ro/SSA, and anti-La/SSB) [14], rheumatoid factor (RF), C-reactive protein (CRP), immunoglobulins (Ig: IgG, IgA, IgM, and IgE), and complement components 3 (C3) and 4 (C4).
We recruited 60 pSS patients aged 36–84 years from Dalian Municipal Central Hospital (Dalian, China) between January 2021 and February 2022. All pSS patients met the 2002 American-European Consensus group classification criteria [15] and the American College of Rheumatology/European League Against Rheumatism classification criteria [16]. None had used local or systemic antibiotics or probiotics in the last three months. Patients with secondary Sjögren’s syndrome, other autoimmune diseases, or gastrointestinal tract disorders were excluded. In addition, 50 age- and sex-matched healthy control individuals that met none of the pSS diagnostic criteria were enrolled in this study. The exclusion criteria for the healthy controls were: diagnosis of gut disease and use of antibiotics or probiotics within the last 3 months prior. This study was approved by the Ethics Committees of Dalian Municipal Central Hospital (Dalian, China; YN2021-002-01). All participants provided written informed consent before participation in this study.
Serum samples were collected from all study participants and stored at -80°C until analysis. Fecal samples were obtained from 41 pSS patients and 44 healthy controls. Serum ANA was measured by the indirect immunofluorescence technique on a HEp-2 cell substrate. Serum anti-Ro52, anti-Ro/SSA, and anti-La/SSB antibody levels were determined using an enzyme-linked immunosorbent assay kit (Quintiles Laboratories North America; Marietta, GA, USA). An immunoturbidimetric assay was used to determine the serum levels of RF, CRP, IgG, IgA, IgM, IgE, C3, and C4 (Beckman Coulter, CA, USA). All analyses were performed at Dalian Municipal Central Hospital.
DNA was extracted from 200 mg (fresh weight) of each fecal sample using the QIAamp DNA stool Mini kit (Qiagen; Hilden, Germany) according to the manufacturer’s protocol. DNA concentration was measured and its purity was confirmed with a Nanodrop2000 (Thermo Fisher Scientific; Wilmington, CA, USA). The gut bacterial V3-V4 region was amplified using primers 341F (5’-CCTAYGGGRBGCASCAG-3’) and 806R (5’-GGACTACNNGGGTATCTAAT-3’). PCR reactions were performed with an initial denaturation step of 98°C for 2 min, 25 cycles of denaturation at 98°C for 15 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s, followed by a final extension step of 72°C for 5 min before being held at 4°C. PCR amplicon sequencing was performed using the Illumina HiSeq platform at Novogene Bioinformatics Technology Co., Ltd. (Beijing, China).
Sequence data analyses were mainly performed using QIIME (v.1.9.1) and R software packages (v.2.15.3). For operational taxonomic unit-based analysis, sequences were clustered using Uparse (v.7.0.1001) with a similarity cutoff of 97%. Community richness and diversity (alpha diversity) analysis were measured by Chao1 and Shannon index. Beta diversity was measured using principal coordinate analysis (PCoA) with unweighted UniFrac analysis in the R software. The linear discriminant analysis (LDA) effect size (LEfSe) analysis was used to identify differentially abundant taxa across two groups. Correlations among variables were assessed using Spearman’s rank correlation coefficient.
Continuous and normally distributed variables are presented as arithmetic means and standard error of the mean (SEM). The significance of data differences (P≤0.05) was assessed using a nonparametric t-test in GraphPad Prism 7 (Graph Pad Software; La Jolla, CA, USA). The statistical analysis of beta diversity used the nonparametric “Adonis” method in the “vegan” package of the QIIME-incorporated version of the R software. Correlation analyses used Spearman’s rank correlation tests. Statistical analyses were performed in SPSS v.9.0 (SPSS Inc.; Chicago, IL, USA).
The sequence data associated with this study are deposited in the NCBI Sequence Read Archive with the accession number PRJNA856785.
This study enrolled 110 participants. Table 1 lists the demographic, clinical, and laboratory characteristics of the pSS patients and healthy controls. The pSS patients (n = 60) had a mean age of 59.37±1.25 years, and 95% were female. The healthy controls (n = 50) had a mean age of 60.0±1.44 years, and 90% were female. Age and sex did not differ significantly between the pSS and control participants (P>0.05). In addition, marital status, smoking status, education, occupation, and diet (including vegetarianism) did not differ significantly between the pSS and control participants (P>0.05; S1 Table). ANA, anti-SSA, anti-SSB, and anti-Ro52 positivity in pSS patients were 95.0%, 68.3%, 26.7%, 78.3%, respectively. In addition, pSS patients had significantly higher RF (P = 0.0110), IgG (P<0.0001), IgA (P = 0.0006), and C3 (P = 0.0002) levels than controls.
A Venn diagram was used to visualize the composition of gut bacterial communities (Fig 1A). The numbers of genera in the pSS and control groups were 1,363 and 1,324, respectively. The total richness of genera in the two groups was 1,678. The number of genera shared by the two groups was 1,009, 60.13% of all observed genera. Next, we compared the alpha diversity in the pSS and control groups. The pSS group had higher community richness (Chao1: P = 0.0169; Fig 1B) and alpha diversity (Shannon index: P = 0.0448; Fig 1C) than the control group. Beta diversity based on an unweighted UniFrac PCoA showed the separate clustering of the pSS and control groups (Adonis test: P = 0.0010; Fig 1D), with principal components 1 and 2 accounting for 12.43% and 9.42% of the total variance, respectively.
Four phyla (Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria) accounted for >99% of the population in both of the pSS and control groups (Fig 2A). At the phylum level, we found a tendency for significantly more Proteobacteria in the pSS group (8.53%) than in the control group (5.82%; P>0.05). In addition, there were tendencies for significantly fewer Firmicutes (64.92% pSS vs. 66.90% control; P>0.05), Actinobacteria (5.89% pSS vs. 7.76% control; P>0.05), and Firmicutes/Bacteroidetes ratio (5.50 pSS vs. 7.09 control; P>0.05) in the pSS group than in the control group. We further studied the compositional differences in gut microbiota at the family (Fig 2B) and genus (Fig 2C) levels between groups. Nine major families (abundance ≥0.01) were identified, among which Prevotellaceae was significantly decreased and Bacteroidaceae was significantly increased, in the pSS group compared to the control group (Fig 3A). In addition, we found significant decreases in Coriobacteriaceae, Peptostreptococcaceae, Eggerthellaceae, and unidentified_Clostridiales in the pSS group compared to the control group (Fig 3A). At the genus level, 15 genera showed significant compositional changes between pSS and control groups. We found significantly increased abundances of Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter in the pSS group compared to the control group. In addition, we found significantly decreased abundances of Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella in the pSS group compared to the control group (Fig 3B).
The LEfSe method, identified 13 significantly different components in the intestinal microbiota (LDA score>4) between the pSS and control groups. The results showed a discriminative association of order Enterobacteriales, families Bacteroidaceae and Enterobacteriaceae, genera Bacteroides and unidentified_Enterobacteriaceae, and species Escherichia_coli with the pSS group compared to the control group. In contrast, phylum Actinobacteria, class Coriobacteriia, order Coriobacteriales, families Prevotellaceae and Coriobacteriaceae, genus Collinsella, and species Collinsella_aerofaciens were significantly associated with the control group compared to the pSS group (Fig 4).
We analyzed correlations between intestinal microbiota composition at the genus level with several clinical characteristics in pSS patients (Fig 5). The genera Bacteroides and unidentified_Prevotellaceae were positively correlated with being female. The abundances of unidentified_Enterobacteriaceae, Bacteroides, Lactobacillus, Megamonas, Streptococcus, Veillonella, unidentified_Muribaculaceae, and Barnesiella were significantly positively correlated with disease duration, positive autoantibody percentages, and RF, IgG, and IgA levels, which were all significantly higher in pSS patients than in healthy controls. Moreover, these genera were negatively correlated with C3 levels, which were significantly lower in pSS patients than in healthy controls. In contrast, the abundances of genera Klebsiella, Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, and Alistipes were significantly negatively correlated with disease duration, positive autoantibody percentages, and RF, IgG, and IgA levels. Moreover, these genera were positively correlated with C3 levels.
pSS is characterized by dryness of the mouth and eyes. Several previous studies have reported dysbiotic salivary microbiota in pSS patients [17–23]. Intestinal dysbiosis has recently been considered a possible environmental influence in pSS etiology [1–3, 24]. To our knowledge, pSS patient characteristics in Northern China have never been reported. Here, we used high throughput sequencing to assesse 50 healthy individuals and 60 pSS patients to explore the intestinal microbial balance in pSS patients in Northern China. Taxonomic analyses showed that pSS patients had lower gut microbiota diversity than healthy controls. In addition, we found microbiota differences between pSS patients and healthy controls at the family and genus levels. We first investigated the relationship between gut microbiota and candidate biomarkers, finding that gut dysbiosis was associated with clinical and laboratory pSS markers, including increased positive autoantibody percentages and RF, IgG, and IgA levels and decreased C3 levels. Intestinal dysbiosis is observed in autoimmune diseases, including IBD, SLE, and RA, and is associated with decreased bacterial diversity, increased pro-inflammatory bacteria, and decreased anti-inflammatory bacteria [25–27]. We found significant changes in alpha- and beta-diversities between pSS patients and healthy controls, suggesting that gut microbiota in the two groups differed significantly, consistent with a previous study [24]. At the phylum level, Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria were the dominant components of gut microbiota in pSS patients and healthy controls. The proportions of these four phyla did not differ significantly between groups. Nevertheless, their changing trends in pSS patients compared to healthy controls were similar to previous studies [17, 24]. At the family level, we found a significantly lower abundance of Prevotellaceae and higher abundance of Bacteroidaceae, contrary to prior data in pSS patients [24]. However, studies have shown that patients with multiple sclerosis may have a uniform decrease in Prevotellaceae, especially the genus Prevotella [28, 29]. Similarly, a study on Chinese RA patients showed them to have decreased Prevotella [30]. The Prevotellaceae family may be beneficial for enhancing the production of protective short chain fatty acids (SCFAs), such as butyrate and propionate, which are bacterial metabolites that expand gut regulatory T cells (Tregs) [28, 31]. In addition, Scher et al. reported that Prevotella abundance was negatively associated with Bacteroides [32]. The trend in Bacteroides abundance in our study was consistant with the findings of a previous study [24]. Importantly, we observed a significant positive correlation between autoantibody positivity in pSS patients and the relative abundance of Bacteroides in fecal samples. While Bacteroides species are commensal gut bacteria and are well-known for their increasing resistance to many antibiotics (33), some have been reported to be associated with the autoimmune diseases. Davis-Richardson et al. showed that Bacteroides dorei abundance correlated positively with future autoimmunity for type 1 diabetes [33]. In addition, Bacteroides fragilis has been identified as a potential gut pathobiont in autoimmune disease [34, 35]. The increase in Bacteroides in pSS patients observed in this study may enhance disease progression. The proliferation of families Coriobacteriaceae and Eggerthellaceae (phylum Actinobacteria) is reported to be triggered by polyphenols and fibers [36] and shows significant decreases in IBD patients compared to controls [26]. Furthermore, a decrease in the beneficial bacteria Peptostreptococcaceae (genus Romboutsia; phylum Firmicutes) was observed in irritable bowel syndrome patients [37]. At the genus level, we found a significant increase in the abundance of several gut opportunistic pathogens, Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter in pSS patients. The proportions of Bacteroides, Megamonas, and Veillonella showed significant positive correlations with clinical indicators in pSS patients. Megamonas and Veillonella were enriched in chronic hepatitis B patients [38], while Flavonifractor was abundant in patients with neuromyelitis optica spectrum disorders [39], and Intestinibacter was abundant in patients with Crohn’s disease [40]. In pSS patients, we also observed decreases in some probiotic genera: Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella. The proportions of Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea showed significant negative correlations with clinical indicators in pSS patients. Dorea, Fusicatenibacter, Lachnospira, and Tyzzerella are all Gram-positive bacteria and belong to the Lachnospiraceae family of Firmicutes. Zhou et al. [41] reported that the abundance of Collinsella, Romboutsia, Dorea, and Fusicatenibacter were significantly lower in allergic rhinitis patients than in healthy controls. In addition, the abundance of the Dorea genus was positively correlated with SCFA concentrations [41]. Among the above differential microbiota, Veillonella had the highest correlation with the positive rate of autoantibodies. In a recent systematic review, Veillonella was found to be significantly increased in three systemic autoimmune diseases, namely SLE, RA, and SS [42]. Therefore, Veillonella may be a factor related to intestinal dysbiosis in pSS patients in Northern China. However, more studies or different approaches are needed in the near future to find the clear connection between Veillonella and pSS patients. This study had several limitations. The results were not replicated in an independent cohort, did not distinguish between men and women due to the sex imbalance in our cohort, and were obtained with subjects from a single hospital. In addition, the results were compared to healthy controls only and not patients with symptoms of dryness without a pSS diagnosis. Nevertheless, this study confirmed intestinal dysbiosis in Northern Chinese pSS patients, which deserves further investigations.
In summary, we confirmed that intestinal microbial diversity and composition in pSS patients differed from healthy controls in Northern China. Increased Bacteroides, Megamonas, Veillonella, Flavonifractor, and Intestinibacter, combined with decreases in Collinsella, unidentified_Ruminococcaceae, Romboutsia, Dorea, Fusicatenibacter, Lachnospira, Adlercreutzia, unidentified_Clostridiales, Butyricicoccus and Tyzzerella appear to be pSS characteristics in Northern Chinese patients. Spearman’s correlation analyses showed that Bacteroides, Megamonas, and Veillonella were positively correlated with clinical indicators in pSS patients, while Collinsella, unidentified_Ruminococcaceae, Romboutsia, and Dorea were negatively correlated. Among these differential microbiota, Veillonella may be a factor related to gut dysbiosis in pSS patients in Northern China. This study provides a theoretical basis for exploring novel diagnostic, prognostic, and treatment modalities in the new era of preventive, predictive and personalized medicine.
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PMC9648751 | Clotilde El Guerche-Séblain,Adrien Etcheto,Frédéric Parmentier,Mohammad Afshar,Alejandro E. Macias,Esteban Puentes,Viviane Gresset-Bourgeois,Meral Akcay,Audrey Petitjean,Laurent Coudeville | Hospital admissions with influenza and impact of age and comorbidities on severe clinical outcomes in Brazil and Mexico | 10-11-2022 | Background The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil. Methods We used national hospital discharge data from Brazil (SIH/SUS) from 2010–2018 and Mexico (SAEH) from 2010–2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0–4, 5–17, 18–49, 50–64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age. Results A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0–5.0), ICU admission rate was 3.3% (95% CI, 3.2–3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5–4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0–7.0), ICU admission rate was 1.8% (95% CI, 1.6–2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5–7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0–3.5) and in-hospital CFR by 13.9 (95% CI, 8.4–22.9) in children 0–4 years. Conclusion The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions. | Hospital admissions with influenza and impact of age and comorbidities on severe clinical outcomes in Brazil and Mexico
The risk of hospitalization or death after influenza infection is higher at the extremes of age and in individuals with comorbidities. We estimated the number of hospitalizations with influenza and characterized the cumulative risk of comorbidities and age on severe outcomes in Mexico and Brazil.
We used national hospital discharge data from Brazil (SIH/SUS) from 2010–2018 and Mexico (SAEH) from 2010–2017 to estimate the number of influenza admissions using ICD-10 discharge codes, stratified by age (0–4, 5–17, 18–49, 50–64, and ≥65 years). Duration of hospital stay, admission to the intensive care unit (ICU), and in-hospital case fatality rates (CFRs) defined the severe outcomes. Rates were compared between patients with or without pre-specified comorbidities and by age.
A total of 327,572 admissions with influenza were recorded in Brazil and 20,613 in Mexico, with peaks period most years. In Brazil, the median hospital stay duration was 3.0 days (interquartile range, 2.0–5.0), ICU admission rate was 3.3% (95% CI, 3.2–3.3%), and in-hospital CFR was 4.6% (95% CI, 4.5–4.7). In Mexico, the median duration of stay was 5.0 days (interquartile range, 3.0–7.0), ICU admission rate was 1.8% (95% CI, 1.6–2.0%), and in-hospital CFR was 6.9% (95% CI, 6.5–7.2). In Brazil, ICU admission and in-hospital CFR were higher in adults aged ≥50 years and increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission by 1.9 (95% CI, 1.0–3.5) and in-hospital CFR by 13.9 (95% CI, 8.4–22.9) in children 0–4 years.
The SIH/SUS and SAEH databases can be used to estimate hospital admissions with influenza, and the disease severity. Age and comorbidities, especially cardiovascular disease, are cumulatively associated with more severe outcomes, with differences between countries. This association should be further analyzed in prospective surveillance studies designed to support influenza vaccination strategy decisions.
Each year, seasonal influenza is associated with up to 3–5 million cases of severe illness and approximately 290–650 thousand deaths worldwide [1]. Although all persons are at risk of infection, the risk of severe influenza that leads to hospitalization or death is higher for children <5 years of age, older adults, pregnant women, and individuals with underlying conditions such as immunodeficiencies, asthma, and chronic heart or lung diseases [1–3]. This is especially true for low- and middle-income countries [3]. However, evaluating the true burden of severe influenza in these countries is challenging due to non-systematic laboratory testing of patients hospitalized with acute respiratory infections, limited access to influenza diagnostic tests, and/or lack of data on specific risk factors. The World Health Organization (WHO) recommends seasonal flu vaccination for those who are most at-risk for morbidity and mortality, particularly the elderly and those with underlying health conditions regardless of age, such as diabetes, hypertension, human immunodeficiency virus infection, asthma and other chronic heart or lung diseases [1]. Although the vast majority of countries recommend vaccination of the at-risk population, vaccination coverage rates (VCR) for this group remains low in many countries (below the target of 75%), including more developed countries [4]. Brazil is the largest country in Latin America. The annual number of influenza cases was estimated to be between 4.2 and 6.4 million cases in 2008, and influenza-like illness led to 4.4% to 16.9% of hospital admissions between 2000 and 2008 [5]. Excess mortality associated with influenza was documented in Southern Brazil with 1.4/100,000 person-years for all ages, and 9.2/100,000 person-years for adults ≥60 years of age between 1980 and 2008 [6]. In Mexico the annual number of confirmed influenza cases was estimated to be between 0.8 and 1.1 million in 2008 [5]. All-cause influenza-associated mortality in Mexico was estimated as 20.3 deaths/100,000 for the 2010–2015 period [7]. Although the factors of age, comorbidities, and virus subtypes have been independently associated with mortality and intensive care unit (ICU) admission in hospitalized patients [8, 9], studies evaluating the cumulative risk of age and comorbidities in patients hospitalized with influenza are scarce in both countries. There are large publicly-available administrative hospital databases in both Brazil and Mexico that have been previously used to estimate burden of dengue at hospitals in Mexico or in-hospital mortality in Brazil, but not yet explored for seasonal influenza disease and for severe outcomes other than in-hospital mortality [10–12]. Using these databases, our study objectives were to estimate the number of hospital admissions with influenza in Brazil and Mexico, compare the associated severe outcomes in patients with or without comorbidities, and estimate the cumulative effect of comorbidities and age on these outcomes.
This cross-sectional study was based on national administrative hospital discharge data from Brazil between 2010 and 2018, and Mexico between 2010 and 2017.
Anonymized data from two publicly available administrative hospital discharge databases were used in this study. The Brazilian Hospital Information System of the Unified Health System (SIH/SUS) included discharge data from 5,930 public and private hospitals, which covered 88.4% of all hospitals in Brazil in 2014 [13]. This represented approximately 75% of the hospitalizations as only hospitalizations financed by the public healthcare system (SUS) are registered in this database. Approximately 103 million hospital admissions were recorded in the SIH/SUS database between January 1, 2010 and December 31, 2018. The Mexican Automated Subsystem for Hospital Discharges (SAEH) is the main hospital discharge database for all Ministry of Health hospitals in Mexico. It includes 859 hospitals, and represents approximately 40% of the hospitalizations in Mexico [14]. Approximately 23 million admissions were recorded in the SAEH database between January 1, 2010 and December 31, 2017. Approval by ethics committees and patient consent were not required because only anonymized data were extracted from pre-existing national hospital databases for analysis.
From these databases, we selected all hospital admissions with a primary or secondary discharge diagnosis of influenza. Discharge diagnoses were based on the International Classification of Diseases, 10th Revision (ICD-10) codes, version 2016 [15]. Influenza cases were defined using the following codes: J09 (influenza due to identified zoonotic or pandemic influenza virus), J10 (influenza due to identified seasonal influenza virus), J11 (influenza, virus not identified), and J12.9 (viral pneumonia, unspecified). In Brazil, the number of fields available for reporting secondary discharge diagnostic codes in the SIH/SUS database varied over time. Each entry could contain only one secondary code from 2010 to 2014, and up to nine codes from 2015 onwards. By contrast, in Mexico each entry in the SAEH database contained unlimited fields for secondary diagnoses over the whole study period. For consistency, we limited this analysis to the first nine secondary codes in Mexico. Comorbidities considered to increase the risk of influenza complications were selected using the published literature and WHO recommendations [3, 16], and grouped into four predefined categories: cardiovascular disease (ICD-10 codes: I10–I13, I15, I20–I25, I26–I28, I30–I52, I60–I74, I77–I89, I95, I97–I99), chronic obstructive pulmonary disease (COPD; J40–J47), diabetes (E10, E11, E13, E14), and immunodeficiencies (D71, D80–D84, D89). Three outcome measures were defined to assess severe influenza cases: hospital stay duration, admission to ICU, and in-hospital mortality.
Time series for weekly hospital admissions with influenza were calculated using the Serfling regression method [17] to model a basic level of influenza impact on each week outside of the epidemic period in each country. Epidemic peaks were defined as periods for which the observed admissions exceeded the upper 90% confidence limit of those predicted by the model (i.e., the epidemic threshold) for at least two consecutive weeks. Discharge records were stratified by patient age: 0–4 years, 5–17 years, 18–49 years, 50–64 years, and ≥65 years. ICU admission rates were calculated as the proportion of cases that were admitted to ICU during a given hospitalization. In-hospital mortality was assessed by case-fatality rates (CFRs), calculated as the proportion of influenza cases that died during hospitalization. Only in-hospital deaths during a given hospitalization episode were considered. The duration of hospital stay was calculated as the difference between the admission date and the discharge date. Relative risks (RRs), with associated 95% confidence intervals (CIs), were calculated as the ratios of the ICU admission rates or CFRs in cases with pre-defined comorbidities (cardiovascular, COPD, diabetes, immunodeficiency) to those without comorbidities. Finally, rates of severe outcomes for patients with predefined comorbidity with or without influenza were calculated. For each predefined comorbidity, association between having influenza, and a severe outcome was tested using a Fisher’s exact test. P values ≤0.05 were considered statistically significant.
In Brazil, 327,572 admissions with discharge ICD-10 codes associated with influenza were recorded in the SIH/SUS database between 2010 and 2018 (0.3% of the total admissions). In Mexico, 20,613 of such admissions were recorded in the SAEH database between 2010 and 2017 (0.1% of the total admissions) (Table 1). In Brazil, the most frequent ICD-10 code used for influenza admissions was J11 (influenza, virus not identified; 49.5%), followed by J12.9 (31.1%), J10 (19.4%), and J09 (0.04%), whereas in Mexico, the most frequent influenza code was J12.9 (viral pneumonia, unspecified; 63.8%), followed by J11 (25.4%), J09 (5.8%), and J10 (5.0%). In both countries hospital admissions associated with influenza occurred mostly in children aged <5 years (29.8% of admissions in Brazil and 58.4% in Mexico). Such admissions were also frequent in Brazilian adults aged ≥65 years (25.9%), but not in Mexican adults aged ≥65 years (7.7%). One or more comorbidity was present in 1% (95% CI, 0.9–1.1) of admissions with influenza in Brazil and 14.3% (95% CI, 13.8–14.7) of those in Mexico. These comorbidities were more frequent among admissions in patients aged ≥50 years. In both countries, weekly hospital admissions with influenza increased during specific periods of the year with an observable annual epidemic peak (Fig 1). In Brazil, weekly admissions ranged between 250 and 1,500, with an epidemic period varying from Week 15 (April) to Week 29 (July), except in 2017 in which the admissions remained below the epidemic threshold (Fig 1A). In Mexico, the weekly admissions ranged between 20 and 290 with an epidemic period varying from Week 03 (January) in 2014 to Week 09 (End February) in 2016 (Fig 1B).
Patients admitted with influenza in Brazil were hospitalized for a median duration of 3.0 days (interquartile range [IQR], 2.0–5.0), with 3.3% (95% CI, 3.2–3.3%) of patients being admitted to the ICU (Table 1). In Mexico, patients were hospitalized for a median duration of 5.0 days (IQR, 3.0–7.0), with 1.8% (95% CI, 1.6–2.0%) of patients being admitted to ICU (Table 1). In-hospital death was reported for 4.6% (95% CI, 4.5–4.7) of all admissions with influenza in Brazil and 6.9% (95% CI, 6.5–7.2) of those in Mexico. Most deaths occurred among patients ≥50 years of age, with 12,999/15,189 (85.6%) in-hospital deaths occurring in these patients in Brazil and 732/1,417 (51.7%) in Mexico.
The duration of hospital stay was generally longer in the presence of comorbidities, particularly for children <5 years of age in Brazil, and those 5–18 years of age in Mexico (Fig 2). In Brazil, the longest duration of hospital stay with influenza was for children <5 years of age with diabetes (median 16.5 days [IQR, 2.0–33.3]), and in Mexico it was for children 5–17 years of age with cardiovascular disease (median 8.0 days [IQR, 4.0–15.3]) (S1 Table). In Brazil, ICU admission rates and in-hospital CFRs increased with both age and with the presence of comorbidities (Fig 3A). ICU admissions in children aged 0–4 years were 2.1% (95% CI, 2.0–2.2) of those without comorbidities and 4.3% (95% CI, 2.9–5.7) of those with comorbidities, and in adults ≥65 years ICU admissions were 3.9% (95% CI, 3.8–4.0) of those without comorbidities and 16.9% (95% CI, 14.9–18.9) of those with comorbidities. Across age groups, the highest proportions of ICU admissions were in patients with cardiovascular disease, ranging from 20.1% (95% CI, 17.3–22.8) of patients ≥65 years to 44.4% (95% CI, 12.0–76.9) of patients aged 18–49 years. The presence of comorbidities increased in-hospital CFRs from 0.3% (95% CI, 0.3–0.3) to 0.6% (95% CI, 0.3–1.4) of children aged 0–4 years and from 11.6% (95% CI, 11.4–11.8) to 23.2% (95% CI, 20.9–25.4) of adults ≥65 years, compared to patients without comorbidities. This suggests a cumulative role of age and comorbidities on mortality in patients hospitalized with influenza in Brazil. As with ICU admissions, the highest CFRs were for patients with cardiovascular diseases in all age groups. In line with these results, the RRs of ICU admissions and in-hospital CFR in patients with comorbidities to those without comorbidities was highest for cardiovascular disease in all age groups, and especially in children 5–17 years of age (RR, 101.4 [95% CI, 39.6–259.8]) (Table 2). In Mexico, ICU admissions rates ranged between 0.5% (95% CI, 0.1–1.8) and 2.1% (95% CI, 1.5–3.1) of hospitalized patients without comorbidities and between 1.0% (95% CI, 0.0–2.3) and 3.9% (95% CI, 2.4–5.3) of those with comorbidities (Fig 3B). The only exception was for the three children 5–17 years of age with immunodeficiency who were all admitted to ICU. In patients ≥50 years of age, the presence of any comorbidity did not significantly increase ICU admission rates, except cardiovascular disease in patients 50–64 years of age (RR, 2.40 [95% CI, 1.09–5.28]) (Table 2). The presence of any of the pre-specified comorbidities did not significantly increase the RR of in-hospital CFR in adults ≥50 years of age in Mexico. However, compared to patients without comorbidities, in-hospital CFR increased in the presence of comorbidities from 0.4% (95% CI, 0.3–0.6) to 5.4% (95% CI, 3.5–7.2) of children 0–4 years, but varied only from 18.9% (95% CI, 15.3–23.0) to 21.4% (95% CI, 18.6–24.2) of those ≥65 years (Fig 3). The RRs of in-hospital CFR in patients with comorbidities to those without comorbidities were highest in children, with a maximum for children 5–17 years of age with diabetes (58.3 [95% CI, 20.8–163.4]) (Table 2). To get further insight into the role of comorbidities in severe influenza, we compared the rates of severe outcomes for each predefined comorbidity among hospital admissions with or without influenza. Both in Brazil (Table 3) and in Mexico (Table 4), all predefined comorbidities were significantly associated with higher in-hospital CFR and ICU admission rates in at least one age group in patients hospitalized with influenza, compared to patients with any disease other than influenza.
We used data from two large administrative health databases in Brazil and Mexico to estimate the number of hospitalizations with influenza, and to determine the role of age and comorbidities on the course of severity, based on ICD10 code discharge information. We found that the number of hospital admissions with influenza followed a seasonal profile, with specific periods presenting a peak of admissions for each country. The proportion of in-hospital deaths increased with increasing age in both countries (from 0.4% to 12.6% in Brazil and from 1.3% to 23.3% in Mexico). In Brazil, ICU admissions were higher in adults aged ≥50 years, and further increased in the presence of comorbidities, especially cardiovascular disease. In Mexico, comorbidities increased the risk of ICU admission and in-hospital CFR mostly in children. The proportions of ICU admissions in our study (3.3% [95% CI 3.2–3.3] in Brazil and 1.8% [95% CI 1.6–2.0] in Mexico) were lower than those reported in the USA (14%–19%), whereas the total rates of in-hospital death (4.6% [95% CI 4.5–4.7] in Brazil and 6.9% [95% CI 6.5–7.2] in Mexico) were in the same range as those reported in the USA(<4.7%) and Spain (4.8%), but lower than in Costa Rica (12%) [18–20]. These variations may reflect the differences in healthcare seeking behavior of patients, hospital capacity and medical healthcare management in these countries. An estimation published by Borja et al. using the United States Center for Disease Control (US-CDC) method showed that the percentage of patients seeking healthcare services in the USA during the 2009–2010 pandemic influenza season was around 50% of patients covered by a major public healthcare provider [21]. In non-pandemic season the proportion seeking healthcare services may be even lower, with the Mexican Ministry of Health estimating as few as 1 in 10 patients seeking healthcare services [22]. Additionally, this unexpectedly low number of ICU admissions in those ≥65 years could be also due to an ICD-10 code reporting bias, as more severe influenza cases may evolve to pneumonia and be preferentially reported using that code, as suggested by the frequent use of the J12.9 code (unspecified viral pneumonia) in our data (31.1% in Brazil and 63.8% in Mexico). Global estimates on the number of hospitalizations with influenza have been recently reported in a review of influenza-associated lower respiratory tract infections and hospitalizations among adults and reported a substantial number in the region of the Americas, which includes Brazil and Mexico, as high as 137 (95% CI 80–217) influenza-associated lower respiratory tract infection (LRI) hospitalizations per 100,000 population [23]. Estimates are particularly high for young children, older adults, and those with underlying conditions. In our study, in addition to providing local influenza admissions estimates, we have observed a role of age on the risk of severe outcomes in patients hospitalized with influenza, notably increased risks in those <5 years and those ≥50 years. This finding is consistent with a study in Chile showing that the risk of serious influenza illness was approximately 6 times higher in children <5 years of age and 13 times higher in adults ≥65 years of age compared to individuals between 5 and 64 years of age [24], or with observations from China, Hong Kong, Singapore, and Costa Rica, where most influenza-associated deaths occurred among older adults [20, 25]. While Brazil and Mexico are both upper-middle income countries, these results are also similar to data from administrative registries in high-income countries such as the USA, England, and Spain [18, 19, 26–28]. In addition to the role of age, the presence of comorbidities increased the duration of hospital stay, the risk of ICU admission, and death. Our results are aligned with recent studies demonstrating that comorbidities such as cardiovascular disease and COPD increase the risk of severe influenza outcomes. In England, 72% of influenza-attributable deaths in hospital occurred in adults ≥65 years of age with comorbidities. Also, the presence of comorbidities increased the admission rate by 1.8 fold in adults ≥65 years of age (from 0.46 to 0.84 per 1000) and by 5.7 fold in children 5–14 years of age (from 0.1 to 0.56 per 1000) [28]. Admission to ICU, in-hospital CFR estimates and any comparisons with other countries should be interpreted in light of the vaccine coverage rates (VCR) reported in these countries during the study period. According to data published by the Pan American Health Organization (PAHO), the influenza VCR among the elderly population in 2017 was 88% in Brazil and 94% in Mexico. In the pediatric population, the VCRs were 72% for Brazil and 84% for Mexico, respectively [29]. These differences of VCR may have affected the severity of the disease in some cases, impacting in some way the frequency of hospitalizations and ICU admissions in our study. In Brazil, the epidemic period ranged from April to July whereas in Mexico peaks of hospital admissions with influenza by year ranged from January to the end of February. This observation is consistent with epidemiological surveillance data showing that influenza epidemics occur during winters in temperate regions (i.e Mexico), and often during the rainy season or all-year-round in tropical regions (i.e Brazil) [30–32]. The recent study from Caini et al. demonstrated the substantial heterogeneity of spatio-temporal patterns of influenza epidemics in Latin American countries, including Brazil [33]. The strengths of this study are that the analyses were conducted using two very large hospital databases over several consecutive influenza seasons. These databases have not been explored for the estimation of hospital admissions with influenza before, but has previously been done with dengue for example [10–12]. We also defined influenza cases using a conservative approach, based on ICD-10 codes specific for laboratory-confirmed influenza virus and viral pneumonia, and therefore the number of admissions are highly specific to these laboratory confirmations. However, the results and estimates of this study should be interpreted in light of several limitations. These two databases are primarily designed for administrative or reimbursement purposes, therefore the number of admissions with influenza may have been underestimated as patients are not always diagnosed with influenza, nor is it routinely laboratory-confirmed during hospitalization [26]. With no laboratory confirmation, admissions due to influenza may have been coded as pneumonia or other respiratory diseases. Also, the numbers of admissions with influenza were defined based on discharge codes which did not include respiratory or cardiovascular complications due to influenza. Influenza is not always prioritized in discharge codes when facing multiple comorbidities. The association between respiratory infections, especially influenza, and acute myocardial infarction for instance has been reported to be significant and responsible for hospitalizations [34, 35], and so this may have biased the true estimates of hospitalizations with influenza. Finally, from January to April 2010, the two databases may have included hospitalizations with the pandemic A(H1N1)pdm09 influenza virus because it became a seasonal virus only after this period. However, this period represents a small fraction of the results in the analyses and we did not see more hospitalizations during this period than during the following years. Direct comparison of severe outcome estimates between Brazil and Mexico is limited due to several differences between the countries. There are differences in ICD coding practices with the number of comorbidities reported for a given hospital admission in Brazil limited to one principal diagnosis and only one secondary diagnosis between 2010 and 2014, whereas multiple comorbidities could be reported for Mexico, which may explain the lower prevalence of comorbidities reported in Brazil compared with Mexico (1.0% vs 14.3%), especially in adults ≥65 years of age (1.7% in Brazil vs 50.7% in Mexico). Also, the database in Mexico captured 40% of admissions in the country and only from public hospitals, whereas in Brazil the database covered approximately 75% of admissions. Finally, differences including hospital management and health seeking behaviors preclude direct comparison between the countries. In Mexico, most admissions were recorded for young children, whereas, in Brazil, admissions were equally recorded in all age groups which can be due to differences for age prioritization in health care systems. In conclusion, despite important limitations, the SIH/SUS and SAEH administrative hospital databases are useful to support estimations of number of hospitalizations with influenza and describe the associated severe outcomes. Developing linkage capacities between virological laboratories for the confirmation of influenza cases, private and public hospitals administrative databases could improve the estimates of hospitalizations associated to influenza at a national level. The cumulative role of age and comorbidities, especially cardiovascular disease, and their association with more severe outcomes in patients hospitalized with influenza is important, and should be further analyzed in prospective surveillance studies designed to support vaccination strategy decisions.
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PMC9648753 | Qiong Li,Ning Yan,Xinyue Miao,Yu Zhan,Changbao Chen | The potential of novel bacterial isolates from healthy ginseng for the control of ginseng root rot disease (Fusarium oxysporum) | 10-11-2022 | Ginseng root rot caused by Fusarium oxysporum is serious disease that impacts ginseng production. In the present study, 145 strains of bacteria were isolated from the rhizosphere soil of healthy ginseng plants. Three strains with inhibitory activity against Fusarium oxysporum (accession number AF077393) were identified using the dual culture tests and designated as YN-42(L), YN-43(L), and YN-59(L). Morphological, physiological, biochemical, 16S rRNA gene sequencing and phylogenetic analyses were used to identify the strains as Bacillus subtilis [YN-42(L)] (accession number ON545980), Delftia acidovorans [YN-43(L)] (accession number ON545981), and Bacillus polymyxae [YN-59(L)] (accession number ON545982). All three isolates effectively inhibited the growth of Fusarium oxysporum in vitro and the antagonistic mechanism used by the three strains involved the secretion of multiple bioactive metabolites responsible for the hydrolysis of the fungal cell wall. All three biocontrol bacteria produce indoleacetic acid, which has a beneficial effect on plant growth. From our findings, all three antagonistic strains can be excellent candidates for ginseng root rot caused by the pathogenic fungus Fusarium oxysporum. These bacteria have laid the foundation for the biological control of ginseng root rot and for further research on the field control of ginseng pathogens. | The potential of novel bacterial isolates from healthy ginseng for the control of ginseng root rot disease (Fusarium oxysporum)
Ginseng root rot caused by Fusarium oxysporum is serious disease that impacts ginseng production. In the present study, 145 strains of bacteria were isolated from the rhizosphere soil of healthy ginseng plants. Three strains with inhibitory activity against Fusarium oxysporum (accession number AF077393) were identified using the dual culture tests and designated as YN-42(L), YN-43(L), and YN-59(L). Morphological, physiological, biochemical, 16S rRNA gene sequencing and phylogenetic analyses were used to identify the strains as Bacillus subtilis [YN-42(L)] (accession number ON545980), Delftia acidovorans [YN-43(L)] (accession number ON545981), and Bacillus polymyxae [YN-59(L)] (accession number ON545982). All three isolates effectively inhibited the growth of Fusarium oxysporum in vitro and the antagonistic mechanism used by the three strains involved the secretion of multiple bioactive metabolites responsible for the hydrolysis of the fungal cell wall. All three biocontrol bacteria produce indoleacetic acid, which has a beneficial effect on plant growth. From our findings, all three antagonistic strains can be excellent candidates for ginseng root rot caused by the pathogenic fungus Fusarium oxysporum. These bacteria have laid the foundation for the biological control of ginseng root rot and for further research on the field control of ginseng pathogens.
Ginseng (Panax ginseng C.A. Mey.) belongs to the family of Wujia, a perennial herb with medicinal and food characteristics and rich nutritional value, mainly produced in China, Korea and other countries [1,2]. In order to ensure the medicinal value of healthy ginseng, a large number of active plant compounds have been isolated and extracted from the root, leaf and fruit parts of ginseng respectively. Specifically, ginsenosides, polypeptides, amino acids and other chemical components have been extracted from ginseng roots, and after their characterization, these compounds were found to possess anti-fatigue [3], anti-tumor [4], anti-diabetic [5], analgesic [6], antioxidant [7] and cardiovascular diseases [8,9]. Ginseng is different from other crops in that it is a crop where the commerciality of the roots is important [10]. It is crucial to cultivate ginseng with healthy roots during the cultivation period of up to 4~5 years [11]. Ginseng is often affected by biotic (microbial pathogens, pests, etc.) and abiotic (drought, high temperature, low temperature, salinity, etc.) factors during the growth process, resulting in complex disease problems [12]. Ginseng root rot is caused by soil fungal pathogens such as Fusarium solani and Cylindrocarpon destructans, resulting in dark brown root rot symptoms on the roots [9]. As a member of the genus Fusarium, Fusarium oxysporum has a wide variety of hosts, strong pathogenicity and serious damage [13,14], and is one of the most destructive pathogenic fungi in the soil, with a high survival rate in the soil under harsh environmental conditions, and in recent years can also cause serious rot on the roots of crops represented by ginseng [15]. According to statistics, the perennial incidence of root rot is around 10% to 20%, which has become a bottleneck problem limiting the sustainable development of ginseng [16]. At present, the main means to prevent fungal diseases of ginseng is to use fungicides, which are effective in controlling plant diseases, but long-term use may cause resistance to the disease and affect soil microorganisms and soil fertility, which cannot effectively alleviate the problem of ginseng diseases [11]. Biological control has become one of the most promising strategies for plant disease control because of its role in protecting the ecological environment, improving human and animal safety, and delaying the resistance of pathogenic bacteria [17,18]. Further, biocontrol products are effective in protecting plants from diseases in an environmentally friendly manner [19]. Current biocontrol products are developed with microorganisms such as bacteria, endophytic fungi and inter-rhizosphere microorganisms as the main raw materials [20–22]. Among them, bacterial biocontrol products are represented by bacteria of the genus Bacillus, which is considered as an ideal microorganism against pathogenic fungi due to its easy colonization of plant surfaces, fast growth rate and resistance [11]. U. R. Radzhabov and K. Davranov have reported that Bacillus subtilis SKB 256 is an active antagonist of Fusarium oxysporum and Pseudomonas syringae [23]. S.N. Sudha et al. have reported that the introduction of the cry1Ac Gene of Bacillus thuringiensis subspecies into Bacillus polymyxa has a dual benefit for rice crops, and they can be used not only as biological insecticide, but also as biofertilizer [24]. It can be said that the use of Bacillus as a biocontrol factor to control plant diseases has become a hot spot for biocontrol research in recent years. At present, there are few studies to screen biocontrol bacteria to specifically control ginseng root rot. Therefore, this study attempted to isolate biocontrol bacteria with the ability to control ginseng root rot from ginseng inter-root soil that was still growing healthily in heavily cropped ginseng fields. Morphological, physiological, biochemical, 16S rRNA gene sequencing and phylogenetic analyses were used to identify the strains. The morphology of the isolates was observed by scanning electron microscopy (SEM). Optimal growth parameters of the bacterial isolates, regarding temperature, pH, and rotary shaker speed, were also determined. The production and secretion of enzymes that would have a negative impact on F. oxysporum were also investigated to provide information on their potential role in the inhibitory activity displayed by the bacterial strains. Lastly, ginseng root disks were used to assess the ability of the bacterial strains to prevent infection by F. oxysporum in vitro. The overall purpose of our study was to identify bacterial isolates that exhibited biocontrol activity against ginseng root rot and potentially enhance the growth of ginseng plants. The identified strains provide a foundation for developing biological control of ginseng root rot and other fungal diseases of ginseng (Panax ginseng).
The experiments involved in this study were carried out in Jilin Ginseng Academy of Changchun University of Chinese medicine, and the work permits of relevant institutions were not involved. It is hereby declared that the situation is true.
Soil samples were collected from a three-year-old Panax ginseng plantation located in ZuoJia, JiLin Province, China in June 2021. Six healthy ginseng plants were harvested, and soil attached to the rhizosphere of the plants was collected. All soil samples were placed in aseptic bags and stored at 4°C prior to processing. Bacterial isolates were obtained using a serial dilution and plating method. One gram of soil was placed in 99 ml of water in a 250 mL bottle, along with some glass beads. The bottle was then placed at 30°C on a rotary shaker set at 200 r/min for 40 min [25]. The rhizosphere soil suspension was subjected 5x to a 10-fold dilution and each time, 100ul was spread on petri plates containing Luria-Bertani agar medium (5.0g yeast extract, 10.0g peptone, 10.0g sodium chloride, 15.0g agar, 1.0L distilled water, pH 7.2). The plates were incubated upside down in an incubator at 30°C until colonies were visible. Single colony isolations were collected, cultured, and maintained for subsequent analysis.
Fusarium oxysporum (Accession number:AF077393) was obtained from the China General Microbiological Culture Collection Center and cultured on potato dextrose agar (PDA) medium. The inhibitory activity of the bacterial isolates was assessed as the ability to inhibit fungal growth. The antifungal activity of the isolates was determined on PDA using the dual culture tests [26]. In the parallel confrontation test, fungal colonies (6mm) were collected using a cork borer from the edge of a F. oxysporum culture exhibiting strong growth and placed on a 90mm diameter petri dish containing fresh PDA. The bacterial isolates were then placed on the PDA medium approximately 2cm away from the fungus [27]. The growth of the fungal colony was observed 7d after inoculation of the plates with the bacterial isolates to assess inhibitory activity, relative to fungal growth on plates without bacterial isolates. The inhibition of fungal growth was calculated using the following equation: Where Dc represents control plate colony diameter and Dt represents colony diameter on plates inoculated with the bacterial isolates. treated plate mycelia growth. Each experiment was performed in triplicate and the replicates are presented as the mean ± standard error (n = 3).
The strains YN-42 (L), YN-43 (L) and YN-59 (L) were cultured on LB medium at 25°C for 3 days. Single bacterial colonies were selected, and colony morphology of each strain was observed and recorded based on Experimental Microbiology 3rd ed. Morphological indicators such as state, color, transparency, and edge were recorded. Gram staining was also performed and observed under a light microscope [27]. Observe the morphology of antagonistic bacteria and measure the size of the bacteria under scanning electron microscopy (SEM). Analyze and measure the size of the bacterium using Image-pro plus 6.0 software.
Various parameters of the selected bacterial isolates were assessed, based on the Manual for Systematic Identification of Common Bacteria and Bergey’s Manual of Determinative Bacteriology [28], including starch hydrolysis, lipid hydrolysis, casein hydrolysis, and gelatin hydrolysis. The following assays were also performed, hydrogen sulfide, catalase, contact enzyme, oxidase, indole, methyl red, citrate utilization, V–P test, urease, sucrose utilization, phenol red staining, fluorescent pigment, bromocresol purple staining, aerobic growth, and nitrate reduction, as well as several other physiological and biochemical tests.
Strains YN-42 (L), YN-43 (L) and YN-59 (L) with strong inhibitory activity against F. oxysporum were preliminarily identified by analysis of 16S rRNA gene sequences. Genomic DNA was isolated using a genomic DNA genome extraction kit (Tian Gen Biochemical Technology Co., LTD., Beijing, China). The 16S rRNA gene was amplified by Polymerase Chain Reaction (PCR) using the universal primers 27F (5′-GATCMTGGCTCAG-3′) and 1492R (5′-TACGGYTACCTTGTTACGACTT-3′). PCR amplification was performed in a thermocycler under the following conditions: initial denaturation at 96°C for 5 minutes, followed by denaturation at 96°C for 20 seconds, annealing at 62°C for 0.5 minutes, extension at 72°C for 0.5 minutes, and finally extension at 72°C for 10 minutes. The PCR products were visualized on a 1.0% agarose gel. The PCR products were purified using a magnetic bead purification kit (ShuoMei). Sequencing results were subjected to a BLAST analysis at NCBI. Phylogenetic and molecular evolutionary analyses were performed using MEGA version 6.0 with the following parameters: maximum likelihood using Kimura 2 method with 1000 bootstrap, neighbor joining method and pairwise distance matrix estimated using maximum composite likelihood (MCL) method, and homogeneity rate [29–31].
Cellulase activity was assayed according to the method described by Florencio Camila [32]. Cellulase activity was determined by using a plate screening medium containing 1% carboxymethylcellulose. Inoculated plates were incubated at 28°C, then stained with 0.1% Congo red dye solution for 15 min; the solution was discarded and the cultures were washed with 1 M NaCl for 15 min, and a clear area was observed around the bacteria, indicating cellulase production.
Chitinase activity assay was performed according to the method described by Agrawal and Kotasthane [33]. The assay medium was prepared: 4.5 g/L colloidal chitin, 3.0 g/L (NH4)2SO4, 0.3 g/L MgSO4, 2.0 g/L K2HPO4, 1 g/L citric acid monohydrate, 15 g/L agar, 0.15 g/L bromocresol violet, 200 μL Tween 80, pH 4.7, and autoclaved at 121°C for 15 min and incubated at 28°C and incubated the biocontrol strain for 72 hours. After incubation, a well-defined area was observed around the bacteria, indicating the production of chitinase.
Protease activity was determined by reference to Sokol’s method with minor modifications [34]. The biocontrol strain was inoculated into LB agar medium containing 3% skim milk powder and incubated at 28°C for 72 hours. After incubation, a clear area was observed around the bacteria, indicating protease production.
The CAS assay was applied to detect the production of iron carriers according to the method of Schwyn and Neilands [35]. Specifically, an iron(III) solution was prepared by mixing 1 mM FeCl3 with 10 mL of 10 mM HCl. In another beaker, an orange mixture was prepared by dissolving 60.5 mg of CAS in 50 ml of distilled water, which was then mixed with 10 ml of iron solution to change the color of the solution to purple. Dissolve 72.9 mg of HDTMA (cetyltrimethylammonium) in 40 ml of distilled water, and while stirring, slowly pour the previous purple solution into the HDTMA solution and mix to turn dark blue. pH was adjusted to neutral. After autoclaving (121°C, 20 min), allow to cool to 50–60°C and pour the plate with 900 mL of W A. After inoculation, incubate at 30°C for 5 d. Observe if a clear orange halo is detected around the bacteria growing in the medium.
Cultures of the three selected strains of biocontrol bacteria were incubated in 100 mL YEM broth containing 100ug/ml tryptophan [36]. And the IAA content was determined by the Salkowski’s reaction method [37]. Salkowski’s reagent containing 0.05 M ferric chloride was dissolved in 1 liter of 35% perchloric acid. An equal volume of Salkowski’s reagent was added to 2.0 ml of culture. The contents were mixed by shaking and allowed to stand at room temperature for 30 min to develop a pink color, estimated spectrophotometrically at 500 nm.
A single plug of medium (6 mm) containing a colony of each of the bacterial isolates, designated YN-42 (L), YN-43 (L), and YN-59 (L) was placed in LB liquid medium. The growth of the cultures at different temperatures, pH, and at different rotary shaker speeds was assessed. The OD600 value of the culture medium at 600 nm was measured with a UV1102Ⅱ UV-vis spectrophotometer and growth curves of the bacterial isolates were constructed to determine optimal growth conditions. Growth at different pH values (pH = 6, 7, 8), temperatures (25°C, 30°C, 35°C), and rotary shaker speeds (160 r/min, 180 r/min, 200 r/min) was assessed.
Fresh, healthy, three-year-old ginseng roots (‘Damaya’) were purchased from the Wanliang ginseng market, Fusong County, Jilin Province, China. The roots were washed with tap water to remove dirt and other material and then immersed in 70% ethanol (EtOH) for 5 minutes, then immersed in 2% sodium hypochlorite (NaClO) for 3 minutes, followed by another immersion in 70% EtOH for 2 minutes, after which the roots were thoroughly rinsed 3x with distilled water. The surface-sterilized ginseng roots were then cut into small discs (approximately 0.5 × 0.5 cm) using a sterile surgical knife prior to their use [11].
The assay of biocontrol activity was based on the protocol reported by Jang et al. and Song et al [38,39]. The bacterial isolates were cultured in LB liquid medium at 30°C for 24 hours on a rotary shaker set at 200 r/min. The OD600 of the culture suspension was then adjusted to 0.1. F. oxysporum, the ginseng root rot pathogen, was cultured on PDA medium for 7 days. Fungal spores were gently washed from the plates with sterile water and the obtained spore suspension was adjusted to 100 spores/ml [40]. The fungal conidial suspension was then evenly sprayed on the root discs laid out on a tray and left to air dry for 15 minutes. The tray with ginseng root disks in the treatment group were then evenly sprayed with bacterial suspension of one of the isolates. Disks that were not sprayed with a bacterial isolate served as a control. The processed ginseng root disks were then placed in a petri dish, covered with a layer of moist filter paper and cultured at 25°C for 3 days. All experiments were conducted in triplicate along with one set of control root discs to observe mycelia growth and rot development.
SPSS 21.0 was used for one-way analysis of variance (ANOVA), and significant differences between treatments was determined using a Duncan’s new multiple range test (p < 0.05). All graphics were produced in Microsoft Excel 2019.
In total, 145 isolates were isolated from rhizosphere soil of Panax ginseng. Among these, three of the bacterial isolates exhibited inhibitory activity against Fusarium oxysporum in vitro. The three strains were then subjected to further analysis to determine their identification and potential as biocontrol agents against ginseng root rot caused by Fusarium oxysporum, as well as their potential mechanism of action.
The antifungal activity of 145 isolates was screened. The three isolates, designated YN-42(L), YN-43(L) and YN-59(L) had significant inhibitory effect on the growth of F. oxysporum. (Fig 1). The level of inhibition exhibited by YN-42 (L) against F. oxysporum was > 60% (Table 1). Repeating the experiments several times indicated that the inhibitory effect was significant. The three strains were then subjected to further analysis and identification.
The colony morphology of the three strains was recorded (Table 2). The YN-42(L) (Bacillus subtilis) colonies on LB medium were white, oval, wrinkled, convex, opaque. The isolate was determined to be gram-positive, and colonies were found to consist of long strips of bacterial cells (0.30 to 0.39 × 0.99 to 1.43 μm) by scanning electron microscopy (Fig 2A). (Fig 2A). The YN-43(L) (Delftia acidovorans) colonies on LB medium were yellow, wrinkled, convex and translucent. The isolate was determined to be gram- negative. By scanning electron microscopy, colonies consisted of short, rod-shaped bacterial cells with a flagellum at one end (0.37 to 0.53 × 1.29 to 2.01 μm) (Fig 2B). The YN-59(L) (Providencia sp.) colonies on LB medium were white, round, wrinkled, convex, and opaque. The isolate was determined to be gram-positive. SEM observations showed that the cells were rod-shaped (0.68 to 0.72×1.45 to 1.60um) (Fig 2C).
The results of physiological and biochemical assays indicated that the three antagonistic strains all have a strong hydrolytic capacity, including the ability to hydrolyze starch, lipids, and gelatin. While YN-43(L) could hydrolyze casein, the other two strains did not exhibit this capacity. The IMVIC test indicated that the three strains exhibited the same positive reaction in the indole test and a negative reaction in the other IMVIC assays. Urease and oxidase activity could not be demonstrated in any of the three strains, however, all three strains had the capacity to produce fluorochromes. No ability to reduce nitrate to nitrite was detected and none of the three strains utilized sucrose. The tests also revealed that YN-43 (L) is anaerobic, while the other two strains are partly anaerobic. The phenol red test was negative for all three strains, however, all three strains were positive in the bromocresol violet test (Table 3).
DNA was extracted from three bacterial samples and then amplified using PCR method. The bands were clear and bright by gel imaging system. The DNA length of about 1500 bp was consistent with the expected size (Fig 3). The PCR products were sent to the company for sequencing and the isolated bacterial strains were identified by sequence analysis and compared using the BLAST analysis tool to confirm 99–100% strain identity coverage. Fig 4 shows the phylogenetic tree of the three identified bacteria whose accession numbers were separated based on bootstrap values, and the identified bacterial FASTA sequence data publication has been uploaded to NCBI and the respective gene sequence accession numbers were obtained. Phylogenetic trees were constructed in MEGA6. The results showed that YN-42 (L) (accession number ON545980) was most similar to Bacillus subtilis (100%), YN-43 (L) (accession number ON545981) was most similar to Delftia acidovorans (99%), and YN-59 (L) (accession number ON545982) (100%) was most similar to Bacillus polymyxa (100%). All three isolates showed strong inhibition of ginseng root rot. Therefore, we conducted further experiments on them.
Protease [41], cellulase [42], siderophores [43] and chitinase [44] activity was assessed in all three strains in the corresponding assays. An Orange halo was observed around colonies of all three strains cultured on CAS agar medium, indicating siderophore production. All three strains exhibited cellulase and protease activity as indicated by the clear area formed around colonies on the CYEA plate containing sodium carboxymethyl cellulose and skim milk (Table 4). These results demonstrate that all bacterial strains could secrete siderophores, proteases, and cellulases, however, none exhibited evidence of chitinase activity. The enzymatic and siderophore properties exhibited by the three strains would contribute to their biocontrol activity against fungal pathogens, such as F. oxysporum. Furthermore, a positive pink reaction to the addition of Salkowski’s reagent in a test tube containing culture supernatant of the three strains indicated that the YN-42(L), YN-43(L) and YN-59(L) strains, obtained from the rhizosphere soil of Panax ginseng, could all produce indoleacetic acid (IAA). The level of IAA produced by the three strains was 5.17mg/L, 5.88mg/L and 24.71mg/L, respectively. These results suggest that in addition to biocontrol activity against F. oxysporum, the three isolates could also promote the growth of ginseng plants.
The three strains quickly entered the logarithmic growth period at the initial stage of growth when cultured in LB medium. Growth began to level off after 12 hours but the population continued to increase. The cultures entered into stationary phase after 40 hours (Fig 5A). The growth rate of the three strains was tested at 25, 30, and 35°C. While some slight differences were observed, results indicated that 35°C was optimum for all three strains (Fig 5B). Growth of the three strains at different pH values (6.0, 7.0 and 8.0), was also assessed. Results indicated that YN-42 (L) grew best at pH 6.0, YN-43 (L) grew best at pH 7.0, and YN-59 (L) grew best at pH 8.0 (Fig 5C). The impact of different speed settings (150, 180 and 200 r/min) on the rotary shaker during culture was also assessed. While some slight differences were observed, in general all three isolates grew best at 200 r/min (Fig 5D).
Cut sections of ginseng root were first inoculated with the biocontrol strains followed by inoculation with the ginseng root rot pathogen F. oxysporum. Results of the in vitro test are present in Fig 6. Root disks in the control (not treated with the any of the bacterial strains) all exhibited signs of infection and were covered with mycelia from F. oxysporum. In contrast, root disks treated with the bacterial strains exhibited distinct evidence of inhibitory biocontrol activity. All three bacterial strains significantly inhibited the growth of F. oxysporum, however, the inhibitory activity exhibited by YN-42 (L) and YN-43 (L) was greater than YN-59 (L).
In plant disease and microbial ecosystems, when plant disease is severe, pathogenic bacteria occupy a favorable position and antagonistic bacteria are in a suppressed state [45]. When effective antagonistic bacteria are able to inhibit the growth and reproduction of pathogenic bacteria, the plant is free from disease or mild disease [46]. Studies have shown that antagonistic bacteria can not only reduce the number of pathogenic bacteria by rapidly occupying relevant physical and ecological sites, but also compete for nutrients and inhibit the growth of pathogenic bacteria, thus slowing down the invasion of pathogenic bacteria [47]. Therefore, screening of fungi, bacteria, actinomycetes and other microorganisms with inhibitory effects on pathogenic bacteria, and the development and application of new biological fungicides on this basis are necessary for the biological control of soil-borne diseases in agricultural fields and even the development of green agriculture [48,49]. In this experiment, 145 strains of bacteria were isolated and purified from the inter-rhizosphere soil of ginseng. Three different biocontrol bacteria, namely YN-42(L), YN-43(L) and YN-59(L), were screened by plate standoff. The results showed that all three antagonistic bacteria showed significant inhibition against F. oxysporum, which causes root rot of ginseng. After physiological and biochemical experiments and molecular sequencing, it was found that they showed high identity with Bacillus subtilis, Delftia acidovorans and Bacillus polymyxa, respectively. In addition, this paper reconfirmed that all three strains showed good biological control effects through root plate rewiring experiments, which have certain application value in biological control. At present, there are few studies on the screening of ginseng root rot pathogenic bacteria, and this paper provides a reference for the screening and control of ginseng root rot bacteria. Antagonistic biotrophic bacteria usually exert their biological control through their metabolites. The three strains screened in vitro were exposed to different metabolites such as indoleacetic acid, iron carriers and essential hydrolases. Indoleacetic acid is an essential secondary plant metabolite that is synthesized by bacteria to promote natural plant growth. In this study, three growth-preventive bacteria YN-42(L), YN-46(L), and YN-59(L) screened from ginseng inter-rhizosphere soil were able to produce indoleacetic acid, indicating that they not only antagonize F. oxysporum but also promote plant growth. Kotoky R also reported that Bacillus subtilis SR1 exhibited indole -3- acetic acid (IAA) production ability [50]. As an important growth hormone for plant growth, indoleacetic acid enhances the vitality of the root system to absorb more nutrients, thus increasing the resistance of the plant itself and improving its immunity to foreign disease invasion [51]. Iron carrier acts as a high-affinity iron chelator [52,53]. When it binds to iron, the organism secretes a soluble iron complex, which is not only essential for the survival of certain bacteria in an iron-limited environment, but is also one of the mechanisms by which biocontrol bacteria inhibit the growth of plant pathogens [54,55].The ability of pathogenic microorganisms to acquire iron from their hosts is one of the key steps in their development in their hosts, Colin Ratledge and Lynn G Dover reported [56]. However, in pathogenic microorganisms, iron carriers can steal iron from host proteins that can then be used by bacteria and other organisms [57]. In this experiment, the three strains screened produced iron carriers by forming an orange halo zone on the ferritin medium, suggesting that the mechanism of antagonism of the three biocontrol strains against root rot pathogens is also related to iron carrier production. Hydrolases that decompose periplasmic pathogenic microorganisms can be important predictors of plant diseases. Different species of Phytophthora can secrete different cell wall lytic enzymes such as protease, cellulase, and chitinase that exhibit degradation. In the present study, all three strains were positive for amylase, protease and cellulase production. These positive results may reveal that when ginseng root rot pathogenic bacteria infest the plants, the three strains of the biocontrol bacteria defend against them by secreting several of these enzymes to lyse the cell wall of the pathogenic bacteria [58].
In our study, the dual culture tests and in vitro biocontrol assays were used to identify and confirm the inhibiting effect of three bacterial strains, isolated from the rhizosphere of healthy ginseng plants, against F. oxysporum. The study provides a foundation for developing biological control of ginseng root rot disease. The initial tests conducted in this study were all conducted in vitro, and further studies conducted under filed conditions will be required to further validate the efficacy of these bacterial biocontrol agents against ginseng root rot and other soilborne diseases of ginseng, as well as their ability to promote the growth of ginseng plants. |
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PMC9648774 | Ha-Rim Lee,Kyung-Ah Kim,Bo-Yun Kim,Yoo-Jung Park,Yoo-Bin Lee,Kyeong-Sik Cheon | The complete chloroplast genome sequences of eight Orostachys species: Comparative analysis and assessment of phylogenetic relationships | 10-11-2022 | We analyzed the complete chloroplast genomes of eight Orostachys species and compared the sequences to those of published chloroplast genomes of the congeneric and closely related genera, Meterostachys and Hylotelephium. The total chloroplast genome length of thirteen species, including the eight species analyzed in this study and the five species analyzed in previous studies, ranged from 149,860 (M. sikokianus) to 151,707 bp (H. verticillatum). The overall GC contents of the genomes were almost identical (37.6 to 37.8%). The thirteen chloroplast genomes each contained 113 unique genes comprising 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. Among the annotated genes, sixteen genes contained one or two introns. Although the genome structures of all Orostachys and Hylotelephium species were identical, Meterostachys differed in structure due to a relatively large gene block (trnS-GCU-trnS-GGA) inversion. The nucleotide diversity among the subsect. Orostachys chloroplast genomes was extremely low in all regions, and among the subsect. Appendiculatae, genus Orostachys, and all thirteen chloroplast genomes showed high values of Pi (>0.03) in one, five, or three regions. The phylogenetic analysis showed that Orostachys formed polyphyly, and subsect. Orostachys and Appendiculatae were clustered with Hylotelephium and Meterostachys, respectively, supporting the conclusion that each subsection should be considered as an independent genus. Furthermore, the data supported the taxonomic position of O. margaritifolia and O. iwarenge f. magnus, which were treated as synonyms for O. iwarenge in a previous study, as independent taxa. Our results suggested that O. ramosa and O. japonica f. polycephala were individual variations of O. malacophylla and O. japonica, respectively. The exact taxonomic position of O. latielliptica and the phylogenetic relationship among the three species, O. chongsunensis, O. malacophylla and O. ramosa, should be a topic of future study. | The complete chloroplast genome sequences of eight Orostachys species: Comparative analysis and assessment of phylogenetic relationships
We analyzed the complete chloroplast genomes of eight Orostachys species and compared the sequences to those of published chloroplast genomes of the congeneric and closely related genera, Meterostachys and Hylotelephium. The total chloroplast genome length of thirteen species, including the eight species analyzed in this study and the five species analyzed in previous studies, ranged from 149,860 (M. sikokianus) to 151,707 bp (H. verticillatum). The overall GC contents of the genomes were almost identical (37.6 to 37.8%). The thirteen chloroplast genomes each contained 113 unique genes comprising 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. Among the annotated genes, sixteen genes contained one or two introns. Although the genome structures of all Orostachys and Hylotelephium species were identical, Meterostachys differed in structure due to a relatively large gene block (trnS-GCU-trnS-GGA) inversion. The nucleotide diversity among the subsect. Orostachys chloroplast genomes was extremely low in all regions, and among the subsect. Appendiculatae, genus Orostachys, and all thirteen chloroplast genomes showed high values of Pi (>0.03) in one, five, or three regions. The phylogenetic analysis showed that Orostachys formed polyphyly, and subsect. Orostachys and Appendiculatae were clustered with Hylotelephium and Meterostachys, respectively, supporting the conclusion that each subsection should be considered as an independent genus. Furthermore, the data supported the taxonomic position of O. margaritifolia and O. iwarenge f. magnus, which were treated as synonyms for O. iwarenge in a previous study, as independent taxa. Our results suggested that O. ramosa and O. japonica f. polycephala were individual variations of O. malacophylla and O. japonica, respectively. The exact taxonomic position of O. latielliptica and the phylogenetic relationship among the three species, O. chongsunensis, O. malacophylla and O. ramosa, should be a topic of future study.
The family Crassulaceae DC., belonging to Rosales Bercht. & J.Presl, includes succulent herbaceous plants. Approximately 1500 species in 35 genera are known and they are mainly distributed throughout the Northern Hemisphere [1, 2]. Among these, the genus Orostachys Fisch. includes approximately 20 to 25 taxa that are distributed from the Ural Mountains to Japan [3–5]. This genus has traditionally been used as an ornamental and a medicinal plant, and some species have recently been shown to be effective in antioxidant and anticancer treatments, thus becoming recognized as a very important plant resource [6, 7]. The taxa belonging this genus are mostly biennial herbaceous plants that are usually succulent. The morphological characteristics of this genus are as follows: the roots are fibrous and there is no rhizome. The leaves are linear to ovate, often with dull purple dots; the apex is usually cuspidate with a white and cartilaginous appendage that is softly obtuse or acuminate. In the first year, the leaves stand together in solitary, basal, and dense rosettes. The flowering stem arises from the center of the rosette in the second year. The inflorescence is a dense raceme or thyrse, narrowly pyramidal to cylindrical, and contains many flowers and foliage-like bracts. The flowers are bisexual, subsessile or pedicellate, and pentamerous. The sepals are usually shorter than the petals. The petals are subconnate at the base and are white, yellow-green, or pinkish to reddish [8, 9]. Due to differences in the growth habits among closely related taxa, the genus Orostachys was first described as a genus independent of Coyledon L. by Fischer [10]. However, Steudel [11] and de Candolle [12] proposed the classification of Orostachys within the genera Sedum and Umbilicus DC., respectively. Since then, various studies [1, 13, 14] have been conducted to support the proposal to treat Orostachys as an independent genus; as a result, it is currently recognized as a genus. The genus comprised two sections, Orostachys Ohba and Schoenlandia Ohba, that differ with respect to the shape of their leaves, number of stamens, and type of inflorescence [15]. Later sect. Schoenlandia was classified in a new genus, Kungia K.T.Fu [16]. Currently, therefore, the genus Orostachys has only one section, Orostachys, and it is split into two subsections, Appendiculatae (Boriss.) Ohba and Orostachys (Boriss.) Ohba, depending on the absence or presence of an appendage at the leaf apex [9, 17]. Recently molecular phylogenetic studies [18–21] revealed a large phylogenetic distance between the two subsections of Orostachys; subsect. Orostachys was in the Hylotelephium Ohba clade, and subsect. Appendiculatae formed a clade with Meterostachys Nakai. Therefore, more research is necessary to clarify the proper classification of Orostachys. The external morphology characteristics among species of Orostachys are very similar. Within each taxon, there is wide variation in external morphological characteristics. For these reasons, the classification of these plants is known to be very difficult. Although several taxonomic studies [5, 6, 18–22] have been conducted, the phylogenetic relationships among the species and the taxonomic position of many taxa remain unclear. We obtained the whole chloroplast genome sequences of eight Orostachys species (O. chongsunensis Y.N.Lee, O. latielliptica Y.N.Lee, O. malacophylla (Pall.) Fisch, O. iwarenge (Makino) H.Hara, O. iwarenge f. magnus Y.N.Lee, O. japonica f. polycephala (Makino) H.Ohba, O. margaritifolia Y.N.Lee, and O. ramosa Y.N.Lee), and compared the sequence to those of five published congeneric and closely related genera (Meterostachys and Hylotelephium) chloroplast genomes, i.e., those from O. japonica (Maxim.) A.Berger, O. minuta (Kom.) A.Berger, M. sikokianus (Makino) Nakai, H. erythrostictum (Miq.) H.Ohba, and H. verticillatum (L.) H.Ohba. The main goal of this study was to evaluate the phylogenomic relationships among the two subsections of Orostachys and its closely related genera. To address the fact that taxonomists differ in their opinions regarding the classification some species, it was our goal to clarify the taxonomic position of several taxa.
The eight Orostachys taxa examined in this study were not classified as endangered or protected. We did not collect materials from any privately owned or protected areas requiring permission for collection. The plant materials for this study were collected from the native habitats of each taxon, and the voucher specimens were deposited in the Sangji University Herbarium (SJUH) (S1 Table). Total DNA was extracted from approximately 100 mg of fresh leaves using a DNeasy Plant Mini Kit (Qiagen Inc., Valencia, CA, USA), and sequenced using the Illumina MiSeq and NovaSeq 6000 platforms (Illumina Inc., San Diego, CA, USA) at Labgenomics (Seongnam, Korea). The DNA of the Orostachys taxa was sequenced to produce 385,646–21,445,885 raw reads with lengths of 301 bp and 150 bp (S1 Table). Low-quality sequences (Phred score < 20) were trimmed using CLC Genomics Workbench (version 6.04; CLC Inc., Arhus, Denmark). Then, reads were assembled using a Geneious assembler with a medium sensitivity option via Geneious Prime v.2022.1.1 (Biomatters Ltd., Auckland, New Zealand). The draft genome contigs were merged into a single contig by joining the overlapping terminal sequences of each contig. The protein-coding genes, transfer RNAs (tRNAs), and ribosomal RNAs (rRNAs) in the chloroplast genome were predicted and annotated using Geneious Prime v.2022.1.1 and manually edited by comparison with the published chloroplast genome sequences of Orostachys. The tRNAs were confirmed using tRNAscan-SE [23]. A circular chloroplast genome map was drawn using the OGDRAW program [24].
The newly complete chloroplast genome sequences of eight Orostachys taxa were used along with the following chloroplast genome sequences from GenBank of NCBI for comparative analysis: two published Orostachys, O. japonica (accession no. MN794320) and O. minuta (accession no. OK094425) sequences, one Meterostachys, M. sikokianus (Makino) Nakai (accession no. MZ365442), and two Hylotelephium, H. erythrostictum (accession no. MZ519882) and H. verticillatum (accession no. MT558730) sequences. The program mVISTA was used to compare similarities among the thirteen species using the shuffle-LAGAN mode [25]. The annotated O. malacophylla chloroplast genome was used as a reference. Additionally, the genome structure of the thirteen species were compared using the MAUVE program [26]. The large single copy/inverted repeat (LSC/IR) and inverted repeat/small single copy (IR/SSC) boundaries of these species were also compared and analyzed.
To assess the nucleotide diversity (Pi) among the thirteen chloroplast genomes, including ten Orostachys, one Meterostachys and two Hylotelephium, the complete chloroplast genome sequences were aligned using the MAFFT [27] aligner tool and manually adjusted with BioEdit [28]. We then performed sliding window analysis to calculate the nucleotide variability (Pi) values using DnaSP 6 [29] with a window length of 600 bp and a step size of 200 bp [30].
Two data sets (whole chloroplast genome sequences and 79 protein-coding gene (PCG) sequences) from 34 Crassulaceae species were compiled into a single file of size 164,887 bp and 69,392 bp, respectively, and aligned using MAFFT [27]. Thirty-two Telephium clade [21] species were selected as the ingroups, and two species from subfam. Kalanchoideae (Cotyledon tomentosa Harv., Kalanchoe delagoensis Eckl. & Zeyh.) were chosen as the outgroups (S2 Table). Maximum likelihood (ML) analyses were performed using raxmlGUI v.2.0.6 with 1000 bootstrap replicates and the GTR+I+Γ model [31]. Bayesian inference (ngen = 1,000,000, samplefreq = 200, burninfrac = 0.25) was carried out using MrBayes v3.0b3 [32], and the best substitution model (GTR+I+Γ) was determined by the Akaike information criterion (AIC) in jModeltest version 2.1.10 [33].
The chloroplast genomes of eight new Orostachys species have been submitted to GenBank of the National Center for Biotechnology Information (NCBI) (Table 1). The total length of the chloroplast genomes of the thirteen species, i.e., the eight species analyzed in this study and the species analyzed in previous studies (O. japonica and O. minuta, M. sikokianus, H. erythrostictum, and H. verticillatum), ranged from 149,860 (M. sikokianus) to 151,707 bp (H. verticillatum), and among the Orostachys species, O. minuta was the smallest (150,369 bp) and O. latielliptica was the largest (151,462 bp) (Table 1 and Fig 1). All thirteen cp genomes exhibited the typical quadripartite structure, consisting of a pair of IR regions (25,285–25,854 bp), separated by an LSC region (82,293–83,070 bp), and an SSC region (16,839–17,018 bp). Their overall GC contents were almost identical (37.6–37.8%). The chloroplast genomes of the thirteen species contained 113 unique genes comprising 79 protein-coding genes, 30 tRNA genes, and four rRNA genes (Table 1). Among the annotated genes, fourteen genes (atpF, ndhA, ndhB, petB, petD, rpl2, rps12, rpl16, rpoC1, trnA-UGC, trnI-GAU, trnK-UUU, trnL-UAA, and trnV-UAC) contained one intron, and two genes (clpP and ycf3) contained two introns. The pairwise cp genomic alignment among the thirteen species (ten Orostachys, one Meterostachys and two Hylotelephium species) were all similar, with the exception of that of Meterostachys. The LSC and SSC regions were more variable than the IR regions (Fig 2). In the chloroplast genome of Meterostachys, the sequence similarity was very low in the relatively large gene block (trnS-GCU—trnS-GGA, approximately 37,000 bp) of the LSC region, which was confirmed to be caused by an inversion (S1 Fig). A comparison of the LSC/IR and IR/SSC boundaries in the thirteen species is shown in Fig 3. The rps19 gene crossed the boundary between the LSC (169 bp) and IRb (110 bp), and the ndhF gene and ycf1 gene were situated in the boundary IRb (15–44 bp) and SSC (2172–2199 bp), and the boundary SSC (4038–4071 bp) and IRa (1089–1092 bp).
The average nucleotide diversity (Pi) among the five subsect. Orostachys species (i.e., O. chongsunensis, O. malacophylla, O. iwarenge, O. iwarenge f. magnus, and O. ramosa), five subsect. Appendiculatae species (i.e., O. japonica, O. japonica f. polycephala, O. latielliptica, O. minuta, and O. margaritifolia), ten genus Orostachys species, and all the cp genomes selected in this study were estimated to be 0.001, 0.002, 0.009, and 0.026, respectively. Among the five cp genomes of subsect. Orostachys species, the Pi values were extremely low in all regions, and the region with the highest value (ycf4-cemA) had a Pi value of only 0.014. Among the five cp genomes of subsect. Appendiculatae species, only one region (ycf4-cemA) showed high values of Pi (>0.03). In the ten genus Orostachys species, five regions (rps16-trnQ, trnC-petN, ycf4-cemA, cemA, and ycf1) had a high value of Pi (>0.03). In all thirteen species, three regions (trnH-psbA, ycf4-cemA, and ycf1) had a high value of Pi (>0.03) (Fig 4). Meanwhile, the Pi values of rbcL and matK, which are corebarcode regions, were very low, 0.005 and 0.021, respectively.
The two ML trees constructed based on the two data sets, whole cp genome sequences and 79 protein-coding genes, were well supported at the genus level, except for those of Orostachys. The two ML trees were divided into two subclades. The first clade consisted of Rhodiola L. and Phedimus Raf., and both genera were well supported monophyly. The second clade comprised Orostachys, Meterostachys, Hylotelephium, Sinocrassula A.Berger and Umbilicus DC., and Umbilicus formed the most basal part. Orostachys formed polyphyly, and subsect. Orostachys and Appendiculatae were clustered with Hylotelephium and Meterostachys, respectively (Fig 5). In the subsect. Orostachys clade, O. iwarenge f. magnus formed the most basal part, followed by O. iwarenge, and O. malacophylla formed the sister to O. ramosa and O. chongsunensis in the ML tree based on whole cp genome sequences, whereas O. ramosa formed the sister to all other species, and O. iwarenge f. magnus formed the sister to O. malacophylla and O. chongsunensis in the ML tree based on 79 PCG sequences. In the subsect. Appendiculatae clade, O. latielliptica was clustered at the most basal part of the tree based on whole cp genome sequences, but O. minuta, which formed a clade with O. margaritifolia, was related to all other species in the tree based on 79 PCG sequences. Additionally, the close relationship between two species, O. japonica and O. japonica f. polycephala, was found in both trees.
Many recent studies have been carried out to clarify the taxonomy of related taxa using complete cp genome sequences. The cp genome is known to be highly conserved in most land plants, but structural changes in chloroplast genomes, such as gene duplication and deletion and inversion due to occasional rearrangements, provide important taxonomic data [34–42]. This study showed that the genome structures of Orostachys and Hylotelephium species were identical, and the sequence identities were also very similar among species in most of the chloroplast regions (Fig 2 and S1 Fig). The LSC/IRs/SSC boundaries were also very similar except for a very slight difference in sequence length (Fig 3). Therefore, these results indicated that the chloroplast genomes of Orostachys and Hylotelephium were very conservative, and that the two genera were closely related. However, the cp genome of Meterostachys was different in structure from those of the other taxa in this study due to the inversion of a relatively large gene block (trnS-GCU—trnS-GGA); this characteristic supports the classification of this group as unique genus.
In many phylogenetic studies [18–21], the genus Orostachys, first described by Fischer [10], was confirmed to be not monophyletic, and the two subsections, subsect. Orostachys and Appendiculatae, showed close relationships with Hylotelephium and Meterostachys, respectively. These results cast doubt on the classification of Orostachys to a single genus. In our study, we found that the genus was not monophyletic; we confirmed that each subsection was monophyletic and closely related to the two genera mentioned above (Fig 5). The two subsections of Orostachys were clearly distinguished morphologically by the absence (subsect. Orostachys) (Fig 6A) or presence (subsect. Appediculatae) (Fig 6B) of appendage such as a thorn at the leaf apex [9, 17]. Two genera, which were closely clustered with each subsection, were identified that shared this characteristic. Meterostachys, which formed a clade with subsect. Appendiculatae, is a monotypic genus containing only one species, M. sikokianus. It was initially described as Cotyledon sikokianus Makino [43]. Later, it was segregated into a new genus [44], but a relatively recent phylogenetic study [45] suggested that it should be classified in the genus Orostachys because it was situated within a subsect. Appendiculatae clade and formed a clade with Orostachys thyrsiflora Fisch. Meterostachys is clearly distinguished from Orostachys by inflorescence morphology (thyrsoid-paniculate to paniculate in Orostachys and bracteates cymose in Meterostachys) (Fig 6C and 6D) [15]. We found that Meterostachys had a unique chloroplast genome structure and formed an independent clade in this study (Figs 2 and 5). Therefore, we think that the taxonomic position of Meterostachys as an independent genus is supported. Based on these results, we strongly agree with Gontcharova et al. [19] that subsect. Appediculatae should be recognized as a distinct genus. Meanwhile, the phylogenetic relationships between subsect. Orostachys and Hylotelephium were not clear in previous studies [18–21]. The phylogenetic relationships examined in this study were very clear (Fig 5), but only two Hylotelephium species were assessed. Further studies including more diverse taxa are needed.
The phylogenetic relationships and taxonomic position of many taxa in Orostachys remain contested. Species, such as O. iwarenge f. magnus, O. ramosa, O. latielliptica, O. chongsunensis, and O. margaritifolia, first described by Lee and Lee [8, 46], are particularly problematic in terms of their taxonomic position because Ohba [17] treated these taxa as synonyms for O. iwarenge, O. japonica, and O. malacophylla, without taxonomic studies. In all ML trees obtained in this study (Fig 5), O. margaritifolia (Fig 6E), which was treated as synonym for O. iwarenge (Fig 6F) by Ohba [17], was not clustered within the subsect. Orostachys clade containing O. iwarenge, but in subsect. Appeidiculatae, characterized by the presence of an appendage at the leaf apex. This species was clearly distinguished from other taxa of this subsection due to morphological characteristics such as obovate leaves and purple anthers. Additionally, O. iwarenge f. magnus (Fig 6G), which has also been treated as a synonym for O. iwarenge (Fig 6F), was independently clustered, although the topology was different in each tree in this study. This species is distributed only on Ulleung-do Island, an oceanic island in Korea, and is reproductively completely isolated. Additionally, it is morphologically very similar to O. iwarenge, except for differences in leaf shape (oval in O. iwarenge f. magnus and oblong to spatulate in O. iwarenge) and stamen color (orange in O. iwarenge f. magnus, and yellow in O. iwarenge). Therefore, we strongly agree with Kim and Park [6] that these two species should be treated as independent taxa. O. latielliptica (Fig 6H) was described as a new species [8] because it has appendages at the leaf apex, as well as glaucous ovate leaves, and one to four aggregated flowers on its pedicel. The latter two characteristics are unique and distinguish them from all other species. Therefore, the taxonomic position of this species as an independent taxon is supported based on these characteristics. However, the exact phylogenetic relationship could not be confirmed because the topology of this species was different in the two ML trees of this study (Fig 5). Furthermore, this species showed a very close relationship to O. japonica, which was treated as synonym [17], in the ML tree based on 79 protein coding gene sequences in this study. To investigate the exact taxonomic position of this species, further in-depth studies are necessary. O. chongsunensis (Fig 6I and 6J), which was treated as a synonym for O. japonica by Ohba [17], was clustered within the subsect. Orostachys clade. This result was due to the absence of appendages at the leaf apex, indicating that O. chongsunensis is not the same species as O. japonica. In the ML trees in this study, O. chongsunensis showed a close relationship with O. ramosa (Fig 5A) and O. malacophylla (Fig 5B). Morphologically, this species was very similar to O. malacophylla except for purplish variegate leaves. Additionally, O. malacophylla was very similar to O. ramosa (Fig 6K) except that it does not branch at the base of the stem. In our experience, branching at the base of the stem is an occasional mutation in O. malacophylla (Fig 6N). Therefore, we concluded that the three species mentioned above are the same species; the purplish variegate leaves of O. chongsunensis are thought to be due to the growth environment and, specifically, factors such as exposure to limestone and the light intensity (Fig 6I and 6J). However, the exact phylogenetic relationship of O. ramosa could not be confirmed in this study because the topology in the two ML trees was different (Fig 5). Meanwhile, since individuals with branching at the base of the stem in O. japonica are also relatively common in the natural population of O. japonica (Fig 6M), we concluded that O. japonica f. polycephala (Fig 6L) is an individual variation of O. japonica. Additionally, O. japonica f. polycephala showed the closest relationship to O. japonica in all ML trees in this study (Fig 5), which well supported our opinion.
In this study, we assembled the chloroplast genomes of eight Orostachys, which had a total length ranging from 150,464 bp to 151,462 bp. The cp genomes of Orostachys and Hylotelephium had identical structures and were highly conserved. However, the structure of Meterostachys was different due to the relatively large gene block (trnS-GCU-trnS-GGA) inversion, which is considered important information supporting its the taxonomic position as an independent genus. The results of phylogenetic analyses suggested that the two subsections of Orostachys, subsect. Orostachys and Appendiculatae, were independent genera. In addition, the results supported the taxonomic position of O. margaritifolia and O. iwarenge f. magnus as independent taxa. The results also suggested that O. japonica f. polycephala and O. ramosa were synonyms for O. japonica and O. malacophylla, respectively. Meanwhlie, the taxonomic position of O. latielliptica remains unclear. Also, it is still unknown whether O. chongsunensis, O. malacophylla and O. ramosa, are the same species.
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PMC9648776 | YeongHo Kim,Hyemin Kim,JooHeon Cha,Si Hyeock Lee,Young Ho Kim | Validation of quantitative real-time PCR reference genes and spatial expression profiles of detoxication-related genes under pesticide induction in honey bee, Apis mellifera | 10-11-2022 | Recently, pesticides have been suggested to be one of the factors responsible for the large-scale decline in honey bee populations, including colony collapse disorder. The identification of the genes that respond to pesticide exposure based on their expression is essential for understanding the xenobiotic detoxification metabolism in honey bees. For the accurate determination of target gene expression by quantitative real-time PCR, the expression stability of reference genes should be validated in honey bees exposed to various pesticides. Therefore, in this study, to select the optimal reference genes, we analyzed the amplification efficiencies of five candidate reference genes (RPS5, RPS18, GAPDH, ARF1, and RAD1a) and their expression stability values using four programs (geNorm, NormFinder, BestKeeper, and RefFinder) across samples of five body parts (head, thorax, gut, fat body, and carcass) from honey bees exposed to seven pesticides (acetamiprid, imidacloprid, flupyradifurone, fenitrothion, carbaryl, amitraz, and bifenthrin). Among these five candidate genes, a combination of RAD1a and RPS18 was suggested for target gene normalization. Subsequently, expression levels of six genes (AChE1, CYP9Q1, CYP9Q2, CYP9Q3, CAT, and SOD1) were normalized with a combination of RAD1a and RPS18 in the different body parts from honey bees exposed to pesticides. Among the six genes in the five body parts, the expression of SOD1 in the head, fat body, and carcass was significantly induced by six pesticides. In addition, among seven pesticides, flupyradifurone statistically induced expression levels of five genes in the fat body. | Validation of quantitative real-time PCR reference genes and spatial expression profiles of detoxication-related genes under pesticide induction in honey bee, Apis mellifera
Recently, pesticides have been suggested to be one of the factors responsible for the large-scale decline in honey bee populations, including colony collapse disorder. The identification of the genes that respond to pesticide exposure based on their expression is essential for understanding the xenobiotic detoxification metabolism in honey bees. For the accurate determination of target gene expression by quantitative real-time PCR, the expression stability of reference genes should be validated in honey bees exposed to various pesticides. Therefore, in this study, to select the optimal reference genes, we analyzed the amplification efficiencies of five candidate reference genes (RPS5, RPS18, GAPDH, ARF1, and RAD1a) and their expression stability values using four programs (geNorm, NormFinder, BestKeeper, and RefFinder) across samples of five body parts (head, thorax, gut, fat body, and carcass) from honey bees exposed to seven pesticides (acetamiprid, imidacloprid, flupyradifurone, fenitrothion, carbaryl, amitraz, and bifenthrin). Among these five candidate genes, a combination of RAD1a and RPS18 was suggested for target gene normalization. Subsequently, expression levels of six genes (AChE1, CYP9Q1, CYP9Q2, CYP9Q3, CAT, and SOD1) were normalized with a combination of RAD1a and RPS18 in the different body parts from honey bees exposed to pesticides. Among the six genes in the five body parts, the expression of SOD1 in the head, fat body, and carcass was significantly induced by six pesticides. In addition, among seven pesticides, flupyradifurone statistically induced expression levels of five genes in the fat body.
Pesticides are indispensable in the agricultural industry for pest control, thereby reducing the loss of agricultural products and improving the yield and quality of food [1–3]. Without the use of pesticides, 78%, 54%, and 32% losses have been estimated in fruit, vegetable, and cereal production, respectively [4]. In 2018 and 2019, nearly 4.2 million metric tons of pesticides were used in the world, and the U.S. used approximately 0.4 million metric tons of pesticides [5]. Despite the benefits, pesticides can be toxic to other organisms, including birds, fish, and non-target insects [4]. Honey bees are particularly affected by unintentional exposure to pesticides during foraging. The phenomenon of rapidly declining honey bee colonies, resulting in 30–90% beehive disappearance, is known as colony collapse disorder (CCD) since its first report in the United States in late 2006 [6]. Although various factors, such as viruses, fungi, parasitic mites, limited floral resources, climate change, and the combination of these stressors have been suggested as reasons for CCD [7–11], neonicotinoid pesticides, including imidacloprid and clothianidin, have been widely reported to be the main factor causing CCD [6, 12]. CCD has been predominantly reported in North America and Europe, but honey bee colonies are also damaged by pesticide exposure due to agricultural activities on numerous other continents. In addition, the application of acaricides to control Varroa mites causes severe damage to honey bee colonies [13]. These pesticides can have destructive effects on honey bees, affecting immune system function, learning ability, memory, foraging behavior, and odor discrimination [14, 15], which in severe cases can have detrimental consequences for the colony [16, 17]. Although foragers are more frequently exposed to pesticides, the whole colony, including nurse bees, is also under the threat of agricultural chemicals as pesticide-contaminated nectar and pollen are delivered to the entire colony through foraging activities [18–22]. Considering that pesticides have been suggested to be a factor in large-scale honey bee decline including CCD, and nurse bees are also possibly exposed to pesticides, the xenobiotic detoxification metabolism in nurse bees should be comprehensively understood by the precise determination of target gene expression levels. In addition, the genes that are significantly increased or reduced in expression because of pesticide exposure can serve as biological markers for the analysis of damage to honey bees by pesticides. Quantitative real-time PCR (qRT-PCR) is the most extensively used method for gene expression analysis because of its rapid speed, high sensitivity, reproducibility, and accuracy. To ensure accurate normalization of target gene expression, the selection of optimal reference genes that are stably expressed under various conditions should be prioritized [23–25]. In previous studies, the suitability of the reference gene was evaluated under various conditions in honey bees, including developmental stages [26, 27], labor/seasons [28], bacterial challenge [29], tissues/seasons [30], and pesticide treatment [31, 32]. In the present study, to select suitable reference genes, we chose five candidate reference genes including 40S ribosomal protein S5 (RPS5), 40S ribosomal protein S18 (RPS18), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ADP-ribosylation factor 1 (ARF1), and Ras-related protein Rab-1A (RAD1a), which have been previously used as reference genes in honey bee studies [28, 30, 32]. Previous studies showed that honey bee is accidently exposed to various pesticides, and exposure of these pesticides negatively affect honey bee colonies, therefore, we chose seven pesticides (neonicotinoids: acetamiprid and imidacloprid, butanolide: flupyradifurone, organophosphate: fenitrothion, carbamate: carbaryl, formamidine: amitraz, and pyrethroid: bifenthrin) [33–38]. In addition, considering that different tissues might involve different detoxification mechanisms [39], we dissected five body parts (head, thorax, gut, fat body, and carcass) from honey bees treated with the seven pesticides. Then, expression stabilities of five reference genes were determined using their Cq distribution and four programs (geNorm, NormFinder, BestKeeper, and RefFinder). In addition, we compared expression levels of acetylcholinesterase 1 (AChE1) normalized with different combinations of reference genes and single genes to select common reference gene(s) for target gene normalization across different body parts of honey bees exposed to various pesticides. After the selection of suitable reference gene(s), transcription levels of genes, including AChE1, cytochrome P450 monooxygenases (CYPs), superoxide dismutase (SOD), and catalase (CAT), putatively involved in the chemical detoxification system were investigated by qRT-PCR using selected reference genes in the five body parts from honey bees exposed to the seven pesticides. The honey bee AChE1 has been suggested to be involved in various stress responses [40, 41]. In particular, AChE1 provides chemical defense against organophosphate (OP) and carbamate (CB) insecticides in honey bees [42]. CYPs are well-known enzymes involved in the metabolic detoxification of pyrethroids [43], and CYP9Q genes contribute to the metabolic detoxification of tau-fluvalinate, coumaphos, and neonicotinoids in honey bees [44, 45]. Considering that a lot of energy is required for pesticide detoxification, which leads to increased reactive oxygen species (ROS) production [46], the upregulation of antioxidant genes, such as SOD and CAT, is an essential physiological response to prevent ROS-mediated damage [47]. Therefore, to understand the physiological response in honey bees under various pesticide exposures, we selected optimal reference genes and determined the expression levels of AChE1, CYP9Q1, CYP9Q2, CYP9Q3, SOD1, and CAT. In addition, expression profiles of these genes increased or reduced after exposure to different pesticides in different body parts of honey bees will be expected to be used as possible molecular markers to identify the pesticides damaging honey bee colonies.
Italian hybrid honey bee (Apis mellifera) colonies were maintained in an experimental apiary (36° 36′ 69. 09″ N, 128° 11′ 70.42″ E) in Sangju-si, Gyeongsangbuk-do, Republic of Korea, with no miticide treatment. Adult nurse bees aged five to ten days old were used in the experiment [48]. To evaluate the toxicity of the pesticides, the analytical standard grade of seven pesticides (neonicotinoids: acetamiprid and imidacloprid, butanolide: flupyradifurone, organophosphate: fenitrothion, carbamate: carbaryl, formamidine: amitraz, and pyrethroid: bifenthrin) were purchased from Sigma-Aldrich (Merck, Saint Louis, MO, USA). The pesticides were dissolved in 100% acetone and stored at -20°C until use. To optimize the concentration of pesticides, we preliminarily treated the honey bees with the recommended field concentration of each pesticide; however, when honey bees showed high mortality at the field concentration, the concentration was reduced until it reached approximately LD20. The pesticide concentrations optimized in this study were: 40 ppm for acetamiprid; 1 ppm for imidacloprid; 85.5 ppm for flupyradifurone; 7.5 ppm for fenitrothion; 7.5 ppm for carbaryl; 200 ppm for amitraz; and 20 ppm for bifenthrin. Based on the test concentration of each pesticide, stock solution (×100) was prepared with 100% acetone, and then diluted 100-fold with 50% (weight/volume) sucrose solution. Ten microliters of the pesticide solution were orally administered to the honey bees. Honey bees were anesthetized with ice and fixed between the abdomen and thorax using stapler pins on a hard Styrofoam plate. Pesticide-treated honey bees were transferred to plastic cups containing 50% sucrose solution and maintained at 28°C and 60% relative humidity. As pesticide-treated samples were prepared in previous studies [49], at 24 h after pesticide exposure, the surviving honey bees were dissected into the head, thorax, gut, fat body, and carcass (excluding gut and fat body from the abdomen) under a microscope (Olympus, Tokyo, Japan).
RNA was extracted from prepared body parts (three replicates; five bees/replication) using the yesR™ total RNA extraction kit with a gDNA Eliminator column (Prefilter PF02) (GenesGen, Busan, Korea). The purity and quantity of the extracted RNA were measured in triplicate using a SpectraMax QuickDrop spectrophotometer (Molecular Devices, CA, USA). For cDNA synthesis, 1 μg of total RNA was primed with oligo (dT), and cDNA was synthesized using ReverTra Ace reverse transcriptase, according to the manufacturer’s protocol (Toyobo, Osaka, Japan). The CFX Connect Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA) was used for qRT-PCR with SYBR GREEN methodology. Primers of candidate reference and target genes were designed according to previous studies [28, 30, 32, 50], and those for AChE1 were newly designed in this study using Oligo Calc software (http://biotools.nubic.northwestern.edu/OligoCalc.html) based on sequence information (GenBank accession no. XM_016914793) (S1 Table). The PCR efficiency of each primer set was calculated from the given slope after running a standard curve generated with three points of 10-fold serial dilutions of cDNA using the E = 10−1/slope formula. qRT-PCR was conducted in duplicate (technical replicates) for a final volume of 20 μL, containing 10 pmol of each primer, 2x Thunderbird SYBR qPCR Master Mix (Toyobo, Osaka, Japan), and 5 μL of synthesized cDNA. The PCR was performed using the following protocol: 95°C for 1 min; and then 40 cycles at 95°C for 15 s, 56°C for 15 s, and 72°C for 30 s. One cycle of melting curve analysis (65°C to 95°C in 0.5°C increments) was carried out to check the amplicon specificity. The quantification cycle (Cq) values of five candidate reference genes and target genes (AChE1, CYP9Q1, CYP9Q2, CYP9Q3, SOD1, and CAT) were obtained from the different body parts of honey bees treated with the seven pesticides and control bees (no pesticide treatment) at the same fluorescence threshold line (0.1).
The Cq distribution of candidate reference genes across the seven types of pesticides and five body parts was analyzed using SigmaPlot 14.0, and the arithmetic mean (AM), standard deviation (SD), and coefficient of variation (CV) values were calculated (CV = SD/AM) (Fig 1). To analyze the expression stability of the five reference genes across different body parts and different pesticide treatment conditions, four programs (Table 1 and Fig 2): geNorm (version 3.3) [51], NormFinder (version 0.953) [52], BestKeeper (version 1) [53], and RefFinder [54] were used. NormFinder automatically calculates the stability values for all candidate reference gene-based overall variations to evaluate the systematic error introduced for gene normalization. Lower stability values indicate more stable genes; therefore, NormFinder ranked all candidates according to their stability values (S2 Table). BestKeeper identifies suitable reference genes based on the geometric mean of the Cq and SD values. The more stable genes have lower SD values, which are used in selecting a suitable reference gene [53] (S3 Table). The geNorm program calculates the expression stability M value, with more stable genes having lower M values (S4 Table). geNorm also reveals the analysis results of pairwise variation (Vn/Vn+1) to suggest the optimal number of references for the normalization of target genes (Fig 3). RefFinder provides a comprehensive ranking by combining the values of the three previous programs and the comparative Delta-Cq method [54] (S5 Table).
The Cq values from the reference genes and AChE1 were obtained from the same sample of body parts, and the expression level of AChE1 was normalized by the relative quantification method modified from the original concept of 2-ΔΔCq [55]. To select common reference gene(s) for target gene normalization across different sample conditions, the expression levels of AChE1 normalized to one of five genes and combinations of multiple reference genes were statistically compared in the different body parts of honey bees treated with seven pesticides using SPSS for Windows (version 23.0) with one-way analysis of variance (ANOVA) test and Tukey’s multiple comparison test (Fig 4). To apply multiple reference genes to normalize AChE1 expression levels, reference genes were selected based on the rank of the stable gene analyzed by RefFinder: combinations of two (RAD1a+RPS18), three (RAD1a+RPS18+ARF1), and four (RAD1a+RPS18+ARF1+RPS5) (Fig 2D),
To investigate the expression of target genes (AChE1, CYP9Q1, CYP9Q2, CYP9Q3, SOD1, and CAT) possibly induced by pesticide treatment in five different body parts, their expression levels normalized to the combination of the two most stable genes, RAD1a and RPS18, were statistically compared between the control (no pesticide treatment) and pesticide treatment conditions using the independent samples T-test with Tukey’s comparison analysis (Figs 5 and S2–S6).
Amplification specificities and efficiencies of the five candidate reference genes and target genes were examined using the primer sets used in this study. All PCR products showed a single band upon electrophoresis and a clear single peak in the melting curve analysis with real-time PCR (S1 Fig). As observed in previous studies [28, 30, 32, 50], eleven genes had linear regression coefficients R2 > 0.994 and efficiencies of 92–107%, indicating that all primer sets used in this study were acceptable (S1 Table). The expression levels of the five putative reference genes in the five body parts were analyzed (Fig 1). Based on the obtained Cq value for each gene, AM, SD, and CV values were determined. Based on the CV values, ARF1 and GAPDH were determined to be the most stable and least stable genes, respectively, in the head, thorax, and gut (Fig 1A–1C). In the fat body, the CV value of GAPDH (0.04) was higher than that of the other four genes, whereas RPS18 exhibited the lowest CV value (0.02). The CV values of the other three genes (RPS5, ARF1, and RAD1a) were 0.03 (Fig 1D). ARF1 showed the lowest CV value (0.02), whereas the CV values of the other four genes (RPS5, RPS18, GAPDH, and RAD1a) were equal (0.03) in the carcass (Fig 1E). The integrated CV values were calculated after the Cq values of each gene from the five different body parts from the honey bees exposed to the seven pesticides and those from the control group (no pesticide treatment) were combined. As judged by all integrated CV values, the stability rank from the most stable (lowest CV value) to the least (highest value) was as follows: ARF1 (0.04) > RPS5 = RAD1a (0.05) > RPS18 (0.06) > GAPDH (0.09) (Fig 1F). Considering that a CV < 1 indicates low variance [56], all five genes are acceptable as reference genes for the different body parts from honey bees exposed to various pesticides.
Although stability values analyzed by NormFinder for each gene were varied across different pesticide treatment conditions and body parts (S2 Table), RPS5 was ranked as the most stable gene in the head, thorax, and gut, whereas it was the third most stable with RAD1a being the most stable gene in the fat body and carcass (S2 Table). When the expression variations of each gene obtained from the five body parts were combined, RAD1a was ranked as the most stable gene under acetamiprid, flupyradifurone, and carbaryl exposure, and RPS5 was the optimal gene in the control, imidacloprid, and fenitrothion treatment conditions. In addition, ARF1 was most stably expressed in carbaryl- and bifenthrin-treated bees. In the amitraz treatment group, RPS18 was the most stable (Table 1). When all expression stability values were integrated from the five body parts, the stability rank from the most to least stable was as follows: RAD1a > RPS18 > RPS5 > ARF1 >> GAPDH (Table 1 and Fig 2A). Considering a stability value < 0.15 as the criterion for suitable reference gene selection in NormFinder [57, 58], all genes were acceptable across different conditions, but GAPDH (stability value = 0.166) was not suggested as a qRT-PCR reference gene for thorax of honey bees exposed to pesticides (Table 1 and Fig 2A).
Candidate genes with low SD (usually < 1.0) are suggested as appropriate reference genes by the BestKeeper analysis [59]. The SD values of candidate reference genes varied across different body parts of honey bees treated with various pesticides (S3 Table). As indicated by SD values < 1.0, all five genes were determined to be optimal reference genes in the head, gut, fat body, and carcass of honey bees exposed to the seven pesticides, and the control. In the thorax, ARF1, RAD1a, and RPS5 exhibited SD values < 1.0, and the SD of RPS18 (1.037) was slightly higher than 1.0, whereas that of GAPDH was 2.865 (Table 1). When data from the different body parts treated with acetamiprid, imidacloprid, flupyradifurone, fenitrothion, and amitraz were integrated, GAPDH was found to be unstable for target gene normalization due to its high SD values (> 1.0). In the case of carbaryl and bifenthrin treatments, the respective SD values of RPS5 and RPS18 were slightly higher than 1 (Table 1). In the control group, RPS18 exhibited a slightly higher SD value; however, the other four genes were applicable as appropriate reference genes. Based on the SD values of all integrated data, four genes (ARF1, RAD1a, RPS5, and RPS18) were determined as optimal reference genes, but the SD value of GAPDH was slightly higher than the cut-off line (1.116) (Table 1 and Fig 2B).
The average expression stability values (M values) were analyzed using geNorm across the body parts of honey bees exposed to different pesticides (S4 Table). As suggested in previous studies, an M value under 0.5 (M ≤ 0.5) is an acceptable criterion for the selection of reference genes [60, 61]. In each sample of body parts, the M values of all five candidate reference genes were below the cut-off value (0.5), except for the thorax sample. In the thorax, the M values of RPS5 (0.583) and RPS18 (0.589) were slightly higher than the cut-off value (0.5), whereas those of ARF1, RAD1a, and GAPDH were 0.675, 0.79, and 1.179, respectively (Table 1). According to the M values integrated from all body parts treated with each pesticide, RPS5 and RPS18 were determined to be stably expressed in each pesticide treatment, while the M values of RAD1a were higher than 0.5 in fenitrothion and bifenthrin treatment conditions, and that of ARF1 was also higher than the cut-off value in the bifenthrin sample. In addition, GAPDH was not recommended as an optimal reference gene for all pesticide treatments (Table 1). In all integrated analyses, the M values of four genes (RPS18, RPS5, RAD1a, and ARF1) were < 0.5, whereas that of GAPDH was 0.952 (Table 1 and Fig 1C). In addition to the M value analysis, the number of reference genes for reliable normalization of target genes in qRT-PCR was determined using pairwise variation (Vn/Vn+1) values calculated in geNorm (Fig 3). Based on the cutoff value (0.15) suggested in previous studies [61, 62], any number of reference combinations was applicable for target gene normalization in the head, gut, fat body, and carcass because all subgroups showed lower pairwise variations (V2/V3, V3/V4, and V4/V5) than the cutoff value (0.15) (Fig 3A, 3C, 3D and 3E). However, in the thorax, Vn/Vn+1 values varied depending on the pesticide (Fig 3B). In the thorax, the lowest Vn/Vn+1 values in the fenitrothion and bifenthrin treatments were V2/V3 (0.173) and V3/V4 (0.193), and the combined pesticide treatment data showed the lowest Vn/Vn+1 values at V3/V4 (0.202) (Fig 3B). When all data from different body parts treated with pesticides were integrated, Vn/Vn+1 values lower than the cutoff value were obtained for the control and five pesticide treatment groups (acetamiprid, imidacloprid, flupyradifurone, carbaryl, and amitraz) (Fig 3F). The V2/V3 ratio was 0.157 in the fenitrothion treatment, and V3/V4 was 0.193 in the bifenthrin treatment group. The lowest Vn/Vn+1 value was 0.192 at V2/V3 for all integrated data (Fig 3F).
Based on the three different algorithms, comprehensive stability was calculated, and the ranks of candidate reference genes were obtained using RefFinder (Tables 1 and S5). The comprehensive rank of each gene varied depending on the body parts and pesticide treatment (S5 Table). In general, RPS5 was ranked as the most stable gene in the head, gut, and fat body, but it was the second and fourth most stable gene in the carcass and thorax, respectively. RPS18 was ranked first in the thorax but the second most stable gene in the head, gut, and fat body. In the carcass, RAD1a was ranked as the most stable gene (Table 1). When body part samples were combined from each pesticide treatment condition, RAD1a was the most stable gene in the acetamiprid, flupyradifurone, and bifenthrin treatments. RPS18 was ranked as the most stable gene under imidacloprid and carbaryl exposure conditions, whereas RPS5 was determined to be the most stable gene under amitraz treatment conditions and in the control group body parts. In the fenitrothion treatment, ARF1 was selected as the most stable gene (Table 1). When the comprehensive rank was calculated from the integrated data of the five body parts treated with seven pesticides, the stability rank from the most to least stable was as follows: RAD1a > RPS18 > ARF1 > RPS5 > GAPDH (Table 1 and Fig 2D).
According to the pairwise variation values analyzed by geNorm, the optimal number of reference genes for target gene normalization varied depending on the body parts, pesticide treatment, and the control (Fig 3). We selected AChE1 as the target gene for the validation of candidate reference genes by comparing its expression levels normalized by a single gene among five candidates and combinations of different numbers of reference genes. Based on the rank of the stable gene in the RefFinder analysis (Fig 2D), candidate reference genes were selected for the combinations of multiple reference genes. When expression levels of AChE1 normalized with the combination of two (RAD1a+RPS18), three (RAD1a+RPS18+ARF1), and four (RAD1a+RPS18+ARF1+RPS5) reference genes were compared, in general, they exhibited statistically similar transcription levels of AChE1 except for the carbaryl-treated head, carbaryl-treated gut, and flupyradifurone-treated fat body (Fig 4). However, in these conditions, the combination of two genes was suggested by pairwise variation analysis (Fig 3). These indicate that the combination of two genes (RAD1a+RPS18) is sufficient for target gene normalization rather than three or four genes. When the expression levels of AChE1 were normalized with a single gene among the five genes, those normalized with RPS5, RPS18, and GAPDH showed significantly similar expression levels in all body parts and pesticide treatment groups (Fig 4). However, if the expression levels of AChE1 normalized with one of these three single genes were compared with those calculated with the combination of two reference genes, they computed statistically different expression levels of AChE1 in carbaryl-treated head and gut, suggesting that application of a single gene is not appropriate for comparing target gene expression levels across various body parts of honey bees treated with different pesticides.
As a combination of two genes (RAD1a+RPS18) was suggested to be the most suitable method for target gene normalization (Fig 4), the expression levels of genes putatively involved in detoxification mechanisms were normalized with the combination of RAD1a and RPS18 (Figs 5 and S2–S6). As summarized in Fig 5, the induction or reduction ratio of each gene did not show significant trends depending on the body parts and pesticide when the expression ratio of genes after pesticide treatment was compared with that of the non-treated control. However, expression of genes in the gut, fat body, and carcass, as compared to that in the head and thorax, were generally induced by pesticides. Among the seven pesticides, in the head, imidacloprid significantly induced the expression of AChE1 and three CYP9Qs (p < 0.05) and significantly reduced CAT expression (p < 0.01). However, CAT was significantly induced by exposure to fenitrothion, carbaryl, amitraz, and bifenthrin (p < 0.05), whereas the expression levels of SOD1 were significantly higher than those in the control group when honey bees were treated with acetamiprid, flupyradifurone, fenitrothion, carbaryl, amitraz, and bifenthrin (Fig 5, see head; S2 Fig). In the thorax, amitraz reduced the expression of CYP9Qs (p < 0.05). Interestingly, CAT expression was significantly reduced by exposure to five pesticides (acetamiprid, imidacloprid, flupyradifurone, fenitrothion, and carbaryl), whereas SOD1 expression was induced by four pesticides (flupyradifurone, fenitrothion, carbaryl, and amitraz) (p < 0.05) in the thorax (Fig 5, see thorax; S3 Fig). In the case of the gut, AChE1, CYP9Q2, and CYP9Q3 were induced by imidacloprid, carbaryl, and flupyradifurone, respectively, whereas CAT was induced by flupyradifurone and amitraz (p < 0.05). Three pesticides (flupyradifurone, carbaryl, and amitraz) significantly induced the expression of SOD1 (p < 0.05) in the gut (Fig 5, see gut; S4 Fig). In the fat bodies, six and five pesticides strongly induced the expression of SOD1 and CAT (p < 0.05), respectively, whereas AChE1 was induced by flupyradifurone, and three CYP9Qs were induced by respective two pesticides (p < 0.05). Among the pesticides, flupyradifurone strongly induced the expression of five genes (AChE1, CYP9Q1, CYP9Q2, CAT, and SOD1) (p < 0.05) (Fig 5, see fat body; S5 Fig). In the carcasses of pesticide-treated honey bees, the expression levels of AChE1 and three CYP9Qs did not differ from those of the control. Four and five pesticides significantly affected the expression levels of CAT and SOD1, respectively (p < 0.05) (Fig 5, see carcass; S6 Fig).
Since, in addition to foragers, nurse bees have been suggested to also be possibly exposed to agricultural chemicals via pesticide-contaminated nectar and pollen [18–22], accurate determination of gene expression levels is essential to identify the genes putatively involved in the physiological response to pesticides in nurse bees; the expression profiles significantly induced or reduced by pesticide exposure can be used as molecular markers to identify the pesticide damaging honey bee colonies. Owing to the possibility of variability in reference gene expression, the selection of appropriate reference genes that are stably expressed across different pesticide treatment conditions is necessary before conducting qRT-PCR for accurate determination of target gene expression. In the previous study [32], the optimal reference genes have been suggested in three body parts (head, thorax, and abdomen) of honey bees treated with four pesticides (imidacloprid, flupyradifurone, coumaphos, and fluvalinate). Considering that the detoxification mechanisms in various body parts may differ depending on the type of pesticide, in addition to three body parts (head, thorax, and abdomen), qRT-PCR reference genes should be evaluated in specific body parts, such as the gut and fat body, of pesticide-treated honey bees. Furthermore, various pesticides may affect honey bee colonies, therefore, expression stabilities of candidate reference genes should be additionally investigated in honey bees exposed to more various pesticides. Therefore, in this study, using pesticides and body parts that have not been previously studied, we evaluated the expression stabilities of five candidate reference genes using Cq distribution analysis and four different software platforms in honey bees. Consistent with previous studies [28, 30, 63–65], the four programs resulted in different gene stability ranks depending on the different sample conditions (Table 1 and Fig 2 and S2–S5 Tables). geNorm analyzed pairwise variation as a guide to suggest the optimal number of reference genes for normalization of target gene expression (Fig 3). Vn/Vn+1 < 0.15 is usually used as a cutoff value in geNorm pairwise variation analysis [51, 61, 62]; however, the Vn/Vn+1 value seems to be applied leniently. According to previous studies [51, 66], Vn/Vn+1 < 0.2 was also suggested to be the cutoff value in pairwise variation analysis, and the lowest Vn/Vn+1 was widely applied to suggest the number of reference genes for target gene normalization regardless of the value [67], suggesting that the cutoff value of Vn/Vn+1 is not crucial. Based on the Vn/Vn+1 values in this study, geNorm pairwise variation analysis showed that combinations with different numbers of reference genes were suggested for target gene normalization in different conditions of honey bee samples, across body parts and pesticide treatments (Fig 3). However, to compare target gene expression levels across different body parts from honey bees exposed to various pesticides, selection of reference gene(s), which are commonly applied across different conditions, is essential. Therefore, to select common reference genes, we compared the expression levels of AChE1 as a target gene, normalized with different combinations of multiple reference genes and a single gene among five candidate references in the five body parts treated with the seven pesticides (Fig 4). Since RefFinder provides a comprehensive ranking by combining the values of three programs (NormFinder, BestKeeper, and geNorm) and the comparative Delta-Cq method [54], references for multiple gene combinations were selected according to the integrated rank of RefFinder (Fig 2D). Although combinations of different numbers of genes were suggested by the geNorm pairwise variation analysis depending on different sample conditions (Fig 3), AChE1 exhibited identical expression levels in most sample conditions, regardless of normalization with the combination of two (RAD1a+RPS18), three (RAD1a+RPS18+ARF1), and four (RAD1a+RPS18+ARF1+RPS5) reference genes (Fig 4). In the carbaryl-treated head, carbaryl-treated gut, and flupyradifurone-treated fat body, expression levels of AChE1 normalized with two genes were not statistically similar with those computed with three reference gene combinations (Fig 4), but the lowest values of pairwise variation were V2/V3 in these condition (Fig 3), suggesting that selection of two genes is the most optimal in these conditions. Taken together, to compare target gene expression levels across different body parts of honey bees exposed to various pesticides, the combination of two genes (RAD1a+RPS18) is sufficient for target gene normalization. A small number of reference genes might be suitable if target gene expression levels calculated by the combination of a different number of multiple reference genes are not significantly different, which also reduces the financial and technical burden in experiments; therefore, the application of a single reference gene has also been suggested in the previous studies after comparison of target gene expression normalized with multiple genes and a single gene [28, 30, 63]. Similarly, in this study, AChE1 expression levels normalized with a single gene among RPS5, RPS18, and GAPDH were generally similar to those normalized with the combination of multiple reference genes (Fig 4). However, in the carbaryl-treated head, RPS18 and GAPDH were not suggested as single reference genes because expression levels of AChE1 normalized with one of these two genes were significantly different from those calculated with multiple reference genes (Fig 4). Furthermore, in the carbaryl-treated gut, no single gene demonstrated an expression level of AChE1 that is similar to those calculated with multiple reference genes (Fig 4). Based on the comprehensive results analyzed by expression levels of AChE1, a single gene is not sufficient but the combination of two genes (RAD1a+RPS18) is more appropriately applied as the common reference genes for comparison of target gene expression levels across different honey bee samples. In addition to RefFinder, three software also analyzed that RAD1a and RPS18 were ranked as the most stable (Fig 2). Based on the criteria: mean stability < 0.15 in NormFinder [57, 58], SD value < 1.0 in BestKeeper [59], and M < 0.5 in geNorm [60, 61], the calculated values of both genes were below the cut-off values (Fig 2 and Tables 1 and S2–S4). Cq distribution analysis also revealed CV values < 1, which indicates low variance (Fig 1) [56], suggesting that the combination of RAD1a and RPS18 can be selected as reference genes for target gene normalization in different body parts from honey bees exposed to various pesticides. RPS18 and RAD1a have been widely investigated as the optimal reference gene in honey bees under various conditions, such as different seasons (12 months), in different tissues (head, thorax, and abdomen), in adult labor roles (nurse and forager), in different developmental stages, pesticide treatments, and under bacterial challenge [27–30, 32, 68], further supporting the suggestion that RAD1a and RPS18 are the most appropriate reference genes in honey bee studies. Based on the analysis of reference gene expression stability with Cq distribution (Fig 1), taken together, four programs (Table 1 and Fig 2), pairwise variation (Fig 3), and target gene normalization (Fig 4), the combination of RAD1a and RPS18 is suggested to be used as the most suitable method for normalization of target gene expression levels in the qRT-PCR assay in different body parts of honey bees exposed to various pesticides.
As summarized in the integrated results of reference gene validation, since the combination of RAD1a and RPS18 was determined to be the optimal reference gene set in this study (Figs 1–4 and Table 1), expression profiles of genes putatively associated with the pesticide detoxification process were investigated in the body parts of honey bees exposed to seven pesticides. Pesticides are known to generate oxidative stress through ROS production in various animals, including humans and insects [69]. Antioxidant enzymes, such as SOD, CAT, glutathione S-transferase (GST), glutathione peroxidase (GPx), and glutathione reductase, play critical roles in defense against oxidative stress in organisms [70–74] and are also associated with pesticide detoxification in insects [75]. In particular, SOD, CAT, GST, and peroxidase have been identified as the most important antioxidant enzymes in honey bees [76–78]. Among these enzymes, the activity of the GPx and CAT was elevated by imidacloprid exposure in A. mellifera [79]. Furthermore, in A. dorsata and A. cerana, three different pesticides, dimethoate (OP), cypermethrin (pyrethroid), and endosulfan (organochlorine), significantly increased the enzymatic activity of CAT and SOD [80]. These studies indicate that oxidative stress might be induced by exposure to various pesticides in honey bees. In the present study, the transcription of CAT and SOD1 was induced in the gut, fat body, and carcass, although their expression levels varied compared with the control group (Fig 5). Interestingly, the expression of SOD1 in the head and thorax was induced by exposure to seven and four pesticides, respectively, but the expression levels of CAT were significantly reduced by five pesticides (acetamiprid, imidacloprid, flupyradifurone, fenitrothion, and carbaryl) in the thorax (Fig 5). Moreover, in the head, CAT expression was significantly reduced by imidacloprid but was significantly increased by exposure to fenitrothion, carbaryl, amitraz, and bifenthrin (Fig 5). Similar large variations in antioxidant enzyme activities, including SOD, CAT, and GST, were investigated in different tissues (head, midgut, and abdomen) of honey bees exposed to imidacloprid, glyphosate, and difenoconazole, alone and in binary and ternary mixtures [81]. This suggests that antioxidant enzymes might respond differently in different body parts and to different types of pesticides. However, it seems clear that CAT and SOD were induced more in the gut, fat body, and carcass than in the head and thorax (Fig 5). Because the honey bees were fed sucrose solution containing pesticides in this study, the gut would be the primary organ directly exposed to pesticides. In addition, the midgut is one of the main sites of detoxification in insects [82]. A recent study revealed that the expression of key enzymes of the honey bee xenobiotic detoxification pathway is promoted by the gut microbiota [83]. After being metabolized in the midgut epithelial cells, pesticides are transported into the hemolymph across the basal membrane [84]; and the fat body plays an essential role in detoxification processes in insects [85]. In addition, although the detoxification pathway that occurs in the carcass, including the epidermis, has not been well characterized, the CYP gene involved in oxidative stress responses was also found to be abundantly expressed in the carcass or epidermis of A. cerana [86]. These studies support the findings of the present study that genes encoding antioxidant enzymes were more induced in the gut, fat body, and carcass than in the head and thorax (Fig 5). Similar to CAT and SOD1, the induction ratios of CYP9Q1, CYP9Q2, and CYP9Q3 varied depending on the body parts or pesticides; however, they generally exhibited higher expression in the gut, fat body, and carcass than in the head and thorax (Fig 5). CYP enzymes contribute to honey bee detoxification [87]. In particular, CYP9Q1, CYP9Q2, and CYP9Q3 were recognized to efficiently metabolize fluvalinate, a typical pyrethroid pesticide that has been widely used as an acaricide for mite control in honey bee colonies [45]. In addition, transgenic Drosophila lines artificially expressing honey bee CYP9Q3 exhibited significant resistance to thiacloprid compared to the control flies [44], suggesting that CYP9Q subfamily members contribute significantly to honey bee xenobiotic detoxification and pesticide tolerance. However, the expression of CYP9Qs did not show significant trends across pesticides or body parts of honey bees in this study (Fig 5). According to a previous study comparing transcript levels of CYP9Q1, CYP9Q2, and CYP9Q3 in honey bees after three pyrethroid treatments, CYP9Q2 transcripts were increased by the three pyrethroids. However, tau-fluvalinate and cypermethrin repressed the expression of CYP9Q1, whereas they enhanced the expression of CYP9Q3. In contrast, bifenthrin did not induce the expression of CYP9Q3 but induced the transcription of CYP9Q1 [45]. While insecticides belonging to the same family have different effects on the expression of the three CYP9Qs [45], the same expression pattern of CYP9Qs cannot be expected in honey bees exposed to insecticides belonging to different families. According to the expression profiles of AChE1 in the five body parts, AChE1 also did not exhibit significant patterns for different body parts or pesticides (Fig 5). According to previous studies, honey bees possess two different AChEs, AChE1 and AChE2; AChE1 is soluble and shows little enzymatic activity in non-neuronal tissues, whereas AChE2 is a membrane-anchored form with high catalytic activity in neuronal tissues [42, 88]. In the kinetic analysis of in vitro expressed AChE1 and AChE2, AChE1 reduced the inhibition of AChE2 by OP and CB insecticides, suggesting a physiological function of AChE1 as a bio-scavenger that provides chemical defense in honey bees [42]. The role of soluble AChE as a chemical defense has also been observed in nematodes [89] and Drosophila [90]. In particular, the expression of soluble AChE was statistically induced by dichlorvos treatment in D. melanogaster [90], further suggesting that soluble AChE exerts a chemical defense effect against xenobiotics. In contrast to these previous studies [42, 89, 90] showing the putative function of soluble AChE associated with defense against OP or CB pesticides, a more recent study revealed that imidacloprid and fluvalinate did not induce the expression of AChE1 at the transcriptional or protein levels in the head and abdomen of honey bees [41]. In this study, AChE1 was not induced by acetamiprid, fenitrothion, amitraz, and bifenthrin in all body parts. In contrast, imidacloprid induced AChE1 expression levels in the head and gut, while carbaryl affected expression levels of AChE1 in the head and carcass. In the fat body, the level of AChE1 was higher than control after flupyradifurone exposure (Fig 5). This result indicates that the expression variability of AChE1 depends on different body parts and pesticide treatments. In conclusion, the expression of genes putatively involved in the detoxification mechanism did not exhibit significantly different expression patterns across different body parts or pesticides in honey bees. Given the high probability that honey bees are exposed to pesticides by their feeding activities [91, 92], gut and fat bodies were suggested to be optimal body parts to investigate the damage to honey bees by pesticide exposure based on determining the expression of genes, as molecular markers associated with detoxification metabolism. In particular, SOD1 in the head and the fat body was significantly induced by seven pesticides, suggesting that SOD1 could be a candidate molecular marker. However, SOD1 is a typical antioxidant enzyme, and oxidative stress is generated by various stressors, such as chemicals [79–81], heavy metals [50], flight, and age [93] in honey bees; therefore, caution is needed when using SOD1 as a molecular marker to investigate the damage caused by pesticides.
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PMC9648782 | Marina Kashiwagi,Kazumasa Nakao,Shigeki Yamanaka,Ichiro Yamauchi,Takafumi Yamashita,Toshihito Fujii,Yohei Ueda,Mariko Yamamoto Kawai,Takuma Watanabe,Shizuko Fukuhara,Kazuhisa Bessho | Circulatory C-type natriuretic peptide reduces mucopolysaccharidosis-associated craniofacial hypoplasia in vivo | 10-11-2022 | Skeletal alterations in the head and neck region, such as midfacial hypoplasia, foramen magnum stenosis and spinal canal stenosis, are commonly observed in patients with mucopolysaccharidosis (MPS). However, enzyme replacement therapy (ERT), one of the major treatment approaches for MPS, shows limited efficacy for skeletal conditions. In this study, we analysed the craniofacial morphology of mice with MPS type VII, and investigated the underlying mechanisms promoting jaw deformities in these animals. Furthermore, we investigated the effects of C-type natriuretic peptide (CNP), a potent endochondral ossification promoter, on growth impairment of the craniofacial region in MPS VII mice when administered alone or in combination with ERT. MPS VII mice exhibited midfacial hypoplasia caused by impaired endochondral ossification, and histological analysis revealed increased number of swelling cells in the resting zone of the spheno-occipital synchondrosis (SOS), an important growth centre for craniomaxillofacial skeletogenesis. We crossed MPS VII mice with transgenic mice in which CNP was expressed in the liver under the control of the human serum amyloid-P component promoter, resulting in elevated levels of circulatory CNP. The maxillofacial morphological abnormalities associated with MPS VII were ameliorated by CNP expression, and further prevented by a combination of CNP and ERT. Histological analysis showed that ERT decreased the swelling cell number, and CNP treatment increased the width of the proliferative and hypertrophic zones of the SOS. Furthermore, the foramen magnum and spinal stenoses observed in MPS VII mice were significantly alleviated by CNP and ERT combination. These results demonstrate the therapeutic potential of CNP, which can be used to enhance ERT outcome for MPS VII-associated head and neck abnormalities. | Circulatory C-type natriuretic peptide reduces mucopolysaccharidosis-associated craniofacial hypoplasia in vivo
Skeletal alterations in the head and neck region, such as midfacial hypoplasia, foramen magnum stenosis and spinal canal stenosis, are commonly observed in patients with mucopolysaccharidosis (MPS). However, enzyme replacement therapy (ERT), one of the major treatment approaches for MPS, shows limited efficacy for skeletal conditions. In this study, we analysed the craniofacial morphology of mice with MPS type VII, and investigated the underlying mechanisms promoting jaw deformities in these animals. Furthermore, we investigated the effects of C-type natriuretic peptide (CNP), a potent endochondral ossification promoter, on growth impairment of the craniofacial region in MPS VII mice when administered alone or in combination with ERT. MPS VII mice exhibited midfacial hypoplasia caused by impaired endochondral ossification, and histological analysis revealed increased number of swelling cells in the resting zone of the spheno-occipital synchondrosis (SOS), an important growth centre for craniomaxillofacial skeletogenesis. We crossed MPS VII mice with transgenic mice in which CNP was expressed in the liver under the control of the human serum amyloid-P component promoter, resulting in elevated levels of circulatory CNP. The maxillofacial morphological abnormalities associated with MPS VII were ameliorated by CNP expression, and further prevented by a combination of CNP and ERT. Histological analysis showed that ERT decreased the swelling cell number, and CNP treatment increased the width of the proliferative and hypertrophic zones of the SOS. Furthermore, the foramen magnum and spinal stenoses observed in MPS VII mice were significantly alleviated by CNP and ERT combination. These results demonstrate the therapeutic potential of CNP, which can be used to enhance ERT outcome for MPS VII-associated head and neck abnormalities.
Mucopolysaccharidosis (MPS) is an umbrella term for a group of progressive diseases caused by the impaired activity of specific lysosomal enzymes required for glycosaminoglycan degradation, and is clinically characterised by deafness, joint stiffness, obstructive airway disease, valvular disease, mental retardation and other manifestations [1, 2]. The estimated overall incidence of MPS is higher than 1:25,000 live births [3], and the life expectancy is dependent on the severity of the disease, but MPS patients may survive up to their 50s or 60s [4]. Craniofacial morphology is distinguished by a flat face, a depressed nasal bridge [5] and foramen magnum stenosis, which can lead to neurological symptoms [6, 7]. MPS is primarily treated with haematopoietic stem cell transplantation (HSCT) and enzyme replacement therapy (ERT) [8]. In HSCT, patients under the age of 24 months show significantly better development, and the donor is preferred to be a human leukocyte antigen (HLA) identical sibling [9]. GAG-mediated damage might be difficult to revert by ERT once it has occurred in the cranial bone and spine [10]. The efficacy of gene therapy combined with the ex vivo lentiviral modification of haematopoietic stem and progenitor cells in MPS I mice and humans has been reported, and with this method, it is possible to achieve higher levels of enzyme expression. However, this treatment strategy is associated with the risk of tumorigenesis as well as other conditions [11]. C-type natriuretic peptide (CNP), a member of the natriuretic peptide family, promotes bone growth and exerts its biological actions through guanylyl cyclase-B (GC-B) [12]. We previously reported that the CNP/GC-B system is a potent stimulator of endochondral bone growth and that CNP is a potent stimulator of the craniofacial region [13]. Based on these findings, we hypothesised that the CNP/GC-B system represents a novel therapeutic target for craniofacial hypoplasia in MPS VII. In this study, we analysed the head and neck morphology of MPS type VII model mice and then the effects of CNP and its co-treatment with ERT in these mice.
Animal care and experiments were conducted in a facility of the Graduate School of Medicine, Kyoto University, and in accordance with institutional guidelines. All experimental procedures involving animals were approved by the Animal Research Committee, Kyoto University Graduate School of Medicine (permit number: Med Kyo 21268). All animals in this study were sacrificed using carbon dioxide gas. The experiments were conducted in accordance with ARRIVE guidelines. The checklist is provided in S1 File. MPS VII model mice (Gusbmps-2J mice) were purchased from Jackson Laboratory (Bar Harbour, ME, USA). We only used homozygous mice with less than 1% of the normal levels of GUSB activity [14]. The genetic background of this strain was C57BL/6. Mice expressing CNP in the liver under the control of the human SAP–component promoter (SAP-Nppc-Tg mice) were generated on a C57BL/B6 background. Littermate of these mice had plasma CNP concentrations 84% higher than those of wild type (WT) mice as determined by radioimmunoassay measurements [15]. In a previous study, plasma CNP levels were approximately four-fold higher in 6-week-old SAP-Nppc-Tg mice than in WT mice [16]. Moreover, these mice, in which endochondral ossification was stimulated systemically, exhibited no hypotension or reduced heart weight as observed for mice with higher blood CNP levels [15]. Therefore, it is considered to be an appropriate in therapeutic effect and side effects. MPS VII mice with elevated circulatory CNP levels (MPS VII/SAP-Nppc-Tg mice) were created by crossing MPS VII and SAP-Nppc-Tg mice. The male and female mice show similar symptoms, only male mice were used as representative specimens in this study.
The pLIVE-Empty vector was purchased from Mirus Bio (Madison, WI, USA). The pLIVE-Gusb vectors were constructed as previously described [19].
Hydrodynamic injection enables gene transfer by the rapid injection of plasmid DNA through the tail vein of mice [17, 18]. Six-week-old mice were injected with plasmid DNA using a hydrodynamic injection-based procedure. The vectors were fixed at 100 μg for the pLIVE-Gusb vector, and the pLIVE-Empty vector was mixed to 125 μg as the total amount. As controls, C57BL/6 mice injected with 125 μg of pLIVE-Empty vector (WT mice), Gusbmps-2J/Gusbmps-2J homozygous mice injected with 125 μg of pLIVE-Empty vector (MPS VII mice), Gusbmps-2J/Gusbmps-2J homozygous mice injected with 100 μg of pLIVE-Gusb vector and 25 μg of pLIVE-Empty vector (MPS VII-GUSB mice), Gusbmps-2J/SAP-Nppc-Tg mice injected with 125 μg of pLIVE-Empty vector (MPS VII/SAP-CNP mice) and Gusbmps-2J/SAP-Nppc-Tg mice injected with 100 μg of pLIVE-Gusb vector and 25 μg of pLIVE-Empty vector (MPS VII-GUSB/SAP-CNP mice) were used for this study; the analysis was performed by dividing the animals into 5 groups. We previously demonstrated that this injection protocol has no significant effect on measured parameters, such as nasal-anal length, nasal tail length, or weights in C57BL/6 WT and WT mice [19]. Further, we previously evaluated GAG content in male mice 4 weeks after injection to validate whether the introduction of the pLIVE-Gusb vector induced sufficient GUSB activity and revealed that GUSB overexpression reduced liver GAG content [19]. The mice were sacrificed and analysed at 12 weeks of age as described previously [20].
As previously reported [21], skull imaging and μCT data analysis were conducted according to linear measurements, Euclidean distance matrix analysis (EDMA), and the size of the foramen magnum was measured using the ImageJ software (National Institutes of Health).
Every week, from 6 to 12 weeks of age, the body weight, naso-anal length and naso-tail length were measured under isoflurane anaesthesia.
Organ cultures of 1-week-old mouse cranial bases treated with vehicle, 10−6 M CNP, or 1.5×10−4 M GUSB or both for 6 days were used to evaluate the spheno-occipital synchondrosis (SOS) as previously described [20, 22]; the GUSB concentration was determined in a previous study [23].
Histological analysis was conducted as previously described [20].
Data are presented as the mean and standard deviations. Statistical analysis was performed using analysis of variance (ANOVA) with P < 0.05 considered statistically significant.
As previously described [24], MPS VII mice exhibited dwarfism and short limb bones, with gross morphologies and soft X-ray images (Fig 1A and 1B). MPS VII-GUSB/SAP-CNP mice had the largest body length among the various treatment groups (Fig 1C and 1D). As demonstrated by the growth curves, naso-anal and naso-tail lengths were significantly decreased in MPS VII mice in all treatment groups, slightly increased in MPS VII-GUSB mice and highly increased in MPS VIIsapCNP-Empty mice, and the lengths in MPS VII-GUSB/SAP-CNP mice were significantly longer than those in mice in all other injection groups at 12 weeks of age (Fig 1E and 1F). The body weights of MPS VII/SAP-CNP and MPS VII-GUSB/SAP-CNP mice were significantly higher than those of MPS VII mice at 12 weeks of age (Fig 1G). Morphometric analysis of the crania was performed using μCT images (Fig 2A). Linear measurements revealed that the skull, nasal bone, nose and upper jaw lengths in MPS VII mice were significantly shorter than those in WT mice (Fig 2B). Conversely, inner canthal distances were significantly larger in the MPS VII crania than in the WT crania (Fig 2B). Furthermore, EDMA revealed that the nasal, premaxilla, maxilla and frontal bones were markedly affected sagittally, resulting in hypoplasia in the MPS VII crania (Fig 2C). In contrast, the cranial width was larger in the MPS VII crania than in the WT crania (Fig 2C). Measurements of EDMA are in the S2 File. μCT imaging revealed that both the occipital and sphenoid bones that make up the skull base were significantly shorter in MPS VII mice than in WT mice (Fig 2D). In spinal canal measurements, μCT imaging also revealed that lengths from the posterior and inferior aspects of the body of cervical vertebra 2 to the inferior aspect of the arch of cervical vertebra 1 in MPS VII mice were significantly shorter than those in WT mice (Fig 2E). μCT images were used for skull morphometric analyses to evaluate the effects of GUSB, CNP, or both on MPS VII (Fig 3A). Linear measurements showed significantly longer skull, nasal bone, nose and upper jaw lengths in MPS VII-GUSB/SAP-CNP mice. The upper jaw length was also significantly increased in MPS VII-GUSB mice, while the skull, nose and upper jaw lengths were significantly increased in MPS VII/SAP-CNP mice (Fig 3B). EDMA revealed that the hypoplasia observed in the MPS VII skulls was slightly alleviated in the MPS VII-GUSB skulls, markedly alleviated in the MPS VII/SAP-CNP skulls and most notably alleviated in the MPS VII-GUSB/SAP-CNP skulls (Fig 3C). Furthermore, the sagittal length of sphenoid bones was significantly increased in MPS VII-GUSB/SAP-CNP mice, and the lengths of occipital bones were also increased in MPS VII/SAP-CNP and MPS VII-GUSB/SAP-CNP mice (Fig 3D). On μCT, sagittal thickness measurements of the SOS showed significant thinning in the presence of GUSB (Fig 3E). Spinal stenosis was significantly alleviated in MPS VII-GUSB mice and most alleviated in MPS VII-GUSB/SAP-CNP mice (Fig 3F).
To understand the mechanism underlying hypoplasia in MPS VII mice, we analysed the SOS and inter-sphenoid synchondrosis (ISS) of the cranial base, which are growth centres of craniofacial skeletogenesis and contribute to the longitudinal length of the cranial base [25, 26]. WT and MPS VII mouse skull base preparations of 2-week-old were stained with Alizarin red and Alcian blue in the horizontal positions and sagittal sections of 0, 1, 2 and 4-week-old mice were stained with Alcian blue/HE. Alizarin red and Alcian blue staining revealed that the skull bases of 2-week-old MPS VII mice had thicker synchondroses than those of WT mice (Fig 4A). ISS and SOS with swelling cells in the resting zone were observed in after 1-week-old MPS VII mice (Fig 4B and 4C). Alcian blue/HE-stained sagittal sections from 0-week-old mice showed no significant difference in the thickness of SOS and ISS between WT and MPS VII mice, but in 1-, 2- and 4-week-old mice, they were significantly thicker in MPS VII mice than in WT mice (Fig 4D and 4E). When MPS VII mice were compared with WT mice, the number of cells in the resting zone was significantly increased, the number of cells in the hypertrophic zone was significantly decreased and the overall cell number of the SOS was significantly increased in the MPS VII mice (Fig 4F–4I).
Despite hypogrowth of the midface in the sagittal direction in 12-week-old mouse skull bases, the SOS was thicker in MPS VII mice than in WT mice (Fig 4J). The GUSB treatment slightly alleviated the swelling cells, and the resting zone became thinner (Fig 4j and 4K). In MPS VII/SAP-CNP mice, the proliferative and hypertrophic zones became significantly thicker, but the resting zones did not become significantly thinner than those in MPS VII mice (Fig 4J–4L). When compared with those in MPS VII mice, the resting zones in MPS VII-GUSB/SAP-CNP mice became significantly thinner, while the proliferative and hypertrophic zones became significantly thicker (Fig 4J–4L).
To evaluate the combined effects of CNP and GUSB on SOS growth, we performed organ culture experiments using skull base explants from 1-week-old MPS VII mice and WT mice because the resting zone became significantly thicker and GAGs recognised the accumulation. Compared to WT explants, the vehicle-treated MPS VII explants showed swelling cells in the resting zone and increased SOS thickness after a 6-day culture but decreased cell numbers in the proliferative and hypertrophic zones. In addition, compared to vehicle-treated MPS VII explants, GUSB-treated MPS VII explants showed an increased number of cells in the proliferative and hypertrophic zones. CNP-treated MPS VII explants showed enlarged chondrocytes in the proliferative and hypertrophic zones, and CNP- and GUSB-treated MPS VII explants showed increased numbers of enlarged cells and increased cell numbers in the proliferative and hypertrophic zones, respectively (Fig 4M).
To investigate whether CNP and GUSB are effective treatments for foramen magnum stenosis observed in MPS VII, we compared this region in WT and MPS VII mouse skulls (Fig 5A). At 12 weeks of age, the foramen magnum was significantly smaller (–7.827%) in MPS VII skulls than in WT skulls (Fig 5B). The MPS VII-GUSB/SAP-CNP mice had the largest foramen magnum (Fig 5C and 5D), while MPS VII-GUSB or MPS VII/SAP-CNP mice did not differ significantly from MPS VII mice in foramen magnum.
The foramen magnum is formed by the exoccipital, supraoccipital and basioccipital bones and grows through endochondral ossification [27]. The anterior intraoccipital synchondrosis (AIOS) exists between the exoccipital and basioccipital bones, and the posterior intraoccipital synchondrosis (PIOS) exists between the exoccipital and supraoccipital bones. At 2 weeks of age, Alizarin red and Alcian blue staining revealed thicker AIOS and PIOS in MPS VII mice than in WT mice (Fig 5E). In Alcian blue/HE staining, MPS VII mice had thicker AIOS and PIOS, and their chondrocyte columns were not in order when compared to those in WT mice (Fig 5F and 5G). The skull base in 12-week-old mice showed that AIOS remained in MPS VII mice, but was closed in WT mice (Fig 5H).
In this study, we analysed the morphology of the head and neck region in MPS VII mice and found that MPS VII mice exhibit a characteristic craniofacial morphology, including midface hypoplasia, foramen magnum stenosis and spinal canal stenosis, similar to that observed in patients with MPS. Various mechanisms of skeletal growth inhibition in MPS have been reported. In patients with MPS, endochondral ossification at the growth plates fails, resulting in reduced chondrocyte proliferation and delayed differentiation from proliferation to hypertrophy in animal models [28]. Previous studies reported that the growth plates of MPS VII model mice with short stature were thicker than those of normal mice [29]. The increase in growth plate thickness was caused by an increase in the number of cells in the resting zone as well as an increase in chondrocyte size and spacing due to GAG storage. As previously reported [19], the resting zone in the tibial growth plates of MPS model mice contained swelling cells. Swelling cells in the resting zone may have had impaired function, and the number of chondrocytes from the resting zone may have been reduced, resulting in growth inhibition. Furthermore, a disorganised structure in the hypertrophic zone was observed, in addition to an abnormal arrangement of the primary trabecular bone and an increased presence of cartilage within the woven bone, suggesting difficulties in cartilage resorption during endochondral ossification in MPS model mice [30]. On the other hand, increased chondrocyte apoptosis in MPS was revealed, and chondrocyte culture experiments of MPS may compensate for the increased chondrocyte apoptosis and increased cell proliferation. The reason for the significant bone abnormalities observed despite this compensation mechanism is that immunostaining revealed that the level of osteonectin, a marker of mature chondrocytes, is significantly reduced in the tibial growth plate of MPS animals, suggesting a defect in bone production due to a reduction in hypertrophic chondrocytes available for mineralisation into bone [31]. This study revealed that the mechanisms of inhibition of long bone growth and midface hypoplasia are similar. Histological analysis revealed that MPS VII model mice had thicker SOS and ISS, which are important for the midfacial sagittal growth given that the columns of chondrocytes are oriented roughly parallel to the longitudinal axis of the basicranium. After one week, swelling was observed in the resting zone, and the number of cells in the hypertrophic zone was lower than that in WT mice. This suggests that functional resting zone cells are not being delivered to other zones in the SOS and ISS as well as long bones. In this rescue experiment involving crossbreeding of MPS VII and SAP-Nppc-Tg mice and the vector administration, MPS VII-GUSB mice showed slight alleviation, MPS VII/SAP-CNP mice showed moderate alleviation and MPS VII-GUSB/SAP-CNP mice showed the most remarkable alleviation of maxillofacial morphological abnormalities. This suggests that the co-treatment of CNP and ERT was the most effective treatment. Histology and organ cultures suggest that GUSB decreased the swelling of the resting zone and increased the number of cells in proliferative and hypertrophic zones but did not increase the width of the proliferative and hypertrophic zones sufficiently. CNP increased the number of enlarged cells in proliferative and hypertrophic zones, as did GUSB and CNP treatment. In addition, we previously revealed that GUSB therapy significantly increased Npr2 mRNA levels [19]; this study also showed that the co-treatment of ERT and CNP may have synergistic effects. We previously reported that MPS VII mice with elevated blood levels of CNP from 6 weeks of age failed to show improvements in short stature [19]; however, in the present study, elevated blood levels of CNP from birth predominantly alleviated short stature in MPS VII mice. This difference is due to CNP expression timing, and it is considered that CNP acts effectively at birth when GAG accumulation is low, and CNP may become resistant to GAG accumulation. This suggests that early CNP treatment could be used to treat skeletal abnormalities associated with MPS VII. A previous study reported that in MPS type II patients who were treated with ERT at 3 months of age, the only physical sign of the disease was a mild deformity of one vertebra, and they did not exhibit short stature or coarse facial features after 3 years of treatment [32]; however, in this study, MPS VII-GUSB mice did not show sufficient improvement in short stature or midface hypoplasia. This may be due to the late timing of ERT and the fact that MPS VII mice exhibit more severe skeletal abnormalities than mice with other MPS types [33]. Spinal canal stenosis and foramen magnum stenosis are characteristic findings of patients with MPS [34, 35], and MPS VII model mice showed spinal canal and foramen magnum stenosis in this study. In terms of foramen magnum stenosis, Alizarin red and Alcian blue staining of the skull base in 12-week-old mice revealed that the AIOS was still present in MPS VII mice but was closed in WT mice; this delayed ossification may have contributed to foramen magnum stenosis. Furthermore, combined treatment with GUSB and CNP alleviated the narrowed spinal canal and foramen magnum stenosis in MPS VII mice. In conclusion, CNP may be effective, while a combination of GUSB and CNP may be more effective, in treating impaired skeletogenesis in patients with MPS, including not only short stature but also craniofacial hypoplasia, narrowing of the foramen magnum and spinal canal stenosis.
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PMC9648792 | 36315446 | Su-Chi Ku,Hsin-Liang Liu,Che-Yu Su,I-Jeng Yeh,Meng-Chi Yen,Gangga Anuraga,Hoang Dang Khoa Ta,Chung-Chieh Chiao,Do Thi Minh Xuan,Fidelia Berenice Prayugo,Wei-Jan Wang,Chih-Yang Wang | Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer | 25-10-2022 | cadherin,bioinformatics,prognosis,breast cancer,therapeutic targets | Breast cancer is one of the leading deaths in all kinds of malignancies; therefore, it is important for early detection. At the primary tumor site, tumor cells could take on mesenchymal properties, termed the epithelial-to-mesenchymal transition (EMT). This process is partly regulated by members of the cadherin (CDH) family of genes, and it is an essential step in the formation of metastases. There has been a lot of study of the roles of some of the CDH family genes in cancer; however, a holistic approach examining the roles of distinct CDH family genes in the development of breast cancer remains largely unexplored. In the present study, we used a bioinformatics approach to examine expression profiles of CDH family genes using the Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), cBioPortal, MetaCore, and Tumor IMmune Estimation Resource (TIMER) platforms. We revealed that CDH1/2/4/11/12/13 messenger (m)RNA levels are overexpressed in breast cancer cells compared to normal cells and were correlated with poor prognoses in breast cancer patients’ distant metastasis-free survival. An enrichment analysis showed that high expressions of CDH1/2/4/11/12/13 were significantly correlated with cell adhesion, the extracellular matrix remodeling process, the EMT, WNT/beta-catenin, and interleukin-mediated immune responses. Collectively, CDH1/2/4/11/12/13 are thought to be potential biomarkers for breast cancer progression and metastasis. | Comprehensive analysis of prognostic significance of cadherin (CDH) gene family in breast cancer
Breast cancer is one of the leading deaths in all kinds of malignancies; therefore, it is important for early detection. At the primary tumor site, tumor cells could take on mesenchymal properties, termed the epithelial-to-mesenchymal transition (EMT). This process is partly regulated by members of the cadherin (CDH) family of genes, and it is an essential step in the formation of metastases. There has been a lot of study of the roles of some of the CDH family genes in cancer; however, a holistic approach examining the roles of distinct CDH family genes in the development of breast cancer remains largely unexplored. In the present study, we used a bioinformatics approach to examine expression profiles of CDH family genes using the Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), cBioPortal, MetaCore, and Tumor IMmune Estimation Resource (TIMER) platforms. We revealed that CDH1/2/4/11/12/13 messenger (m)RNA levels are overexpressed in breast cancer cells compared to normal cells and were correlated with poor prognoses in breast cancer patients’ distant metastasis-free survival. An enrichment analysis showed that high expressions of CDH1/2/4/11/12/13 were significantly correlated with cell adhesion, the extracellular matrix remodeling process, the EMT, WNT/beta-catenin, and interleukin-mediated immune responses. Collectively, CDH1/2/4/11/12/13 are thought to be potential biomarkers for breast cancer progression and metastasis.
Breast cancer is one of the most common malignancies among women and the second leading cause of death after lung cancer [1, 2]. The prognosis of breast cancer is better with early detection and improved treatment. Because of the poor prognosis of advanced breast cancer, research on breast cancer has recently focused on precise detection of invasion and metastasis with accurate tumorigenic biomarkers [3–9]. Despite progress in developing diagnostic screening tools, distant metastases at the time of diagnosis indicates a worse prognosis with only 23% of patients surviving 5 years post-diagnosis [10]. Therefore, novel research on genetic alterations and signal transduction pathways is playing important roles in both early breast cancer detection and treatment in advanced stages [11–13]. The cadherins (CDHs) are a superfamily of calcium-dependent adhesion molecules which have functions in cell recognition, tissue morphogenesis, and tumor suppression [14, 15]. The CDH family consists of 23 members, from CDH1 to CDH26, as documented in the GeneCards database [16]. Basic characteristics of the CDH gene family, including gene IDs and aliases, are presented in Table 1. Classic cadherins have mostly been thoroughly studied, including epithelial (E)-cadherin (CDH1), neural (N)-cadherin (CDH2), placental (P)-cadherin (CDH3), and retinal (R)-cadherin (CDH4) [17]. It is widely accepted that the epithelial-to-mesenchymal transition (EMT) of epithelial cells results in strong cell-cell adhesion and more invasive features [18]. The EMT is essential for this phenomenon and is considered a promoter of metastasis, and metastatic processes associated with mesenchymal features are similar among various cancers such as advanced breast cancer. The EMT has also received a lot of interest in cancer research and is thought to be an important step in metastases [19, 20]. As a result, finding new molecules that can inhibit this mechanism is an important subject of scientific study. A feature of the EMT is in part a result of downregulation of CDH1 and parallel upregulation of other cadherins like CDH2, which plays an essential role during early invasion and metastasis [21]. Loss of CDH1 alone might be insufficient to induce the EMT [22]. Instead, CDH1 expression was observed in invasive lobular carcinomas (ILCs) and invasive ductal carcinomas (IDCs) [23]. Other cadherins and molecules such as β-catenin, which forms an important membrane complex, are often detached from the cell membrane and are translocated to the nucleus to induce EMT signaling events [24–26]. Previous studies reported the roles of cadherins in breast cancer. However, interactions and pathways among all CDH family members and related molecules in tumorigenesis are still unclear, and challenges remain in discovering suitable biomarkers for precision treatment and detection. The present study is the first study to perform a bioinformatics analysis of the entire CDH family in patients with breast cancer by analyzing several large online databases. A flowchart depicting the investigative strategies we utilized in this study, including expression levels, clinical survival, and functional enrichment analyses, of CDH family members in breast cancer is presented in Figure 1. First, original data were retrieved from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Second, differential expression levels were analyzed using the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. Third, Kaplan-Meier (KM) plots were utilized to reveal the significance of CDH family in the prognosis of breast cancer patients. Incorporating these results, we selected targeted genes due to higher expression levels and lower survival for further analysis. Then, the Cancer Cell Lines Encyclopedia (CCLE) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases were used to discover differences in expressions between breast cancer and normal tissues [27–31]. Afterwards, we used the MethSurv database to determine single CpG methylation expression patterns. In addition, we studied the gene potential thoroughly through a functional enrichment analysis and micro (mi)RNA-regulated networks, including biological processes (BPs), cellular components (CCs), molecular functions (MFs), signaling pathways, and potentially regulated miRNAs. Ultimately, we utilized the TIMER2.0 database to uncover correlations between CDH genes and immune cell markers in breast cancer. The flowchart is presented to offer insights into our comprehensive approach and possibly suggest a theoretical foundation for future research.
Oncomine (https://www.oncomine.com/) is an online database established to show information of gene expressions in major cancers compared to their respective normal samples [32]. In this study, individual expression levels of CDH family members in various cancers were obtained from the Oncomine database with p<0.05 and fold change (FC) defined as 1.5 [33–37].
The TIMER database was utilized to identify complements or regulatory factors that are upregulated or downregulated in tumor samples compared to normal tissues. To analyze differences in gene expressions of each CDH family member between breast cancer and normal tissues, differentially expressed genes (DEGs) in breast invasive carcinoma (BRCA) in TCGA dataset were identified via TIMER. The threshold |log2[FC]| was set to 1, and the value of q was 0.05. GEPIA2 (http://gepia2.cancer-pku.cn/#index) is a web platform that contains RNA sequencing (RNA-Seq) expression data from 9736 tumors and 8587 normal samples from TCGA and GTEx projects [38]. An independent t-test was used to calculate p values, and p<0.05 was considered statistically significant; Pr(>F) < 0.05 was based on Student’s t-test [39–44].
The KM plotter (http://kmplot.com/analysis/) contains 54,000 genes associated with survival in 21 types of cancer [45], including breast cancer samples (n=7830), which can be analyzed to examine the effects of CDH gene family members on survival times of patients with breast cancer. Results are presented by plotting the survival curve and hazard ratios (HRs) with 95% confidence intervals (CIs) and log-rank p values [46]. To assess the prognosis of breast cancer patients, distant metastasis-free survival (DMFS) was applied to evaluate the survival of advanced breast cancer patients.
The cBioPortal (http://www.cbioportal.org/) is an open platform providing large-scale visualization, analysis, and downloading of cancer genomic datasets for various types of cancer [47, 48]. Cancer genome profiles can be obtained by a portal query interface, allowing researchers to explore and compare genetic alterations across samples. This study used the cBioPortal to explore alterations, correlations, and networks of the CDH gene family. CDH family protein expressions were evaluated by the Human Protein Atlas (HPA) platform. HPA contains images of pathologic tissues labeled with antibodies in conjunction with 11,250 human proteins. Microarrays include sections from forty-six normal tissues and more than twenty types of human cancers [49–51]. This study used the HPA to obtain the intensities of labeled antibodies in pathologic malignant tissues. Bar charts represent the quantification of four classifications, “negative”, “weak”, “moderate”, and “strong”, of IHC staining intensities in breast cancer samples with different antibodies.
“Breast cancer gene-expression miner” (bc-GenExMiner), which contains published annotated breast cancer transcriptomic data (DNA microarrays [n=11,359] and RNA-Seq [n=4421]), is a breast cancer-associated web portal (http://bcgenex.ico.unicancer.fr) that conducts several differential gene expression analyses. We obtained data from Affymetrix® median probe data. To evaluate the difference in a gene’s expression among different groups, Welch’s test was used. Moreover, Dunnett-Tukey-Kramer’s test was used for two-by-two comparisons (allowing determination of the significance levels but not giving a precise p value) when there were more than three different groups and Welch’s p value was significant. Variant corresponding clinical or pathological data is contained in bc-GenExMiner version 4.5, which stresses that the Expression Module can be utilized for both exploratory and validation purposes [52].
Over 1100 cell lines among 37 cancer types are contained in the CCLE database (https://portals.broadinstitute.org/ccle). The CCLE dataset provides extensive genomic data, computational analyses, and visualization [53]. For the present study, we used the CCLE dataset to investigate messenger (m)RNA expression levels of CDH family members to further verify their participation in cancer cell lines [54–57].
The MethSurv (https://biit.cs.ut.ee/methsurv/) database was utilized to determine single CpG methylation expression patterns and establish a heatmap of the different DNA methylated regions [58]. DNA methylation values are presented as beta values (ranging from 0 to 1). We used the formula of M / (M + U + 100) to calculate each single methylation of CpG, where M and U respectively represent methylated and unmethylated intensity values.
The METABRIC and TCGA datasets in the cBioPortal database were accessed for functional enrichment analyses [59, 60]. There were two parts of the MetaCore analysis (https://portal.genego.com). The first part was to find overlapping genes coexpressed in the two datasets with Venny version 2.1. The second part was to uncover BPs, disease biomarker networks, breast neoplasm signaling pathways, and drug target networks [61–65]. Moreover, a gene ontology (GO) analysis was implemented to discover the functional significance of genes with BPs, MFs, CCs, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) with p values of <0.05 indicating statistical significance [66–70]. Next, we used the median expression of targeted genes and then performed a differential analysis with an algorithm in the “DESeq2” package in R/Bioconductor. After the differential analysis, results were utilized for the gene set enrichment analysis (GSEA) with the Hallmark database [71–73]. Then, we used the “fgsea” packages in R Studio software to evaluate enriched pathways in transcriptional data by the GSEA, and online platform (http://www.bioinformatics.com.cn/) and used “SRplot” for visualization (http://www.bioinformatics.com.cn/srplot) [74, 75]. The level of statistical significance was presented via p values, and a normalized enrichment score (NES) reflected the rank of gene classes. In addition, the gene potential of the CDH family was conducted using the miRWalk database (http://mirwalk.umm.uni-heidelberg.de/) to investigate the regulatory potential of miRNAs and to analyze regulated pathways and networks by an Ingenuity Pathway Analysis (IPA) [76–79].
The TIMER web server was accessed for a Cox regression analysis [80, 81]. We used the “Survival” module to explore the clinical significance of covariates in a multivariable Cox proportional hazard model. Clinical factors such as age, gender, ethnicity, and tumor stage and gene expression were covariates in the analysis. TIMER presents Cox regression results including hazard ratios (HRs) and statistical significance. For outputs of the Cox model, Surv(CancerType)~variables is the formula of the user-defined Cox regression model, which is fitted by the function coxph() from the R package ‘survival’. In the results, the coefficient reads as a regression coefficient. The 95% confidence intervals (CIs) are shown.
The present study is based on open-source data. Users could download relevant data in public databases for research.
To understand differences in expressions between breast cancer and normal tissues, all 24 CDH family members were investigated in the Oncomine database (Figure 2A, 2B). Findings of this database revealed that at the transcriptional level, CDH1/2/4/6/7/11/12/13/15/22/23/24 were overexpressed in breast cancer samples compared to normal tissues, while transcriptional levels of CDH1/3/5/8/9/10/16/17/18/19/20/26/28 were downregulated compared to normal tissues. In addition, complement expressions were explored across TCGA database via the TIMER database (Figure 3A). We investigated expression levels of CDH family members in breast cancer cell lines using the CCLE database as well (Figure 3B). Results revealed that expression levels were upregulated or downregulated in BRCA samples compared to non-tumor samples. Compared to normal tissues, expression levels of CDH2/3/4/5/6/7/8/10/11/12/13/15/17/19/20/22/23/24/26 were significantly higher in BRCA tissues. In contrast, expression levels of CDH1/18 were significantly lower in BRCA tissues. The molecular subtypes of cell lines are also shown in Supplementary Figure 1. CDH1 and CDH7 were highly expressed in luminal A cell lines; CDH5 was mostly expressed in human epidermal growth factor receptor-2 (HER2) cell lines; CDH2/3/4/6/11/12/13/15/18/19/22/23 showed high expressions in multiple triple-negative breast cancer cell lines; and other CDH genes showed no specific expressions in molecular subtypes of breast cancer cell lines.
The impact of the entire CDH family on breast cancer survival was evaluated through the KM plotter database. Distant metastasis-free survival (DMFS) was analyzed due to its significance in clinical prognosis of advanced breast cancer. Results demonstrated that most CDH family genes were associated with the prognosis of BRCA patients including CDH1/2/3/4/5/7/9/10/11/12/13/15/16/19/26 (Figure 4 and Table 2). High expression levels of CDH1 (HR=1.32, 95% CI=1.13~1.55, p=0.0058), CDH2 (HR=1.39, 95% CI=1.17~1.64, p=0.00012), CDH3 (HR=1.55, 95% CI=1.32~1.82, p=6.4e-8), CDH4 (HR=1.27, 95% CI=1.08~1.5, p=0.0036), CDH7 (HR=1.34, 95% CI=1.14~1.58, p=0.00048), CDH9 (HR=1.21, 95% CI=1.03~1.43, p=0.02), CDH10 (HR=1.34, 95% CI=1.13~1.58, p=0.00059), CDH11 (HR=1.42, 95% CI=1.04~1.96, p=0.028), CDH12 (HR=1.21, 95% CI=1.03~1.41, p=0.019), CDH13 (HR=1.31, 95% CI=1.12~1.54, p=0.00089), CDH15 (HR=1.22, 95% CI=1.03~1.44, p=0.023), CDH16 (HR=1.28, 95% CI=1.09~1.5, p=0.003), and CDH26 (HR=1.68, 95% CI=1.28~2.19, p=0.00012) were correlated with poorer DMFS in BRCA patients. On the other hand, high expressions of CDH5 (HR=0.84, 95% CI=0.71~0.98, p=0.031) and CDH19 (HR=0.71, 95% CI =0.54~0.92, p=0.01) were associated with a good prognosis in BRCA patients. Other family members in the CDH family showed negative results. A univariate Cox regression analysis was conducted to validate our results from clinical breast cancer patients, data of which were obtained from the TIMER database. The univariate Cox regression demonstrated that high levels of CDH13 were an independent risk factor for poor overall survival (OS) (Supplementary Table 1A) in breast cancer patients. In addition, subtypes of breast cancer, including luminal, HER2, and basal, were analyzed. The luminal subtype showed no significance among CDHs (Supplementary Table 1B). CDH12 was a significant risk factor for poor OS in the HER2 subtype (Supplementary Table 1C). CDH11 and CDH12 were significant risk factors for poor OS in the basal subtype (Supplementary Table 1D). To further understand correlations of expression levels of CDH family members in breast cancer, some clinical and pathological factors were analyzed in specific genes among the CDH family. Among all CDH family members, CDH1/2/3/4/7/9/10/11/12/13/15/16/26 were significantly positively associated with a lower DMFS (Figure 4), and CDH1/2/4/6/7/11/12/13/15/22/23/24 mRNA expression levels were higher in breast cancer than in normal tissues in the Oncomine database (Figure 2A, 2B). Results demonstrated that the eight CDH1/2/4/7/11/12/13/15 genes simultaneously expressed significance in the gene database and clinical survival analysis. Therefore, in this study, these eight specific genes were further analyzed with an extensive database, clinical factors, and bioinformatics tools and were demonstrated to be potential biomarkers for breast cancer.
As CDH1/2/4/7/11/12/13/15 were positive in terms of both gene expressions and with the KM survival analysis, immunohistochemical (IHC) patterns from the HPA were utilized to validate clinical applications by pathology (Figure 5A, 5B). CDH1, CDH2, and CDH12 exhibited strong intensities in cell nuclei in breast cancer samples. Otherwise, other members of the CDH family showed negative or weak intensities in pathological samples. The relative staining intensities of CDH1 were negative (two cases), moderate (one case), and strong (eight cases) in breast cancer samples. CDH2 staining intensities were negative (nine cases), weak (three cases), and strong (one case) in breast cancer samples. CDH12 staining intensities were negative (one case), weak (three cases), moderate (one case), and strong (seven cases) in breast cancer samples. There was no IHC pattern for CDH4 obtained from the HPA. Other staining intensities of CDH family members are shown in Figure 5A, 5B. CDH staining expressions, magnification in 4x, were displayed among CDH family except CDH13 and CDH15 due to negative intensities. Correlations of expression levels of CDH1/2/4/7/11/12/13/15 with pathological stages of breast cancer are shown in violin plots in Figure 6A. mRNA levels of CDH1/11/13 were relatively high in breast cancer patients classified as stage IV with metastasis but without statistical significance. Scarff-Bloom-Richardson (SBR) grading is a clinical prognostic predictor associated with cell proliferation and an indicator of the response to chemotherapy (Figure 6B). A determination of an association between the SBR grade and responsiveness would be clinically useful [82]. SBR1 indicates good differentiation, SBR2 moderate differentiation, and SBR3 poor differentiation. Figure 6B demonstrates that CDH1, CDH2, and CDH11 with the poorest prognoses were assigned to grade SBR3. The Nottingham prognostic index (NPI) is used to predict a prognosis after breast cancer surgery, and is calculated by three pathological factors: the tumor size, the number of involved lymph nodes, and the tumor grade (Figure 6C). Values are used to define three subsets of patients with different survival chances of breast cancer: 1) good prognosis, comprising 29% of patients with an 80% chance of 15-year survival; 2) moderate prognosis, 54% of patients with a 42% chance of 15-year survival; and 3) poor prognosis, 17% of patients with a 13% chance of 15-year survival [83]. The NPI can also be used to evaluate the effect of adjuvant treatment like chemotherapy or radiotherapy. Figure 6C demonstrates that only CDH1 expression was correlated with higher NPI values, indicating poor prognoses in patients with CDH1 gene expression. Otherwise, CDH11 and CDH13 expressions demonstrated lower NPI values with better prognoses. Other CDH family members showed no significance. Other clinical predictors were also analyzed in terms of CDH family gene expressions in breast cancer (Supplementary Figure 2 in Supplementary Materials). Estrogen receptor (ER)/progesterone receptor (PR)-positive samples showed a high probability of positive effects of hormone therapy such as with tamoxifen. HER2 samples corresponded to positive effects of targeted therapy with trastuzumab. Subtypes of breast cancer including basal-like, HER2-E, luminal A, and luminal B were correlated with different pathological characteristics and clinical prognoses. Mutations of breast cancer gene-1 (BRCA1) and BRCA2 were also analyzed with respect to CDH family gene expressions, which represent breast cancer oncogenes (Supplementary Figure 2D). Supplementary Figure 2A demonstrates that ER-/PR- expressions were correlated with higher expressions of CDH2 and CDH11, indicating a poorer response to hormone therapy. Supplementary Figure 2B demonstrates that HER2-negative expression was found to be associated with CDH7 and CDH11, suggesting a poorer response to targeted therapy. Supplementary Figure 2C demonstrates relationships of different subtypes of breast invasive carcinoma with CDH family gene expressions. CDH1 was highly expressed by the HER2-E and luminal B types; CDH2 was highly expressed by the HER2-E type; CDH4 was highly expressed by the basal-like type; CDH7 and CDH11 were highly expressed by the luminal A type; CDH12 and CDH13 were highly expressed by the basal-like and luminal A types; and CDH15 expression was significantly associated with no types.
Genomic changes in the CDH family were analyzed via the cBioPortal database, which demonstrated changes in CDH1 (14%), CDH2 (6%), CDH4 (11%), CDH7 (5%), CDH11 (5%), CDH12 (7%), CDH13 (4%), and CDH15 (4%) (Figure 7A). Our results of mutated gene frequencies demonstrated that those of CDH1 and CDH4 were >10%. Altered genes at higher frequencies affect signaling pathways and cellular processes and can induce tumorigenesis based on previous studies [84–86]. CDH1 showed more gene alterations of truncating mutations and deep deletions, and low mRNA expression, while in contrast, CDH4 showed more amplifications and high mRNA expression. In the METABRIC dataset, CDH1 acted more like a TSG, and CDH4 acted like an oncogene in breast cancer. A previous study demonstrated that loss of E-cadherin was a key hallmark of ILCs [87]. In Giovanni et al. [86], CDH1 was one of the most recurrently mutated genes in breast cancer. In mixed ILC-IDC samples, genetic alterations of ILC tumors were found at a frequency of 14%. Mutations targeting CDH1 were mostly truncated mutations, and this result was similar to our mutation analysis. We also used Pearson’s correlations to calculate correlations between CDH family members based on mRNA expressions (Figure 7B). CDH11 was significantly positively correlated with CDH13. Other genes in the CDH family otherwise showed no relative correlations with each other (Figure 7B). In addition, a protein-protein interacting (PPI) network analysis of the CDH family was conducted via STRING at various transcription levels to investigate potential relationships. The STRING analysis revealed that linkages among CDH gene family members were complicated. Using a three-group k-means algorithm, it was found that the group consisting of CDH1, CDH13, and CDH15 had a close relationship, and CDH2, CDH4, and CDH11 comprised another related group. A third group consisted of CDH7 and CDH12 (Figure 7C).
We present a heatmap of DNA methylated locations of CDH1/2/4/11/12/13 in breast cancer in Supplementary Figure 3 in “Supplementary Materials”. In total, 18 methylated CpG sites were determined for CDH1, with six CpG sites presenting high expressions. Among them, cg26508465 and cg09220040 showed the highest levels of DNA methylation. In total, 20 methylated CpG sites of CDH2 were determined with six CpG sites presenting high expressions. Among them, cg24776465 showed the highest level of DNA methylation. In total, there were 26 methylated CpG sites of CDH11, with 19 CpG sites presenting high expressions. Among them, cg02724025 showed the highest level of DNA methylation. Over half of the CpG sites of CDH11 presented high levels of methylation and relevance to breast cancer. These results provide a potential mechanism by which CDH11 can serve as an oncogene for breast cancer.
To understand how DEG lists are linked to downstream CDH-regulated networks in various biological pathways and diseases, an enrichment analysis was performed using MetaCore software. After uploading genes coexpressed with CDH1 from Metabric and TCGA databases into MetaCore, we found that numerous pathways and networks were related to cell cycle (Figure 8 and Supplementary Table 2 in Supplementary Materials) including “Immune response_B cell antigen receptor (BCR) pathway”, “Oxidative stress ROS-induced cellular signaling”, “Development_negative regulation of WNT/Beta-catenin signaling in the cytoplasm”, and “Immune response_IFN-alpha/beta signaling via PI3K and NF-κB pathways”. Similar pathway analyses of CDH2, CDH4, CDH7, CDH11, CDH12, CDH13, and CDH15 are displayed in “Supplementary Materials” (Supplementary Figures 4–9 and Supplementary Tables 3–9). Genes coexpressed with CDH2 were correlated with “Cell adhesion_ECM remodeling” and “Cytoskeleton remodeling_Regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases” (Supplementary Figure 4). Genes coexpressed with CDH4 were correlated with “Protein folding and maturation POMC processing”, “Beta-catenin-dependent transcription regulation in colorectal cancer”, and “Cell adhesion_ECM remodeling” (Supplementary Figure 5). Genes coexpressed with CDH7 were correlated with “Cell cycle_Chromosome condensation in prometaphase” and “Cell cycle_the metaphase checkpoint” (Supplementary Figure 6). Genes coexpressed with CDH11 were correlated with “Cell adhesion_ECM remodeling”, “IL-1 beta-and endothelin-1-included fibroblast/myofibroblast migration and extracellular matrix production in asthmatic airways”, and “Development regulation of epithelial to mesenchymal transition (EMT)” (Supplementary Figure 7). Genes coexpressed with CDH12 were correlated with “Cytoskeleton remodeling_Regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases” and “Development negative regulation of WNT/Beta catenin signaling in the cytoplasm”. Genes coexpressed with CDH13 were correlated with “Development_Regulation of epithelial-to-mesenchymal transition (EMT)”, “Role of stellate cells in progression of pancreatic cancer”, and “Cell adhesion ECM remodeling” (Supplementary Figure 8). Genes coexpressed with CDH15 were correlated with “Transcription_HIF-1 targets”, “Oxidative stress_ROS-induced cellular signaling”, and “Development_negative regulation of WNT/Beta catenin signaling in the cytoplasm”. In summary, genes coexpressed with CDH11 and CDH13 were both correlated with regulation of the EMT, while genes coexpressed with CDH2, CDH4, CDH11, and CDH13 were all correlated with cell adhesion.
For comprehensive analysis, we obtained data from the METABRIC and TCGA datasets to acquire GO enrichment results including BPs, CCs, MFs, and KEGG (Supplementary Figures 9A–14A in Supplementary Materials). The BP analysis demonstrated that CDH1 was correlated with T-cell activation; the CC analysis showed correlations with cell-cell junctions and vacuolar membranes; MFs revealed significant relationships with phospholipid binding and actin binding, while KEGG ontology indicated the role of the mitogen-activated protein kinase (MAPK) signaling pathway and cytokine-cytokine receptor interactions (Supplementary Figure 9A). For CDH2, BPs demonstrated correlations with positive regulation of catabolic processes; the CC analysis showed correlations with mitochondrial matrix and cell-cell junctions; MFs revealed significant relationships with actin binding and protein serine/threonine kinase activity, while KEGG ontology indicated the role of the phosphatidylinositol 3-kinase (PI3K)-Akt signaling pathway (Supplementary Figure 10A). For CDH4, BPs demonstrated correlations with proteasomal protein catabolic process; the CC analysis showed correlations with the mitochondrial inner membrane and mitochondrial matrix; MFs revealed significant relationships with actin binding and ion channel activity, while KEGG ontology indicated the role of pathways of multiple neurodegenerative diseases (Supplementary Figure 11A). For CDH11, BPs demonstrated correlations with non-coding (nc)RNA metabolic processes; the CC analysis showed correlations with the mitochondrial inner membrane and mitochondrial matrix; MFs revealed significant relationships with transcription coregulator activity and actin binding, while KEGG ontology indicated the role of pathways of multiple neurodegenerative diseases (Supplementary Figure 12A). For CDH12, BPs demonstrated correlations with positive regulation of catabolic processes; the CC analysis showed correlations with cell-cell junctions and the mitochondrial matrix; MFs revealed significant relationships with phospholipid binding and actin binding, while KEGG ontology indicated the role of pathways of multiple neurodegenerative diseases (Supplementary Figure 13A). For CDH13, BPs demonstrated correlations with positive regulation of catabolic processes; the CC analysis showed correlations with cell-cell junctions and the mitochondrial matrix; MFs revealed significant relationships with transcription coregulator activity, while KEGG ontology indicated the role of neuroactive ligand-receptor interactions (Supplementary Figure 14A).
GSEA results indicated that the Hallmark pathway analysis of CDH1 was significantly associated with protein secretion, estrogen response_early, and mammalian target of rapamycin C1 (mTORC1) signaling (Supplementary Figure 9B in Supplementary Materials). Yet the EMT revealed negative correlations with CDH1. The Hallmark pathway analysis of CDH2 revealed that it was significantly associated with the EMT, mTORC1 signaling, the G2M checkpoint, and E2F targets (Supplementary Figure 10B). The Hallmark pathway analysis of CDH4 showed that it was significantly associated with the EMT, myogenesis, and apical junctions (Supplementary Figure 11B). The Hallmark pathway analysis of CDH11 indicated that it was significantly associated with the EMT, UV response_DN, coagulation, and angiogenesis (Supplementary Figure 12B). The Hallmark pathway analysis of CDH12 revealed that it was significantly associated with the EMT, tumor necrosis factor (TNF)-α signaling via nuclear factor (NF)-κB, and UV response_DN (Supplementary Figure 13B). The Hallmark pathway analysis of CDH13 showed that it was significantly associated with UV response_DN, DNA repair, adipogenesis, and IL-2-signal transduction and activator of transcription 5 (STAT5) signaling (Supplementary Figure 14B). CDH2/4/11/12 were all associated with EMT signaling in the GSEA and were seen to be important inflammation- and immune-related gene sets and cancer-related gene sets in tumor metastasis.
We used the miRWalk database to identify associations with CDH1/2/4/11/12/13, and network regulation was analyzed by an IPA. Analysis of miRNA-regulated networks with CDHs (Supplementary Figure 15) indicated that hsa-miR-219a-2-3p regulated CDH1 and was thus associated with breast cancer development; hsa-miR-330-3p, has-miR-4429, and hsa-miR-199a-5p regulated CDH2; hsa-miR-4644, hsa-miR-211-5p, hsa-miR-520f-3p, hsa-miR-34e-5p, and hsa-miR-34a-5p regulated CDH4; hsa-miR-486-5p, hsa-miR-200c-3p, hsa-miR-200b-3p, hsa-miR-26a-5p, hsa-miR-140-5p, hsa-miR-128-3p, and hsa-miR-19a-3p regulated CDH11; and hsa-miR-30c-5p regulated CDH13. In a previous study, the miRNA hsa-miR-200 family was identified as being a definitive factor of the epithelial phenotype of malignant cells, which targeted the E-cadherin repressors, zinc finger E-box-binding homeobox 1 (ZEB1) and ZEB2 [88–90]. Meanwhile, hsa-miR-200 was identified as a repressor of the EMT and was downregulated in more-aggressive molecular subtypes of breast tumors such as HER2 and triple-negative [91]. Our results of miRNA-regulated networks that hsa-miR-200c-3p and hsa-miR-200b-3p regulated CDH11 were consistent with previous studies.
The TIMER database was utilized to investigate the immunological microenvironment. We identified correlations of immune infiltration levels with expressions of CDH gene family members in breast cancer (Figure 9). Results of the analysis showed significant correlations of CDH1 with cluster of differentiation 4-positive (CD4+) T cells; CDH2 with CD4+ T cells, macrophages, neutrophils, and dendritic cells (DCs); CDH4 with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs; CDH7 with CD8+ T cells; CDH11 with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs; CDH12 with B cells and DCs; CDH13 with CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and DCs; and CDH15 with CD8+ T cells.
After comprehensive research on CDH family members, we were curious about drug targets and related mechanisms of drug resistance. Hence, drug target networks of CDH1/2/4/11/12/13 were analyzed by the MetaCore and MetaDrug system (Supplementary Figure 16 in Supplementary Materials). We found that “Signal transduction_c-myc, CREB1 signaling” was the top drug target of CDH1; “Cell adhesion_Fibrinogen, collagen signaling” was the top drug target of CDH2; “Metabolism_PPAR, RXR, VDR regulation of metabolism” was the top drug target of CDH4; “Cell adhesion_Fibrinogen, collagen signaling” was the top drug target of CDH11; “Transport_Potassium transport (core work 1)” was the top drug target of CDH12; and “Cell adhesion_Intergrin signaling” was the top drug target of CDH13.
In previous studies, the CDH family was proven to be associated with invasiveness and metastasis [92–95]. The cadherin family as transmembrane glycoproteins mediate calcium-dependent cell-cell adhesion and regulates cell growth and differentiation. In the process of cell adhesion, cadherins act as essential factors to maintain stable homeostasis of tissue structures [96–98]. Once cell-cell adhesion is disturbed, adhesion-related pathways are subsequently interfered with. Disruption of cadherin signaling has determining influence on tumor progression and tumor immune responses [99–104]. In the present study, to determine whether CDH family members can serve as suitable biomarkers for breast cancer and pathways related to the EMT and metastasis, comprehensive integrative data mining was utilized, including gene expressions, survival analyses, clinical and pathological factors, immune infiltration, and enrichment pathway analyses. In the Oncomine, TIMER, and prognostic analyses, significantly high expression levels of CDH1/2/4/7/11/12/13/15 were observed in breast cancer compared to normal tissue samples, and these were associated with poor DMFS outcomes. These results were confirmed by IHC staining in which CDH1, CDH2, and CDH12 exhibited strong intensities. Furthermore, results of the bc-GenExMiner database demonstrated that increased CDH4/12/13 expressions were associated with basal-like breast cancer, and increased CDH1/2/11 expressions suggested a high SBR grade status in patients. Genetic mutations of CDH1 and CDH4 at frequencies of >10% showed higher possibilities of altering cell signaling pathways and promoting proliferation in malignancies. CDH2, CDH4, and CDH11 had close relationships via the PPI network, and this was further confirmed by the MetaCore enrichment pathway analysis. These three CDH family genes, CDH2, CDH4 and CDH11, and genes coexpressed with CDH13 were correlated with the “Cell adhesion_ECM remodeling” process. CDH11 and CDH13 were also found to be closely related to CDH1 due to its roles in regulating of the EMT. The enrichment pathway results suggested that in addition to CDH1, genes coexpressed with CDH11 and CDH13 were also correlated with “Development_Regulation of the epithelial-to-mesenchymal transition (EMT)”. These correlations of CDH family genes could lead to a better understanding of breast cancer development and metastasis. The ability to infiltrate different tissues is a critical step in cancer because it defines the metastatic potential of tumor cells [105–108]. This capacity can be achieved by the EMT [109–111]. Previous studies reported that the EMT is featured by the loss of CDH1 expression and the concomitant upregulation or de novo expression of CDH2, the so-called “cadherin switch”, which is associated with increased migration and invasiveness and thus poor prognoses [112–115]. The EMT causes disorganization of cell-cell adhesive junctions, thereby facilitating cancer metastasis. Irrespective of CDH1 expression, the migratory and invasive capacities are present in tumor cells by CDH2 expression. Therefore, CDH2 seems to be the key factor in epithelial cancer metastasis and disease progression. Those studies demonstrated the key roles of CDH2 in cancer metastasis, corresponding to our results with poor survival prognoses, strong intensities in pathological samples, and advanced SBR grading, indicating poor cell differentiation. Furthermore, we found that CDH2/4/11 had similar signaling pathways with cell adhesion, which was further correlated with the EMT. In other words, high expression levels of CDH2/4/11 are crucial for the EMT and cancer metastasis. To validate our results of positive correlations between CDH genes and the EMT, Pearson’s correlations were utilized to calculate correlations between CDH1/2/4/7/11/12/13/15 and EMT-regulated genes such as TWIST and SNAIL based on mRNA expression levels (Supplementary Figure 17 in Supplementary Materials). Other EMT-core genes associated with cell adhesion and migration were obtained from a previous study [116]. CDH11 displayed the greatest correlations with COL3A1, COL1A1, COL5A1, and ADAM12 with Spearman’s rank correlation coefficients of >0.5 among these eight genes. CDH13 showed mild positive correlations with COL3A1, COL1A1, COL5A1, ADAM12, SNAI2, COL6A1, and TWIST2. Conversely, traditional EMT markers, CDH1 and CDH2, demonstrated nearly no correlations with these common EMT-regulated genes. CDH1 only showed mild negative correlations with TWIST2 and SNAI3. Other genes otherwise showed relatively no correlations with EMT-core genes. It was interesting to discover that CDH11/13 demonstrated greater correlations with EMT-core genes rather than the traditional EMT-related cadherins, E-cadherin and N-cadherin, as mentioned in previous research [117, 118]. Our results supported the roles of CDH11 in inducing the EMT, which corresponded to other research not only in cancer [119] but in other diseases including melasma [120] and pulmonary fibrosis [121]. To understand drug targets of CDH family genes, we implemented a drug target network analysis (Supplementary Figure 16). Since drug target network analyses of the CDH2, CDH11, and CDH13 genes were all targeted to cell adhesion, we thus discussed the roles of cell adhesion in drug resistance. Cancer cells attaching to microenvironment components such as collagen type 1 (COL1) weakens the sensitivity of chemotherapeutic drugs like mitoxantrone, which is called cell adhesion-mediated drug resistance (CAM-DR) [122]. In consideration of the extensive presence of COL1 in mammary glands, breast cancer appears to have a high probability of presenting CAM-DR. The importance of COL1 is proven that patients with high-density breast tissues have higher risks of breast cancer [123–125] and poorer outcomes due to metastatic processes [126]. Regarding CAM-DR, the EMT plays a crucial part in drug resistance to breast cancer as well. The epithelial cell adhesion molecule (EpCAM) was implicated in tumor progression and drug resistance in breast cancer [127]. It was proven that EpCAM-knockdown resulted in upregulation of CDH1 and attenuation of CDH2 expression, which reversed the EMT. This process demonstrated that the EpCAM might possess the capability to induce the EMT in breast cancer to promote multidrug resistance. In addition, transcriptional silencing of CDH1 was associated with the EMT in human breast cancer cells [128]. Previous research demonstrated that upregulation of E-cadherin by miR-200b and miR-200c via direct targeting the transcriptional repressors of E-cadherin, ZEB1 and ZEB2, inhibited the EMT [129]. In summary, CDH1/2/11/13 were associated with the cell adhesion network on drug targets and were thus associated with important factors in drug resistance. CDH4 hypermethylation was significantly associated with increased risks for breast cancer in peripheral blood leukocyte DNA [130]. CDH11 was also known as one of the mediators that interacted with malignant cells and normal cells and was detected in various cancers, especially in metastatic cancer cell lines [131–133]. In particular, CDH11 was involved in the maintenance of high endogenous Rac activity and cytoskeletal reorganization in migratory breast cancer cells [134]. Moreover, because of the role of CDH11 as an inducer of metastatic signaling, targeting CDH11 triggered re-expression of the miR-335 tumor suppressor, which limited the CDH11-induced EMT. This phenomenon repressed cancer stem cell activities. CDH11-related pathways demonstrated the miR-335-mediated therapeutic value of anti-CDH11 antibody treatment and provided a therapeutic option in patients with metastatic breast cancer. Downregulation of CDH12 could inhibit the process of angiogenesis. Previous research implied that CDH12 might be influential in colorectal tumor metastasis [135]. CDH13 expression exhibited functions in cell adhesion and migration which were promoted by DNA polymerase beta (Pol β) by augmenting DNA demethylation of the CDH13 promoter [136]. Abnormal methylation of CDH13 promoter was observed in breast, colorectal, cervical and lung cancers, and chronic myeloid leukemia [137–139]. Those studies supported our results of the importance of CDH4/11/12/13 in tumorigenesis. We supposed that high mRNA expression levels of CDH4/11/12/13 were associated with breast cancer and poor survival. As the tumor microenvironment plays important roles in tumorigenesis, we conducted an immune infiltration analysis in Figure 9. Previous studies also supported the associations between cadherin and immune pathways [140–142]. One of the most important pathways related to cadherin in immune responses is the Wnt pathway, which regulates cellular signaling by a canonical pathway with β-catenin [143]. β-Catenin plays a fundamental role in the cadherin protein complex, whose stabilization is crucial to activate the Wnt/β-catenin pathway. The WNT/β-catenin pathway mediates the self-renewal and relocation of cancer stem cells, promoting malignant progression and metastasis in breast cancer [144]. Our results of the enrichment pathway analysis were consistent with the importance of Wnt/β-catenin in breast cancer. Genes co-expressed with CDH1/12/15 were correlated with the pathway of “Development negative regulation of WNT/Beta catenin signaling in the cytoplasm”. Induction of Wnt/β-catenin signaling was crucial in maintenance of stemness of memory CD8+ T cells by blocking T-cell differentiation [145]. Clinical responses to immune checkpoint inhibitors were correlated with tumors in the immune cell microenvironment [146, 147]. The Wnt/β-catenin pathway is considered to be a potential target for cancer treatment. In pancreatic cancer, effective immunotherapy is likely to require upregulation of CDH1 expression [148]. The roles of cadherin and Wnt/β-catenin signaling in regulating immune cell infiltrations of the tumor microenvironment aroused interest in immunotherapy treatment. This study performed a comprehensive and systematic review of the genetic expressions, prognostic values, mutation levels, immune infiltration, and enrichment pathways of the CDH family. CDH1/2/4/11/12/13 expressions are significantly increased in breast cancer and are associated with poor clinical prognoses of DMFS. We concluded that CDH1/2/4/11/12/13 may be crucial for breast cancer tumorigenesis, providing novel insights into developing detection biomarkers or targeted therapies for breast cancer. Nevertheless, evidence from clinical applications such as in vitro data or large patient cohorts should be produced to validate associations between CDH1/2/4/11/12/13 and breast cancer.
CDH1/2/4/11/12/13 were overexpressed in breast cancer and were associated with poor prognoses in the distant metastasis-free survival analysis. Genes coexpressed with these CDH family members were correlated with regulation of the EMT and cell adhesion ECM remodeling, which were validated as playing critical roles in tumor metastasis. Although further evidence of clinical correlations for validation in the future should be determined to support our hypothesis, CDH1/2/4/11/12/13 are expected to be potential biomarkers for breast cancer progression and metastasis. |
PMC9648793 | 36260870 | Xingchen Fan,Xuan Zou,Cheng Liu,Shuang Peng,Shiyu Zhang,Xin Zhou,Jun Zhu,Wei Zhu | Identify miRNA-mRNA regulation pairs to explore potential pathogenesis of lung adenocarcinoma | 19-10-2022 | miRNA,miRNA-mRNA regulation pairs,lung adenocarcinoma | Purpose: MicroRNA (miRNA) function via base-pairing with complementary sequences within mRNA molecules. This study aims to identify critical miRNA-mRNA regulation pairs contributing to lung adenocarcinoma (LUAD) pathogenesis. Patients and methods: MiRNA and mRNA microarray and RNA-sequencing datasets were downloaded from gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases. Differential miRNAs (DE-miRNAs) and mRNAs (DE-mRNAs) were screened by the GEO2R tool and R packages. DAVID, DIANA, and Hiplot tools were used to perform gene enrichment analysis. The pairs of miRNA-mRNA were screened from the experimentally validated miRNA-target interactions databases (miRTarBase and TarBase). External validation was carried out in 30 pairs of LUAD tissues by quantitative reverse transcription and polymerase chain reaction (qRT-PCR). The diagnostic value of the miRNA-mRNA regulation pairs was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Biological function assay was were also performed to confirm the function of miRNA-mRNA axis in LUAD progression. The study also performed the clinical, survival and tumor-associated phenotypic analysis of miRNA-mRNA pairs. Results: A total of 7 miRNA and 13 mRNA expression datasets from GEO were analyzed, and 11 DE-miRNAs (5 down-regulated and 6 up-regulated in LUAD tissues) and 128 DE-mRNAs (30 up-regulated and 98 down-regulated in LUAD tissues) were identified. The pairs of miR-1-3p(down) and CENPF(up) and miR-126-5p(down) and UGT8(up) were verified in the external validation cohort (30 LUAD vs. 30 NC) using qRT-PCR. Areas under the ROC curve of the two miRNA-mRNA regulation pairs panel were 0.973 in TCGA-LUAD and 0.771 in the external validation. The DCA also showed that the miRNA-mRNA regulation pairs had an excellent diagnostic performance distinguishing LUAD from normal controls. The expression of the regulation pairs is different in different ages, TNM stages, and gender. The overexpression of miR-1-3p and miR-126-5p significantly inhibited the proliferation and migration of LUAD cells. Correlation analysis showed that CENPF correlated with prognosis and tumor immunity. Conclusions: The research identified potential miRNA-mRNA regulation pairs, providing a new idea for exploring the genesis and development of LUAD. | Identify miRNA-mRNA regulation pairs to explore potential pathogenesis of lung adenocarcinoma
Purpose: MicroRNA (miRNA) function via base-pairing with complementary sequences within mRNA molecules. This study aims to identify critical miRNA-mRNA regulation pairs contributing to lung adenocarcinoma (LUAD) pathogenesis. Patients and methods: MiRNA and mRNA microarray and RNA-sequencing datasets were downloaded from gene expression omnibus (GEO) and the cancer genome atlas (TCGA) databases. Differential miRNAs (DE-miRNAs) and mRNAs (DE-mRNAs) were screened by the GEO2R tool and R packages. DAVID, DIANA, and Hiplot tools were used to perform gene enrichment analysis. The pairs of miRNA-mRNA were screened from the experimentally validated miRNA-target interactions databases (miRTarBase and TarBase). External validation was carried out in 30 pairs of LUAD tissues by quantitative reverse transcription and polymerase chain reaction (qRT-PCR). The diagnostic value of the miRNA-mRNA regulation pairs was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Biological function assay was were also performed to confirm the function of miRNA-mRNA axis in LUAD progression. The study also performed the clinical, survival and tumor-associated phenotypic analysis of miRNA-mRNA pairs. Results: A total of 7 miRNA and 13 mRNA expression datasets from GEO were analyzed, and 11 DE-miRNAs (5 down-regulated and 6 up-regulated in LUAD tissues) and 128 DE-mRNAs (30 up-regulated and 98 down-regulated in LUAD tissues) were identified. The pairs of miR-1-3p(down) and CENPF(up) and miR-126-5p(down) and UGT8(up) were verified in the external validation cohort (30 LUAD vs. 30 NC) using qRT-PCR. Areas under the ROC curve of the two miRNA-mRNA regulation pairs panel were 0.973 in TCGA-LUAD and 0.771 in the external validation. The DCA also showed that the miRNA-mRNA regulation pairs had an excellent diagnostic performance distinguishing LUAD from normal controls. The expression of the regulation pairs is different in different ages, TNM stages, and gender. The overexpression of miR-1-3p and miR-126-5p significantly inhibited the proliferation and migration of LUAD cells. Correlation analysis showed that CENPF correlated with prognosis and tumor immunity. Conclusions: The research identified potential miRNA-mRNA regulation pairs, providing a new idea for exploring the genesis and development of LUAD.
Lung cancer has the highest incidence and mortality of all cancers [1, 2]. Lung adenocarcinoma (LUAD) is a major component of lung cancer, accounting for 40% of lung cancer [3]. Although the oncology treatment of advanced lung cancer has made significant progress in recent years, the 5-year survival rate remains poor. Therefore, further studies on the underlying mechanism of tumor initiation and development are necessary. MicroRNA (miRNA) is a class of short non-coding RNA molecules ranging from 19 to 25 nucleotides [3–5]. MiRNAs work by base-pairing with complementary sequences within the mRNA molecule [6, 7]. More and more researches are focusing on the miRNA-mRNA regulation pairs, trying to explore the mechanism of the pairs in the occurrence and development of the disease [8–10]. The research performed an extensive analysis of miRNA-mRNA regulatory pairs in LUAD to provide a new strategy for the underlying mechanism of LUAD.
We downloaded the miRNA and mRNA expression profile from the TCGA database and the Gene Expression Omnibus (GEO) database. We use the GEO database web analytics tool GEO2R and “limma” and the “edgeR” R bag to filter DE-mRNAs and DE-miRNAs. The overview of the workflow steps is shown in Figure 1.
TarBase and miRTarBase databases were used to screen miRNA-mRNA regulatory pairs. TarBase and miRTarBase are experimentally supported miRNA target index reference databases [11, 12]. Then, we further analyzed the correlation between miRNA and mRNA in TCGA-LUAD. We used DAVID, Diana-miRPath and Hiplot for functional and pathway analysis [13].
Formalin-fixed paraffin-embedded (FFPE) of LUAD and corresponding normal tissues were obtained from the First Affiliated Hospital of Nanjing Medical University. This study was conducted in accordance with the Helsinki Declaration and approved by the Institutional Review Committee of the First Affiliated Hospital of Nanjing Medical University (ID: 2016-SRFA-148). All specimens were collected with informed written consent of patients. The clinical characteristics of the 30 patients are listed in Table 1.
External validation of qRT-PCR validation was performed using PrimeScript RT reagent Kit (Takara) and SYBR Premix Ex Taq II (Takara). PCR primer sequences are shown in Supplementary Table 1. We used the 2-ΔΔCt to calculate miRNA and mRNA expression levels (ΔCt = CtmiRNA/mRNA− Ctnormalizer; Ct: the threshold cycle) [14].
Lung adenocarcinoma cell lines A549 was obtained from the American Type Culture Collection (ATCC). The cells were seeded into 24-well plates. The miR-126-5p mimics, miR-1-3p mimics, Negative control mimics were purchased from RiboBio. When cell fusion reached 60%, cells were transfected with 20mM Opti-MEM transfection medium (Invitrogen) and Lipofectamine 2000(Invitrogen).
Cell Counting Kit-8 (CCK-8, Dojindo, Kumamoto, Japan) assay was used to assess cell proliferation. At indicated time points (24h, 48h, 72h, 96h), the cells were incubated in 10% CCK8 solution in culture medium at 37° C. The absorbance at 450nm was measured with a microplate reader. To examine the migratory ability of cells in vitro, the scratch wound healing assay was performed. When the cells were cultured to 80%-90% in 6-well plates, after the medium was discarded, the cells were scratched with 100 μL tip. The cells were placed in serum-free DMEM medium and observed at 0 and 24h.
We downloaded the data of single sample gene set enrichment analysis (ssGSEA) from UCSC Xena [15, 16]. The infiltrating immune cell types data were downloaded from the TCGA website [17]. ESTIMATE software was used to evaluate the stromal and immune levels of TCGA-LUAD specimens [18]. The data of TMB and methylation in TCGA-LUAD samples were obtained from the UCSC Xena platform (https://xena.ucsc.edu/) [19].
We used the IBM SPSS Statistics v.26 software, GraphPad Prism software and R language v3.6.3 (https://cran.r-project.org/) to analyze the data.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
A total of 7 miRNA and 13 mRNA expression datasets were downloaded from GEO database, and the information of 20 GEO datasets is shown in Table 2. As shown in Figure 2A, the GEO2R tool was used to analyze each dataset, and the DE-miRNAs and DE-mRNAs in each dataset were screened out. Then, the intersection was taken in the GEO database. A rank-sum test was performed to screen out the DE-miRNAs and DE-mRNAs in the TCGA database. A total of 11 miRNAs and 128 mRNAs were selected with differences in both databases as the final DE-miRNAs and DE-mRNAs (Table 3). We utilized DIANA-miRPath to predict the possible functions of the 11 DE-miRNAs (Figure 2B). KEGG pathway enrichment analysis revealed that the DE-mRNAs enriched in the drug metabolism, etc. (Figure 2C). The GO terms were enriched in the cell adhesion, cellular protein modification process, cytoplasm, organelle, etc.
As shown in Figure 3A, six miRNA-mRNA regulation pairs (miR-1-3p/CENPF, miR-126-5p/UGT8, miR-135b-5p/BMPR2, miR-9-5p/STARD13, miR-196a-5p/TGFBR3, miR-1-3p/UGT8) were identified. The 6 pairs of miRNA-mRNA were experimentally verified, and the 4 miRNA-mRNA pairs in TCGA-LUAD showed significant negative correlation (Figure 3B).
We used qRT-PCR to verify 4 miRNA-mRNA pairs in 30 matched tissues. In Figure 4, the expression of the miR-1-3p (P=0.0037) and miR-126-5p (P=0.0032) were down-regulated in tumor tissues, while miR-135b-5p (P=0.0037), CENPF (P<0.001) and UGT8 (P<0.001) were up-regulated. There was no significant difference in the expression of miR-9-5p (P=0.0841), BMPR2 (P=0.4522), and STARD13 (P=0.1241). Spearman correlation analysis showed that miR-1-3p was significantly correlated with CENPF expression (P<0.001, r=-0.5398), and miR-126-5p was significantly correlated with UGT8 (P=0.0116, r=-0.3239). IHC images in the HPA database evidenced higher expression of CENPF and UGT8 in LUAD tissue than in normal control and the results are shown in Supplementary Figure 1.
MiR-1-3p, PTPRM, miR-126-5p and UGT8 were combined as a panel using the logistic regression analysis, and the equation to predict LUAD probability was: Logit(P) = 0.813 + 0.028*miR-126-5p – 0.262*UGT8 + 1.727*miR-1-3p – 0.526*CENPF. The AUC of the panel was 0.973 (95% CI: 0.955-0.991, p<0.0001) in TCGA-LUAD and 0.771 (95% CI: 0.652-0.890, p<0.0001) in the external validation (Figure 5A, 5B). The DCA results showed that regulation pairs had good diagnostic performance (Figure 5C, 5D).
According to the analysis of FIGO stages, the expression of CENPF (P=0.008) is lower in early-stage (I) than late-stage (II+III+IV) (Figure 6A). The expression of miR-1-3p (P=0.001) in female patients is higher, while CENPF (P=0.001) was lower in female patients (Figure 6B). CENPF was higher in the age≤65 group while UGT8 was lower in the age≤65 group (Figure 6C). We analyzed the association of the regulation pairs and gene mutations in KRAS, ROS1, ALK, and EGFR. The level of miR-1-3p was higher in KRAS(P=0.039), ROS1(P=0.013), ALK(P=0.02) wild-type LUAD tissues than KRAS-mutated LUAD tissues (P=0.039, P=0.013, and P=0.02, respectively). The expression of CENPF was higher in ROS1-mutated, ALK-mutated LUAD tissues and EGFR wild-type LUAD tissues (P<0.001, P=0.008, and P=0.041, respectively) shown in Figure 6D. K-M survival analysis showed that CENPF (P=0.026) correlated with prognosis, and the higher the CENPF expression level, the worse the prognosis (Figure 6E).
We transfected miR-1-3p mimic and miR-126-5p mimic into A549 cells to established miR-1-3p and miR-126-5p overexpressed cells to investigate the potential function in regulating LUAD cell proliferation. The expression level of miR-1-3p and miR-126-5p in A549 cells upregulated significantly after transfecting miR-1-3p and miR-126-5p mimic (Figure 7A). We found that the mRNA levels of CENPF and UGT8 were declined after transfecting miR-1-3p mimics and miR-126-5p mimics respectively (Figure 7A). Then CCK8 assay was performed to testify the effects of miR-1-3p and miR-126-5p on cell proliferation. MiR-1-3p and miR-126-5p overexpressed significantly inhibited cell proliferation of A549 cells after transfecting 48h, 72h and 96h (Figure 7B). To gain further insight into the role of miR-1-3p and miR-126-5p in LUAD cell migration was performed in A549 cells transfected with miR-1-3p and miR-126-5p mimics or Negative control. The overexpression of miR-1-3p and miR-126-5p significantly inhibited LUAD cell migration (Figure 7C).
The miRNA-mRNA regulation pairs correlated with mRNA synthesis pathways, such as transport of the SLBP dependent mature mRNA (Figure 8A). Therefore, we further analyzed the correlation between miRNA-mRNA regulation pairs and immune cells, and explored its role in tumor immunity. There are 19 different types of immune cells between tumor tissue and normal tissue, as shown in Supplementary Table 2. MiR-1-3p and CENPF correlated with macrophages m0, mast cells resting, and dendritic cells resting, etc. while miR-126-5p and UGT8 were related to plasma cells (Figure 8B). As shown in Figure 8C, miR-1-3p and CENPF have some correlation with TMB and tumor microenvironment, but not with DNA methylation.
In the past few years, many studies have suggested that changes in the expression levels of miRNA and mRNA are closely related to cancers [20–22]. The research aims to construct potential miRNA-mRNA regulatory pairs in LUAD. Firstly, we selected qualified datasets from the GEO database and determined 7 miRNA and 13 mRNA datasets. Expression profiles in the GEO datasets and TCGA database were analyzed using GEO2R, “R-limma” and “R-edgeR” tools to screen for DE-miRNA and DE-mRNA. 11 DE-miRNAs (6 upregulated and 5 down-regulated miRNAs) and 270 DE-mRNAs (30 upregulated and 98 down-regulated mRNAs) showed consistent differential expression in the TCGA database and 7 miRNA and 13 mRNA datasets of the GEO database. The verified pairs were screened from miRTarBase and TarBase database, and Pearson correlation analysis was performed on TCGA-LUAD to screen out 4 miRNA-mRNA regulatory pairs. We further verified the expression levels of 4 pairs in 30 pairs of FFPE lung tissues by qRT-PCR. Finally, the pairs of miR-1-3p-CENPF and miR-126-5p-UGT8 were verified. In this study, miR-1-3p was low expressed in LUAD tissues and was different in different genders. Studies have shown that miR-1-3p is significantly down-regulated in human LUAD tissues and acts as a suppressor in LUAD cells [23, 24]. Overexpression of CENPF has been reported to have poor prognosis and metastasis of breast cancer, lung adenocarcinoma and prostate cancer [15, 25, 26]. In the research, the expression of CENPF was higher in LUAD tissues and higher in late-stage (II+III+IV) compared with early-stage (I). Patients with higher CENPF expression had worse prognosis. Our study found that miR-126-5p was lower while UGT8 was higher in LUAD tissue. miR-126-5p plays an important role in regulating apoptosis, invasion, migration and EMT of NSCLC cells [27]. A previous study reported that UGT8 is a molecular marker associated with lung cancer metastasis [28]. The correlation analysis between ssGSEA and miRNA-mRNA regulation pairs indicated that these two miRNA-mRNA regulation pairs were related to the synthesis and processing of RNA and mRNA. MiR-1-3p targeting CENPF affects the tumor microenvironment through infiltrating interactions with tumor-associated inflammation, macrophages, mast cells, dendritic cells, and B cells. Therefore, CENPF has an important relationship with tumor immunity. KRAS, ROS1, ALK, and EGFR are the main biomarkers affecting clinical practice of lung cancer [29–31]. KRAS mutations are present in 30% of lung adenocarcinomas and lead to activation of the Ras-Raf-MEK-ERK signaling pathway, making it an attractive target for small molecule inhibition in KRAS mutant NSCLC [32]. ROS-1 chromosomal rearrangement defines novel genomic driver in 1-2.5% of NSCLC patients [33]. The product of EML4-ALK is detected in 3–6% of unselected NSCLC [34, 35]. In this study, we discovered that miR-1-3p is down-regulated in KRAS, ROS1, and ALK mutation cases while CENPF is up-regulated in ROS1 and ALK mutation cases. EGFR may be involved in the progression of NSCLC by regulating various biological processes [36]. In the study, the expression of CENPF is down- regulated in EGFR mutation cases. Further study will continue to explore the potential role of miR-1-3p and CENPF in monitoring KRAS, ROS1, ALK and EGFR treatment effectiveness. Although the regulation of miRNA-mRNA involved in LUAD was comprehensively analyzed and experimentally verified in this study, there are still some deficiencies in this study, such as lack of studies and insufficient sample size on the mechanism of DE-miRNAs and DE-mRNAs. More researches are needed to address these questions.
In summary, we have constructed two miRNA-mRNA regulatory pairs that may be involved in the pathogenesis of LUAD. In the future, it is possible to help the treatment and prognosis of LUAD by targeting the established miRNA-mRNA regulatory pairs. |
PMC9648795 | 36287187 | Jing Zhen,Yun Ke,Jingying Pan,Minqin Zhou,Hong Zeng,Gelin Song,Zichuan Yu,Bidong Fu,Yue Liu,Da Huang,Honghu Wu | ZNF320 is a hypomethylated prognostic biomarker involved in immune infiltration of hepatocellular carcinoma and associated with cell cycle | 26-10-2022 | ZNF320,liver cancer,biomarker,cell cycle,immune infiltrates | Hepatocellular carcinoma (HCC) is one of the most deadly and common malignant cancers around the world, and the prognosis of HCC patients is not optimistic. ZNF320 belongs to Krüppel like zinc finger gene family. However, no studies have focused on the influence of ZNF320 in HCC. We first analyzed ZNF320 expression in HCC by using data from TCGA and ICGC, then conducted a joint analysis with TIMER and UALCAN, and validated by immunohistochemistry in clinical HCC samples. Then we applied UALCAN to explore the correlation between ZNF320 expression and clinicopathological characteristics. Consequently, using Kaplan-Meier Plotter analysis and the Cox regression, we can predict the prognostic value of ZNF320 for HCC patients. Next, the analysis by GO, KEGG, and GSEA revealed that ZNF320 was significantly correlated to cell cycle and immunity. Finally, TIMER and GEPIA analysis verified that ZNF320 expression is closely related to tumor infiltrating immune cells (TIIC), including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The analysis of the TCGA and ICGC data sets revealed that ZNF320 expression was significantly correlated with m6A related genes (RBMX, YTHDF1, and METTL3). In conclusion, ZNF320 may be a prognostic biomarker related to immunity as a candidate for liver cancer. | ZNF320 is a hypomethylated prognostic biomarker involved in immune infiltration of hepatocellular carcinoma and associated with cell cycle
Hepatocellular carcinoma (HCC) is one of the most deadly and common malignant cancers around the world, and the prognosis of HCC patients is not optimistic. ZNF320 belongs to Krüppel like zinc finger gene family. However, no studies have focused on the influence of ZNF320 in HCC. We first analyzed ZNF320 expression in HCC by using data from TCGA and ICGC, then conducted a joint analysis with TIMER and UALCAN, and validated by immunohistochemistry in clinical HCC samples. Then we applied UALCAN to explore the correlation between ZNF320 expression and clinicopathological characteristics. Consequently, using Kaplan-Meier Plotter analysis and the Cox regression, we can predict the prognostic value of ZNF320 for HCC patients. Next, the analysis by GO, KEGG, and GSEA revealed that ZNF320 was significantly correlated to cell cycle and immunity. Finally, TIMER and GEPIA analysis verified that ZNF320 expression is closely related to tumor infiltrating immune cells (TIIC), including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The analysis of the TCGA and ICGC data sets revealed that ZNF320 expression was significantly correlated with m6A related genes (RBMX, YTHDF1, and METTL3). In conclusion, ZNF320 may be a prognostic biomarker related to immunity as a candidate for liver cancer.
There is evidence that hepatocellular carcinoma is a tumor with a poor prognosis and the third primary cause of cancer death in the world. Globally, the number of deaths due to HCC is estimated to be 810,000 per year, while there exit about 854,000 new cases of HCC per year [1]. Among the twenty-two most frequent cancer types, hepatocellular carcinoma (HCC) ranks sixth from the frequency perspective and fourth from the death rate associated with cancer perspective [1]. It is extremely hard to detect HCC at an early stage [2, 3]. The most common treatment for HCC is surgery, which is able to improve the survival rate, but HCC patients will still be detected with terminal cancer and have a poor prognosis. Recent existing treatments of HCC (e.g., surgery, chemoradiotherapy, hepatic transplantation, and radiofrequency ablation) can only cure a few patients, and untreated HCC patients’ mediant of overall survival (OS) is less than nine months [4]. Therefore, there is an urgent need to explore new early diagnostic markers and therapeutic targets so that the prognosis of HCC patients can be improved. As the largest transcription factor family in the human genome, the ZNF family is widely involved in various biological processes in the human body, and zinc finger (ZNF) transcription factors are characterized by finger-like DNA domains, which require one or more zinc ions to stabilize the structure. Previous research has established the possibility that ZNF is a dominant factor in the occurrence and development of HCC [5] that ultimately regulates cell proliferation, apoptosis, invasion and metastasis by regulating the transcription of downstream target genes through a variety of regulatory levels [5, 6]. It could be a new tumor biomarker and therapeutic target for HCC [7, 8]. Though previous research has established the diagnostic and prognostic biomarkers for HCC alpha-fetoprotein (AFP), the reliability and accuracy of other biomarkers, including AFP-L3, osteopontin, and glypican-3 still need to be improved to detect HCC at an early stage [9, 10]. Zinc finger proteins are one of the most plentiful proteins in the eukaryotic system, which own exceptionally abundant biological functions, including DNA recognition, RNA transcriptional activation, apoptosis and protein structure [11]. There exists a study which objections are to confirm ZNF320 was implicated in glioblastoma [12]. However, the role and mechanism of ZNF320 in HCC have not been revealed, and its correlation with prognosis remains uncertain. The research data in this thesis is drawn from four main sources: TCGA, GTEx, UALCAN databases, and patients and tumor specimens from 35 patients undergoing nephrectomy in the Second Affiliated Hospital of Nanchang University from June 2017 to January 2021. In this study, we dissected the expression of ZNF320 mRNA and protein in HCC and assessed the correlation between the expression level of ZNF320 and prognosis of HCC. Besides, we also investigated the possible mechanism of high expression of ZNF320 in HCC and the relation of ZNF320 expression and cell cycle, tumor infiltrating immune cells [3, 13] in HCC patients. The importance and originality of this study are that it reveals the important function of ZNF320 in hepatocellular carcinoma and provides a potential link between ZNF320 and cell cycle, HCC immune invasion, m6A Modification and its underlying mechanisms.
Hepatocellular carcinoma (HCC) clinical and mRNA expression level was collected from the TCGA Database. In terms of the gene expression profile, this study included 374 LIHC samples and 50 normal samples, and the data type of mRNA expression profile was HTSeq-FPKM. We obtained clinical information from 377 patients. In addition, RNA-seq data and clinical information were also acquired from the ICGC website (https://dcc.icgc.org/projects/LIRI-JP). [LINC-JP] Liver Cancer - NCC, JP datasets included 202 normal samples and 243 tumor samples [14].
Human HCC tissues and corresponding adjacent tissues were obtained from 35 patients undergoing nephrectomy in the Second Affiliated Hospital of Nanchang University from January 2018 to January 2021. The patients’ informed consent was obtained. At the same time, the research ethics committee of Second Affiliated Hospital of Nanchang University agreed to the experiment.
The Tumor Immune Estimation Resource (TIMER) is a synthetic web used for analyzing the levels of immune invasion in diverse cancers, which includes 32 cancer types. In this work, we use “Diff-Exp module” to confirm the expression of ZNF320 in diverse tumors. Then the “gene module” was used to estimate the relationship of ZNF320 with immune infiltration. Moreover, the “SCNA module” was used to compare the level of tumor invasion among tumors with different somatic copy number changes for ZNF320. Finally, we considered the correlation between ZNF320 expression in TIICs and immune markers by using related modules. The difference between infiltration level and normal level was evaluated for each SCNA category through Wilcoxon rank-sum test. Finally, with the help of the “Correlation module”, considering the Spearman’s rho value and predicted statistical implications, we verified the relationship between ZNF320 and tumor infiltrating immune cell markers in HCC [15].
Cut formalin-fixed, paraffin-embedded HCC tissue into 4 μm-thick sections. After deparaffinization, rehydration, and microwave heating in microwave-heated antigen unmasking solution (EDTA, pH8.0) to repair the antigen, 30 minutes in total was used to seal the sections with goat serum. Next, incubate the sections overnight with anti-ZNF320 monoclonal antibody (RRID 24882-1-AP, Abcam, 1:500 dilution) at 4° C. After that, HRP-conjugated secondary antibody (Boster) was allowed to stand at room temperature for 2 hours. Subsequently, the two-step method (catalog no.: PV-9000; ZSGB-BIO Co., Ltd., Beijing, China) were used for immunostaining. Finally, three pathologists who are unaware of the clinical parameters assessed the staining intensity and the percentage of positive cells semi-quantitatively.
UALCAN database is a tumor data on-line analysis website that provides comprehensive cancer transcriptome and clinical patient data (extracted from TCGA) [16]. In our research, we assessed ZNF320 expressions by using the “Expression Analysis” module. We also studied the correlation between the ZNF320 expression and clinicopathological features.
LinkedOmics, a comprehensive online site, is usually chosen to analysis multidimensional data within and across 32 kinds of cancer. Using it, we succeeded in mining the co-expressed genes linked to ZNF320 in the TCGA LIHC database through the results of analysis. Volcano plots and heat maps provided strong evidence for this. We define the top 500 related genes as co-expressed genes.
Database for Annotation, Visualization and Integrated Discovery (DAVID) 6.8 is a comprehensive, functional annotation website, which enables us to study the biological functions and signal pathways of specific gene sets. Gene annotation includes Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [17].
The cBioPortal database provides visualization, analysis, and download of large-scale tumor genome data for a variety of cancers. We used it to study the association between ZNF320 promoter methylation level and expression in HCC with “Plot module” [18].
We used GSEA to conduct a group study on the RNA-sequencing data of 374 TCGA-LIHC patients downloaded from Genomic Data Commons, analyzing the pathways associated with ZNF320 expression in HCC specimen. The number of permutations: 1000; normalized enrichment score (NES) >1; false discovery rate (FDR) <0.25, p-val<0.05 [19].
STRING database is an online tool for evaluating protein interactions in multiple ways. In our research, the top 500 genes with co-expression coefficients greater than 0.4 were collected from the STRING database. They were evaluated by Cytoscape 3.8.2 and its plug-in MCODE (Molecular Complex Detection). And the selection criteria are as follows: Max depth=100, node score cutoff=0.2, K-core=2 [20].
Hub genes are highly connected to the nodes in the module and have been certificated to play an important role in function. The significance of the genes was measured by the absolute values of the Spearman’s correlation in our study. On the basis of the results, the top 500 genes with confidence > 0.9 were uploaded to the STRING database to construct protein-protein interaction (PPI). Then, we used Cytoscape 3.8.0 and its plug-in MCODE to evaluate the top 500 genes. Furthermore, a standard for hub genes (yellow nodes) was set with a degree cut-off = 2, node score cut-off = 0.2, k-core = 2, and max. depth= 100, which was screened with MCODE. In total, there were 6 genes scoring the highest and initially defined as hub genes. Ultimately, after digging out their backgrounds, three genes, closely related to the cell cycle, were considered to be a “real” hub gene among these genes.
GEPIA [21], an interactive web server containing RNA sequencing data based on the TCGA and GTEx databases, which analyzed the mRNA expression. In this study, GEPIA was used to compare tumors with normal tissues, analyze pathological stages, and analyze m6A related prognosis. We used Spearman method to ascertain the correlation coefficient of relevance [22].
All statistical analysis in our work was done by R software (version 3.6.3). The detection of different ZNF320 expression levels between LIHC samples and normal samples was realized by using the “limma” and “beeswarm” packages of “R” and rank sum test method. We probed the relevance between ZNF320 expression and clinicopathological feature by Wilcoxon signed-rank test or Kruskal-Wallis test. Then, Kaplan-Meier survival curve using log-rank test to explore the survival distribution among patients with different levels of expression was drawn to evaluate whether the differential genes expression had a significant influence on the prognosis of patients (p<0.05). Univariate and Multivariate Cox regression analysis screened factors significantly related to prognosis (p<0.05) (Cox model uses the “survival” and “survminer” packages of “R”). Lastly, we used the ROC curve drawn by “survival ROC” to evaluate the predictive ability of ZNF320 expression level on one-year, three-year, or five-year survival. T-test was used to study the different expression of m6A interrelated genes between ZNF320 high and low expression groups.
The expression profiling of genes from diabetics and diabetics nephropathy can be available from the GEO (GSE142153 and GSE26168). The other data can be obtained from the corresponding author (Bo Zhang).
To compare the differential expression of ZNF320, we first studied the expression of ZNF320 in LIHC and other tumors by using TIMER database. Expression of ZNF320 in liver hepatocellular carcinoma was predicted to be vitally upregulated (Figure 1A). Next, we used TCGA sequencing data to explore ZNF320 expression in 374 HCC samples and 50 normal samples. Our result revealed ZNF320 expression was vitally upregulated in HCC (Figure 1B). Analysis of the paired samples revealed a notable rise of ZNF320 expression in HCC tissues (n =50) (Figure 1C). What’s more, we used ICGC, another database, to do the same analysis (Figure 1D), and got a similar consequence with our preliminary result in TCGA. To further check the ZNF320 expression in HCC, we compared 35 HCC tissue samples with para cancerous samples by IHC analysis. Our results showed that ZNF320 expression protein in HCC tissues was conspicuously higher than that of adjacent tumor tissues (Figure 1E, 1F). In sum, these outcomes verified that ZNF320 is conspicuously overexpressed in HCC tissues.
After the expression of mRNA and protein were found to increase in HCC patients, we next used UALCAN to integrate various clinic factors of HCC samples, including patients’ individual tumor grade, cancer stages, gender nodal metastasis, age, and weight. Then we compare the expression levels of ZNF320 in each group. The result exhibited that with grade increasing, the patients had statistically higher expression of ZNF320. (Figure 2A) Additionally, ZNF320 were associated with patients’ cancer stages, and patients in more advanced cancer stages has a tendency to have higher expression of ZNF320. (Figure 2B) We also caught that the expression of ZNF330 increased in tumor tissue with nodal metastasis. (Figure 2C) The analysis result revealed significant differences between male and female. (Figure 2D) What’s more, compared with the normal subjects, older and heavier patients maintained a high expression level of ZNF320. (Figure 2E, 2F) The above findings further prove that ZNF320 is increased expression in HCC and related to clinicopathological characteristics.
To further find the potential role of ZNF320 expression in TCGA patients with Hepatocellular Carcinoma, we used Kaplan Meier survival method for survival analysis. We found via the KM plotter that high expression of ZNF320 patients had shorter OS and DSS times than low expression (OS:HR= 1.82, P<0.01; DSS:HR=1.66, P<0.05) (Figure 3A, 3B). The results also manifested that patients with high ZNF320 expression had a obviously shorter OS time proportionate to low expression group, which meant that ZNF320 high expression predicted a poorer prognosis (p = 0.017) (Figure 3C). Next, we constructed the ROC curve to detection the sensitivity and specificity to predict one-, three-, and five- year survival in HCC patients, The AUC of the ROC curve is significant, (one-year AUC:0.645, three-year AUC:0.569, five-year AUC:0.544), which indicates that the expression of ZNF320 can availably predict the survival time of patients (Figure 3D). Then, to screen out deeply the connections between the ZNF320 expression and clinical characteristics, we conducted univariate and multivariate Cox regression (Table 1). The univariate cox analysis revealed ZNF320 (HR: 1.298, 95% CI: 1.127-1.495, p <0.001) is substantially related with OS. The multivariate cox analysis exposed the variables of ZNF320 expression (HR: 1.294, 95% CI: 01.108-1.521, p = 0.001) could regard as an independent prognostic indicator for patients with HCC. The forest map also demonstrated this point (Figure 3E). In brief, our study implied that ZNF320 expression can be an independent prognostic parameter, and cases with elevated ZNF320 expression tend to be communicate with a worse prognosis.
Significant increases of ZNF320 expression in HCC were observed. In consequence, we will study the reason for the overexpression of ZNF320. As methylation plays an important role in gene expression. we studied the methylation and expression of ZNF320 through the cBioPortal dataset. The finding was that ZNF320 expression was highly positively correlative with methylation (R = 0.1, p =0.022) in HCC (Figure 4A). We detected the methylation level of FARSB promoter in liver cancer tissues through the MethSurv website, and the correlation heat map showed that three methylation probes 16204618, cg03067828, and cg24484296 were in a hypermethylated state (Figure 4B) What’s more, the MethSurv analysis indicated that patients with high ZNF320 methylation had a worse overall survival than those with low ZNF320 methylation (p < 0.05) (Figure 4C–4E). In conclusion, the methylation level of ZNF320 was positively associated with its expression. It is supposed that the increased methylation level of ZNF320 promoter leads to the high ZNF320 expression in liver cancer, which in turn causes poor prognosis in patients with liver cancer.
To better realize the biological meaning of ZNF320 in HCC, the LinkedOmics was used to find the co-expression pattern of ZNF320. As displayed in Figure 5A, it reveals that 13934 genes (red dots) positively associated with ZNF320, and 5989 genes (green dots) was negative correlation with ZNF320 (p-value < 0.05). The top 50 obvious gene set positive (left) and negative (right) correlation with ZNF320 were present in the heatmap. (Figure 5B, 5C) The top 200 genes that were correlated most clearly with ZNF320 were extracted for enrichment analysis. Moreover, Supplementary Table 1 detailed lists the co-expressed genes. We used David database to analyze the function and pathway enrichment of gene co-expressed with ZNF320 (version 6.8). Bubble plots demonstrate the top 10 enriched biological functions and pathways (based on P-value) were present by the hipplot. Analysis using the GO database showed that ZNF320 was functionally associated with G2/M transition of mitotic cells, Cell-cell adhesion, Regulation of transcription, Cell division, Cadherin binding involved in cell-cell adhesion, DNA binding, Transcription factor activity (Figure 5D–5F). Pathway analyses were conducted by the KEGG database. Results revealed that genes were enriched mostly in pathways in cancer, Wnt signaling pathway. These pathways, which were related to the tumorigenesis and progression of tumors, indicated that ZNF320 may be closely-related to HCC tumorigenesis and progression. (Figure 5G) Furthermore, GSEA was conducted to seek out KEGG pathways, which exposed FC epsilon pathway, FC gamma R mediated phagocytosis, cell cycle, WNT signaling pathway, and pathways in cancer, leukocyte transendothelial migration, mismatch repair, tight junction (Figure 6A–6I) These results suggested that ZNF320 worked by participating in cell cycle, DNA mismatch repair, WNT signaling pathway and immune-related pathways in HCC.
To further investigate the potential function of ZNF320 in HCC, STRING database was performed on ZNF320 co-expressed genes to make the protein–protein interaction (PPI) network, and Cytoscape (MCODE plug-in) was applied to find the most vital module. What’s more these genes were highlighted in yellow (Figure 7A). Based on the degree score, the module with the highest score consisted PRIM2, SPDL1, CKAP5, GINS4, KIF23, KIF18A. (Figure 7B) And we investigate that there existed an obvious correlation coefficient between ZNF320 and PRIM2 through GEPIA analysis (Figure 7C). What’s more, we have done prognosis analysis of these genes by Kaplan-Meier Survival Method, which showed that all of these 6 genes were oncogenes that were related to poor prognosis. (Figure 7D) The results of pathway analysis proved that all 6 genes in the module were related to cell cycle, and based on the above analysis, we inferred that the impact of ZNF320 on HCC prognosis may be connected to the cell cycle.
It has been confirmed that the occurrence and development of tumors and their prognosis depend on the number of immune cell infiltration. Meanwhile, KEGG and GSEA suggest that ZNF320 is associated to immune infiltration. Through analyzing whether ZNF320 expression was associated to immune infiltration levels in HCC, we discovered a positive correlation between ZNF320 expression and tumor purity, infiltrating levels of B cells, CD8+ T cells, CD4 + T cells, Macrophage, Neutrophils, and Dendritic cell (Figure 8A). Furthermore, ZNF320 CNV was significantly correlate with infiltrating levels of B cells and CD4 + T cells. (Figure 8B). Additionally, we detected the expression of ZNF320 in different immune subgroups in LIHC by TISIDB. We found higher ZNF320 expression in the Cl (wound healing) and C2 (IFN-gamma dominant) subgroups, whereas lower in the C6 (TGF-b dominant) subgroup (Figure 8C). Altogether, ZNF320 is closely related with the main immunity cells in HCC.
To further explore the underlying correspondence of ZNF320 and different immunocyte, TIMER was used to analyze correlations between ZNF320 levels and multiple gene markers of immune cells, which included TAM, M1 and M2 macrophages, CD8+ T cell, T cell and B cell in HCC (Table 2). Because B cells, T cells, CD8+ T cells, and macrophages are the most relevant immune cell types to ZNF320 expression, the connection between ZNF320 and the sets of immune markers of these cells were further investigated through TIMER. We found positive correlations between ZNF320 and the expression of these specific immune markers of these cells, e.g., B cell markers, TAM markers, M1 markers, M2 markers (Figure 9A–9F). In conclusion, our results showed that there is an association between ZNF320 and tumor cell infiltration in HCC. Moreover, ZNF320 was also associate with the HCC-related chemokines including CCL15, CCL8, and CCL26. It could be known from the results that ZNF320 was negatively associated with CCL15 and CXCL2 while positively associated with CCL26 (Figure 9G). Therefore, ZNF320 constitutes an immunosuppressive microenvironment by affecting the expression levels of relevant chemokines, helping tumor cells to participate in immune escape, thereby enhancing tumor cell invasion and migration capabilities. These results indicated that ZNF320 could affect immune cell infiltration partly by regulating these chemokines expression.
Because ZNF320 expression was significantly correlated with immune infiltration, which is related to prognosis in HCC, we analyzed whether ZNF320 expression influences the prognosis of HCC by influencing immune infiltration. The prognosis was analyzed based on the expression level of ZNF320 in HCC related immune cell subgroup. As shown in Figure 10, the expression of ZNF320 in different B cell, CD4+ memory T-cells, CD8 + T cell levels had no significant correlation with the prognosis of HCC. Whether or not these immune cells are enriched or not high expression of ZNF320 leads to a poor prognosis for HCC patients (Figure 10A–10C). However, when Natural killer T-cells, Regulatory T-cells, Type 1 T-helper cells, and Type 2 T-helper cells are enriched, ZNF320 high expression leads to a poor prognosis in patients with HCC (Figure 10D–10G). These outcomes indicated that differential gene expression can affect patient outcomes at different levels of cell infiltration. In conclusion, these results proclaimed that ZNF320 was related to tumor cell infiltration in HCC.
Modification of m6A plays a significant role in HCC. By analyzing TCGA and ICGC HCC data, we detected the correlation between the expression of ZNF320 and the expression of 21 m6A related genes in HCC, and ZNF320 expression significantly positively correlated with ZNF320, RBMX, RBM15B, LRPPRC, YTHDF1, HNRNPC (Figure 11A, P<0.01) in the TCGA database. Furthermore, ZNF320 expression significantly positively correlated with RBM15B, LRPPRC, YTHDF1, HNRNPC in ICGC data sets (Figure 11B, P < 0.01). We divided TCGA samples into two groups according to the expression of ZNF320. We tried to exam the differential expression of genes related to M6A between the high and low ZNF320 groups. As shown in Figure 11, the m6A modification was not the same between high and low groups with the ZNF320 expression in HCC (Figure 11C). The intersection of genes with correlation coefficient greater than 0.39 with ZNF320 two databases was calculated, genes were presented in Venn’s diagram, including RBMX, YTHDF1, METTL3, IGF2BP2. (Figure 11D). The scattering plot shows the correlation between the expression of the genes related to ZNF320 and m6A (Figure 11E) Compared to the group of low expression, the expression of 19 genes in the high expression group of ZNF320 were increased (P <0.001). Then, we used Kaplan-Meier curve to reveal that the high expression of RBMX, YTHDF1, METTL3 were intensely associated with a poor prognosis of HCC (P<0.001) (Figure 11F). These results claim that ZNF320 may be closely related to the m6A modification of HCC, especially through its regulation with RBMX, YTHDF1, METTL3, which eventually influent the progression and prognosis of HCC.
Liver cancer is the fifth most common type of cancer worldwide, it is also the second leading cause of death from cancer [23]. Due to the symptoms are not evident during early stages, A huge number of HCC patients are found in the advanced stages. Although many treatment strategies have been used [24], the prognosis of HCC patients are still not satisfactory. Furthermore, advanced patients are not amenable for surgery. Therefore, there exists an imperative need for effective early diagnostic markers which may be able to assist the existing clinical diagnoses to enhance the HCC patients’ prognosis. In our study, we figured out ZNF320 as a new potential prognostic biomarker for hepatocellular carcinoma., and studied the relationship between ZNF320 and cell cycle, tumor-associated immune cells, m6a. With the purpose of detecting the expression and prognosis of ZNF320 in HCC, first through public database analysis, we noticed that ZNF320 expression in HCC tissues was highly increased compare with adjacent normal tissues, and we applied clinical samples to verify it. Secondly, we applied Kaplan-Meier survival analysis established that ZNF320 is one of the factors of poor overall survival and prognosis in HCC patients. At the same time, results showed that ZNF320 has been associated with many clinicopathological parameters, including tumor grade, tumor stage, and so on. Finally, univariate Cox analysis showed that ZNF320 was pointedly related to overall survival (OS). Variational calculation Cox analysis revealed that ZNF320 could be regarded as an independent predictor. At the same time, we explore the connection between ZNF320 methylation and ZNF320 expression through the cBioPortal dataset [25]. Results displayed that ZNF320 expression was positively associated with methylation (R = 0.1, p =0.022) in HCC, the methylation high levels of ZNF320 promoter in HCC were higher than that in normal tissue. Overall, these results manifested that ZNF320 is highly expressed in HCC, and the high expression of ZNF320 leads to a poor prognosis of HCC, the methylation with high level of ZNF320 promoter also result in a poor prognosis of HCC [26]. With the purpose of further exploring the function and mechanism of ZNF320 in HCC, we explored the co-expressed genes of ZNF320 in HCC through LinkedOmics, used GO and KEGG to perform functional analysis of the co-expressed genes, and performed GSEA analysis on ZNF320. The results indicated that the functional classification and KEGG pathway related to ZNF320 included: “FC epsilon pathway”, “FC gamma R mediated phagocytosis”, “cell cycle”, “WNT signaling pathway”, “pathway in cancer”, “leukocyte transendothelial migration”, “mismatch repair” and so on. In the results of GSEA, we noticed that ZNF320 can play a role by participating in DNA mismatch repair, so we deeply explored the correlation between ZNF320 and mismatch repair proteins, and the results showed that the expression of ZNF320 was associated with MSH2, MSH6, MLH1 and PMS2. All were positively correlated (Supplementary Figure 1A–1D), and we found that when MSH2 and MLH1 were highly expressed, HCC patients had a shorter survival time (Supplementary Figure 1E, 1F). It has also been reported before that mismatch repair-related genes are positively correlated with oncogene expression in HCC, and the role of oncogenes can affect the prognosis of patients. For example, MSH2 is positively correlated with oncogene expression [27], mismatch repair Repair-related gene EXO1 plays an oncogenic role in HCC [28], MSH6 is an up-regulated HCC staging-related gene, and high expression of MSH6 is positively correlated with 1-year recurrence of HCC [29], which is consistent with our results. In conclusion, the poor prognosis of HCC patients caused by the high expression of ZNF320 may be related to the high expression of MSH2, but whether ZNF320 leads to the poor prognosis of HCC through MLH1 and MSH2 needs more experiments to prove. However, more experiments are needed to prove whether ZNF320 leads to poor prognosis of HCC through MLH1 and MSH2. The main conclusion drawn from our research was that ZNF320 was closely related to cell cycle and immune infiltration. The cell cycle is a highly regulated process that enables cell growth, replication of genetic material, and cell division [30]. In cancer, the genetic control of cell division has undergone a fundamental change, leading to unrestricted cell proliferation. The deregulation of cell cycle was related to cancer occur through abnormal expression of protein especially at different levels of the cell cycle [31, 32]. Studies have reported that KIF18A promotes the proliferation, invasion, and migration of liver cancer cells by promoting cell cycle signaling pathways [33]. At the same time, recent studies have found that KIF23 has carcinogenic effects in HCC [34, 35]. CKAP5 encodes a cytoskeleton-related protein belonging to the TOG/XMAP215 family. CKAP is also known as colon and liver tumor overexpression gene protein [34]. We used PPI to identify the first 13 genes with the highest cluster scores related to ZNF320 gene, and it turned out that six genes including SPDL1, KIF18A, PRIM2, GINS4, KIF23, CKAP5. What’s more, all of these genes play an important role in cell cycle. In addition, we used GEPIA to analyze the connection between expression of ZNF320 and SPDL1, KIF18A, PRIM2, GINS4, KIF23, CKAP5 and conducted the Kaplan–Meier survival method to dissect their prognosis. The results showed that SPDL1, KIF18A, PRIM2, GINS4, KIF23, CKAP5 were related to the ZNF320 expression and the high expression of SPDL1, KIF18A, PRIM2, GINS4, KIF23, CKAP5 lead to a low overall survival rate. This indicated that ZNF320’s influence on the prognosis of HCC may be related to the cell cycle. What’s more, the disorder of the cell cycle is also related to the invasion, and migration of tumors. Through EdU and transwell assays, we found that interfering with the expression of ZNF320 could obviously inhibit the proliferation, invasion, and migration of HCC cells and downregulate the protein expression of CDK1, CDC20 and CCNB1. In summary, this experiment showed that down-regulation of ZNF320 can inhibit cell proliferation, invasion, and migration of HCC cells, which may be related to regulating the cell cycle signaling pathway. With the advent of immunotherapy [36, 37], a shift in the way of treating cancer was created [38, 39]. Tumor microenvironment (TME) is one of the key factors affecting the efficacy of immunotherapy [40, 41]. In recent years, research have indicated that immune therapy provides a survival benefit for liver cancer treatment [42, 43]. Therefore, researching the immune microenvironment and identifying potential immunotherapeutic targets become increasingly important in improving the effectiveness of immunotherapy in patients. To some extent, this study explored the relationship between the expression levels of ZNF320 and immune infiltration levels, various immune markers. Positive relationships were found between the expression levels of ZNF320 and infiltration levels of some immune cells. Consistently, we found that ZNF320 is significantly related to the gene marker set of B cells, T cells (general), CD8+ T cells, TAM, M1 macrophages, and M2 macrophage immune cells. In general, our study demonstrated that ZNF320 may take a part in the regulation of infiltration cells in the immune microenvironment in HCC. Studies have shown that CD8+ T cells can kill tumor cells TAM to help tumor cells participate in immune escape. Although ZNF320 and CD8+ T cell infiltration are associated, we found that ZNF320 is positively correlated with the immune checkpoint gene (Supplementary Figure 2), cannot produce tumor killing benefits, and is actually involved in immune escape. It was found through TISIDB that ZNF320 is positively correlated with CCL26 while negatively correlated with CCL15 and CXCL2. Moreover, interference with ZNF320 can significantly affect the expression of these chemokines. To further explore the contribution of ZNF320 to immunotherapy, we further investigated the association of ZNF320 with immune checkpoint-related genes, and the results showed that ZNF320 was significantly positively correlated with PD-L1, meaning that patients with high expression of ZNF320 may benefit from PD-L1 blocking therapy. From the above, it demonstrates that ZNF320 acts a pivotal part in regulating the immune cell infiltration in HCC. N6-methyladenosine, also called m6A, refers to the methylation of the sixth N position of adenylate in RNA, which mainly regulates RNA transcription, splicing, maturation, stability, translation, etc. The effect of m6A modification on human cancer has been confirmed in different cancer types [44], and the function of m6a modification on HCC has also received a lot of attention [45, 46]. Recent research have displayed that m6A modification have a significant role in HCC tumor, and many m6A-related genes can promote liver cancer progression [47, 48]. For example, YTHDF1 can promote the migration and invasion of HCC cells, and promote the proliferation of HCC cells by inducing EMT [49]. METTL3 is frequently upregulated in human HCC [50]. There is no research report on the communication between ZNF320 and m6A modification. Our results show that there is a strong correlation between ZNF320 and m6A. The 19 m6A-related genes showed differential expression when ZNF320 was overexpressed and under expressed. In addition, by constructing a Kaplan-Meier curve, it was found that compared to the low expression group, the survival time of the RBMX, YTHDF1, METTL3 high expression group was shorter. Our results show that the cancer-promoting impact of ZNF320 is associated with the modification of m6a, which may influent the mRNA methylation level of HCC through its association with YTHDF1, and finally impact the progress of HCC. In conclusion, our research suggests that ZNF320 may be a possible biomarker for poor prognosis of hepatocellular carcinoma. ZNF320 may not only take part in cell cycle regulation and affect the proliferation of HCC, but also may assume the role in the microenvironment of HCC by regulating tumor infiltrating immune cells. In addition, the results claim that ZNF320 may be closely associated with the m6A modification of HCC, which eventually influents the progression and prognosis of HCC. These put forward that ZNF320 may serve as a target for early clinical diagnosis and treatment which may have a chance to improve diagnostic and therapeutic options, and at the same time provides a reference for further exploration of new cancer immunotherapy. |
PMC9648796 | 36260873 | Xiaorong Liu,Zhaofeng Gao,Xiaoguang Wang,Yiyu Shen | Parthenolide targets NF-κB (P50) to inhibit HIF-1α-mediated metabolic reprogramming of HCC | 18-10-2022 | parthenolide,hepatocellular carcinoma,NF-κB,HIF-1α,glycolysis | We focus on investigating the role of Parthenolide (Par), a small sesquiterpenoid molecule, in hepatocellular carcinoma (HCC) and its effective target. Highly-metastatic HCC cells, MHCC97-H, were divided into the DMSO and the Par groups, of which the Par group was intervened at 5 and 10 mg/L doses. Cell viability was assessed by CCK-8 assay. Transwell chamber assay was performed to examine the metastatic and invasive abilities, while plate clone formation assay was conducted to detect the clone formation ability. For analysis of glucose uptake, glycolytic ability and lactate level, the glycolysis assay was employed. Brdu staining was performed to evaluate the cell proliferative potential. The P50 and HIF-1α levels were measured by immunofluorescence, while the expressions of p-P50 and HIF-1α were determined by Western-Blot. Small molecule–protein docking and Pull-down experiments were conducted to validate the Par–P50 binding model. After establishing the tumor-bearing mouse model, Par was administered by gavage to measure the tissue levels of P50 and HIF-1α, followed by plotting of tumor growth curves. Par could inhibit the metastatic, invasive and clone formation abilities of MHCC97-H cells, reduce the cell proliferative potential, and suppress the glycolysis, as manifested by down-regulated level of lactate and reduced oxygen consumption. Meanwhile, Par inhibited the HIF-1α expression. We found that after silencing P50, the HIF-1α was down-regulated, the glycolytic ability decreased drastically, and the cellular metastatic and invasive abilities were suppressed. After P50 knockout, the effect of Par intervention on the MHCC97-H cells was reduced. In HCC-bearing mice, Par also exhibited an excellent anti-tumor effect, decreasing the tissue levels of P50 and HIF-1α. This study discovers that Par can inhibit the HIF-1α-mediated glycolysis of HCC cells by targeting P50, thereby exerting an anti-tumor effect. P50 is a major effective target of Par. | Parthenolide targets NF-κB (P50) to inhibit HIF-1α-mediated metabolic reprogramming of HCC
We focus on investigating the role of Parthenolide (Par), a small sesquiterpenoid molecule, in hepatocellular carcinoma (HCC) and its effective target. Highly-metastatic HCC cells, MHCC97-H, were divided into the DMSO and the Par groups, of which the Par group was intervened at 5 and 10 mg/L doses. Cell viability was assessed by CCK-8 assay. Transwell chamber assay was performed to examine the metastatic and invasive abilities, while plate clone formation assay was conducted to detect the clone formation ability. For analysis of glucose uptake, glycolytic ability and lactate level, the glycolysis assay was employed. Brdu staining was performed to evaluate the cell proliferative potential. The P50 and HIF-1α levels were measured by immunofluorescence, while the expressions of p-P50 and HIF-1α were determined by Western-Blot. Small molecule–protein docking and Pull-down experiments were conducted to validate the Par–P50 binding model. After establishing the tumor-bearing mouse model, Par was administered by gavage to measure the tissue levels of P50 and HIF-1α, followed by plotting of tumor growth curves. Par could inhibit the metastatic, invasive and clone formation abilities of MHCC97-H cells, reduce the cell proliferative potential, and suppress the glycolysis, as manifested by down-regulated level of lactate and reduced oxygen consumption. Meanwhile, Par inhibited the HIF-1α expression. We found that after silencing P50, the HIF-1α was down-regulated, the glycolytic ability decreased drastically, and the cellular metastatic and invasive abilities were suppressed. After P50 knockout, the effect of Par intervention on the MHCC97-H cells was reduced. In HCC-bearing mice, Par also exhibited an excellent anti-tumor effect, decreasing the tissue levels of P50 and HIF-1α. This study discovers that Par can inhibit the HIF-1α-mediated glycolysis of HCC cells by targeting P50, thereby exerting an anti-tumor effect. P50 is a major effective target of Par.
Under sufficient oxygen, tumor cells still break down glucose through glycolysis to produce lactate. This phenomenon is referred to as the “Warburg effect”, which is also known as the “aerobic glycolysis” [1]. Tumor microenvironment hypoxia can activate glycolysis by suppressing oxidative phosphorylation and inhibit pyruvate catabolism and oxygen consumption [2]. Studies have demonstrated that HIF-1α can induce pyruvate dehydrogenase kinase 1 (PDK-1). By phosphorylating the E1 subunit of pyruvate dehydrogenase (PDH), PDK-1 further stimulates the PDH activity, resulting in the cytoplasmic accumulation of pyruvate [3]. HIF-1α can also increase the efficiency of glycolytic pathway by up-regulating the glucose transporters (GLUTs) and many other aerobic glycolysis-related genes [4]. Existing research has thus proven that HIF-1α is an important promoter of glycolysis. Since HIF-1α is also regulated by NF-κB transcription, it can be said that NF-κB-HIF-1α is the major regulatory signal of tumor glycolysis [5]. Parthenolide (Par), a small sesquiterpenoid molecule, has been found to possess a good inhibitory effect on multiple tumors. Its mechanism of action is associated with inducing apoptosis and enhancing tumor cytotoxicity [6]. However, its specific target requires further clarification. Through small molecule–protein docking, we found that Par might be a small regulatory molecule of P50. As an important subunit of NF-κB, P50 regulates the NF-κB activation. Hence, we further investigated the role of Par in a HCC model.
After thawing, Highly-metastatic HCC cells, MHCC97-H cell lines (Procell Life Science and Technology, Wuhan, China) were cultured in an incubator (37°C, 5% CO2) with 10% FBS-containing DMEM. Cell viability was assessed with trypan blue reagent. The H22 cells were divided into the DMSO and Par groups after reaching logarithmic phase. The Par group was intervened at 5 and 10 mg/L doses. The DMSO group comprised control cells, which was treated with 0.1% DMSO. We silenced P50 by RNAi technique, and transfected cells with siRNAs. The cells were divided into the DMSO and DMSO-RNAi groups for investigating the role of P50 in glycolysis. In further exploration of the correlation between P50 and Par, we divided the cells into the DMSO-RNAi and DMSO-RNAi+Par groups, where the intervention dose of Par was 10 mg/L.
In cell viability assay, the H22 cells were seeded into 96-well plates, and 100 μl of complete DMEM was added to each well. After cells were adherent, the medium was replaced with Par-containing one, which was added at 100 μl per well. Then, 24 h later, 10 μl of CCK-8 reagent was added to each well for further incubation. The optical density (OD) value was measured 4 h later, and the cell viability was statistically analyzed against the blank medium control.
Immunofluorescence staining for P50 and HIF-1α was performed. H22 cells were cultured on coverslips, and intervened for 24 h with Par-containing medium after adherence. Then, the cells were washed thrice in pre-cooled PBS, fixed in 4% formaldehyde at room temperature for 0.5 h, and permeabilized with 0.2% Triton X-100 for 5 min, followed by overnight incubation at 4°C with P50 and HIF-1α monoclonal antibodies (1:500 dilutions; Abcam, MA, USA). Thereafter, the cells were washed twice in PBS, incubated with fluorescent secondary antibody, and then mounted with 95% glycerol and observed under a fluorescence microscope.
H22 cells were seeded into 12-well plates, and intervened for 24 h with Par-containing medium after adherence. Then, the cells were washed in PBS, added with Brdu reagent (concentration: 30 μM), and incubated for 1 h in the dark. After removing Brdu, the cells were washed once with PBS, treated with 1 ml of ethanol, and then incubated for 30 min with Triton X-100 at room temperature, followed by washing once with 1% BSA. Afterwards, 1-h incubation proceeded in the dark using 10 μl of anti-Brdu reagent mixed with 0.5% Tween-20 and 1% BSA, and a subsequent incubation with secondary antibody. Finally, the cells were nuclear-stained with DAPI, mounted with 95% glycerol and observed under a fluorescence microscope.
After liquefying Matrigel (BD Biosciences, NJ, USA) overnight for 24 h at 4°C, it was diluted at a 5:1 volume ratio in serum-free medium, mixed well and placed into the upper layer of Transwell chamber, followed by cultivation in a 37°C incubator for 4–5 h to allow solidification of gelatinous layer. The cells were cultured after transfection. The logarithmic cells were suspended in serum-free medium, and then seeded into the upper chamber, while the lower chamber was added with 20% FBS-containing complete medium. After a further 24-h incubation with Par-containing medium, the upper layer cells were removed from the Transwell chamber, washed twice with PBS, fixed in neutral formaldehyde, stained with 0.1% crystal violet solution and microscopically observed for the number of invaded cells. The operational procedure for metastasis assay was identical to the invasion assay, where the pretreatment with Matrigel was unnecessary.
Logarithmic cells were collected from various groups, digested with 0.25% trypsin, pipetted into single cells, and suspended in 10% FBS-containing DMEM medium for subsequent use. The cell suspensions were diluted in multiples of gradient. Cells in various groups were separately seeded into a dish containing 37°C pre-warmed medium (10 mL) at a gradient density of 500 cells/dish, and gently rotated to allow uniform dispersion. This was followed by a 3-week cultivation of cells in a 37°C, 5% CO2 incubator with saturated humidity. The cultivation was terminated when macroscopically visible clones appeared in the petri dish. After discarding supernatant, the cells were washed carefully with PBS twice, and fixed in 5 mL of 4% paraformaldehyde for 15 min, followed by removal of the fixing solution. Transfection was performed with an appropriate amount of GIEMSA stain for 20 min, and then the staining solution was washed off slowly with running water, and the remaining was air-dried. Finally, clones were counted with the naked eye.
(1) Determination of intracellular glucose uptake was achieved by measuring the cellular uptake of 3H-2-deoxyglucose (Sigma). Briefly, the cells were cultured in 12-well plates and pre-incubated in glucose-free medium for 30 min. Then, 3H-2-deoxyglucose was added at 1 μCi per well to the cells, followed by 30 min of incubation. After washing with PBS, the cells were lysed with 1% SDS. Liquid scintillation counting was performed for radioactivity measurement of the cell lysates, and the final step was normalization of the radioactivity to the lysate protein concentrations. (2) For determination of intracellular glycolytic rate, the 5-3H-glucose conversion into 3H-H2O was monitored as formerly described. The first step was PBS washing of cells and a 30-min resuspension in glucose-free Krebs buffer (1 ml). Next, a 1-h resuspension of cells proceeded in Krebs buffer (0.5 ml) involving 5-[3H] glucose (5 mCi) and glucose (10 mM). Three aliquots, each 100 ml in volume, were shifted to PCR tubes (uncapped) containing 0.2 N HCl (100 ml), and each tube was shifted to a H2O (0.5 ml)-involving scintillation vial. This was followed by sealing of the scintillation vials and a 48-h reaction for diffusion purpose. Eventually, a liquid scintillation counter was utilized to assess the 3H diffusion, as well as the non-diffused 3H amount. (3) For determination of intracellular lactate production, cells were subjected to cultivation in fresh media (absent of phenol red) and 12–24 h incubation prior to the medium harvesting. Biovision assay kits were utilized for examining the lactate production as per the protocol of manufacturer, followed by normalization of the lactate level against cell number.
H22 cells were seeded into 6-well plates, and intervened for 24 h with Par-containing medium after adherence. All the cells were collected, washed twice with pre-cooled PBS, and lysed on ice for 30 min with NP-40 lysate (0.5 ml; Beyotime Biotechnology, Shanghai, China), followed by centrifugation for collecting total protein. Tumor tissues were ground with liquid nitrogen, and total protein was extracted with NP-40 lysate. Protein quantification was accomplished with BCA kit (Beyotime Biotechnology, Shanghai, China), and the protein concentration was adjusted. After SDS-PAGE gel electrophoresis and PVDF membrane transfer, the membranes were blocked for 2 h with 5% skimmed milk powder, and then incubated with p-P50 and HIF-1α monoclonal antibodies (TBST dilutions; Abcam, MA, USA). Following twice washing with TBST, a further incubation was carried out with HRP-labeled goat anti-rabbit IgG antibody (1:2000 dilution; Abcam, MA, USA). Finally, chemiluminescent immunoassay was performed, and OD was analyzed via Image Pro-Plus 6.0. The results were presented as OD comparisons between the target proteins and the internal reference (GAPDH).
Wild-type BALB/c mice were reared at the Animal Experiment Center of Jiaxing University. The murine experiments were approved by the Ethics Committee of Jiaxing University, which conformed to the animal ethics and welfare regulations. For establishment of HCC-bearing mouse model, the cultured H22 cells were collected at logarithmic phase, washed twice with PBS and density-adjusted to 5 × 105/ml. Each nude mouse was injected with 0.2 ml of cell suspension into the foreleg armpit. Then, rearing continued in a clean environment for observing the growth of mice and the formation of solid tumors. On around 15th–18th d, visible subcutaneous tumors appeared in mice. At this point, the mice were divided into the Control and Par groups. The Control group comprised naturally-grown tumor-bearing mouse. In the Par group, gavage administration was given at 5 and 10 mg/kg doses once daily for 15 consecutive d. The mice lived in a consistent environment, which were fed and watered ad libitum. Fifteen d later, the mice were killed by carbon dioxide asphyxiation and the tumor tissues were harvested.
Immunohistochemical (IHC) staining was performed to determine the P50 and HIF-1α levels. The tumor tissues were fixed in 4% paraformaldehyde, paraffin-embedded and sectioned. The resulting tissue sections were soaked in 1:50 acetone solution for 3 min, dried, and then treated with xylene and absolute ethanol. Following antigen retrieval in 0.01 mol/L citrate buffer at 92–98°C, the tumor tissue sections were treated with 3% hydrogen peroxide for 10–15 min to eliminate endogenous peroxidase, and then blocked with 5% BSA at 37°C for 15–30 min. Afterwards, the sections were incubated with Caspase-3 monoclonal antibody at 37°C for 1–2 h, and further with HRP-labeled avidin at 37°C for 20 min, followed by 3–5 min staining with DAB. Finally, the tissue sections were counterstained with hematoxylin, dehydrated, permeabilized and mounted with resin.
Mouse tumor tissues were treated with formalin for 0.5 h, dehydrated, permeabilized, paraffin-embedded and sectioned. Tissue sections were baked at 45°C for 2 h, and then treated with gradient concentrations of xylene and ethanol. After washing with water, the tissue sections were stained with hematoxylin for 10 min, rinsed with tap water and then treated with 1% hydrochloric acid-alcohol, followed by ethanol dehydration and eosin staining. Finally, the tissue sections were dehydrated, permeabilized, mounted with neutral gum and observed under an optical microscope.
P50 was searched in the Protein Data Bank (PDB ID: 1SVC). By setting up appropriate box centers (center_x = 81.143, center_y = 95.143, center_z = 93.644) and box grid parameters (size_x = 50, size_y = 60, size_z = 50) of the P50 receptor, the active pocket sites to which the small-molecule ligands might bind were covered. AutoDock Vina 1.1.2 was used to perform molecular docking between P50 receptor and Pra ligand small molecules. The hydrogen bond interaction between the receptor and the small molecules was visualized in 3D via PyMOL, while their hydrophobic interaction was visualized in 2D using Ligplus software. Recombinant P50 receptor (15 μg) and Biotin-labeled Pra (Biotin-Pra) were bound, and the recombinant protein G magnetic beads were incubated with P50 antibody. After washing with Tris buffer, P50 was determined as per the aforementioned Western-Blot procedure, and biotin detection was carried out with the HRP-conjugated antibiotin antibody (CST, MA, USA).
SPSS 20.0 was used to perform statistical analyses, and all measurement data were expressed as (). One-way ANOVA was used for comparison among multiple groups, while SNK test was employed to make pairwise comparisons. The differences were considered statistically significant when P < 0.05.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Our treatment at high and low doses found that Par could inhibit the viability of H22 cells. The cell viability was significantly lower than that of the DMSO group. Meanwhile, the cells in the high-dose group were less viable than those in the low-dose group (Figure 1A). Transwell assay revealed that Par could inhibit the metastasis and invasion of H22 cells in a dose-dependent manner (Figure 1B–1D). The results of clone formation assay also showed the ability of Par to inhibit the H22 clone formation, as manifested by the significantly reduced number of clones, which was lower than that in the DMSO group (Figure 1E). According to Brdu staining results, there were many positive cells in the DMSO group, indicating that H22 cells were in the proliferative phase. Par group exhibited significantly decreased number of positive cells as compared to the DMSO group. Par could inhibit the proliferation of H22 cells (Figure 1F). As suggested by glycolysis assay results, Par could inhibit the cellular absorption of glucose, lower the intracellular glucose level and decrease the lactate release (Figure 1G–1I). IFA revealed high expression levels of P50 and HIF-1α in H22 cells, with strong fluorescence intensities. Par could suppress the P50 and HIF-1α expressions, as manifested by the significantly weakened fluorescence intensity in the Par group than in the DMSO group (Figure 2A). Protein assay results also demonstrated that Par inhibited the expressions of p-P50 and HIF-1α (Figure 2B, 2C).
To explore the role of P50 in glycolysis, we silenced P50. In the DMSO-RNAi group, the invasive and metastatic abilities of cells weakened drastically, showing lower numbers of invasive and metastatic cells than the DMSO group (Figure 3A–3C). Meanwhile, the DMSO-RNAi group exhibited significantly reduced glucose uptake, intracellular glucose level and lactate expression as compared to the DMSO group (Figure 3D–3F). The clone formation assay also revealed decreased number of clones in the DMSO-RNAi group (Figure 3G). Proliferation assay found less number of Brdu-positive cells in the DMSO-RNAi group than in the DMSO group, suggesting inhibition of the cell proliferative potential (Figure 3H). Both IFA and protein assay revealed that after P50 silencing, the expression of HIF-1α could be suppressed (Figure 3J, 3K and 3I).
We treated the P50-silenced cells with Par, and found that Par was incapable of further inhibiting the cellular metastasis or invasion, nor could it inhibit glycolysis. In the Transwell assay, insignificant differences were found in the number of invasive or metastatic cells between the DMSO-RNAi+Par and the DMSO-RNAi groups (Figure 4A–4C). In the glycolysis assay, the glucose uptake, intracellular glucose level and lactate expression of the DMSO-RNAi+Par group differed insignificantly from those of the DMSO-RNAi group (Figure 4D–4F). Regarding the number of plate clones, the inter-group difference was also insignificant (Figure 4G). In Brdu staining, the positive cell count in the DMSO-RNAi+Par group differed insignificantly from that in the DMSO-RNA group (Figure 4H). Both IFA and protein assay revealed that after P50 silencing, Par exerted an insignificant effect on the HIF-1α expression (Figure 4J, 4K and 4I).
After establishing the tumor-bearing mouse model, we intervened it with Par. H&E staining revealed that Par had an impotent killing activity against tumor tissues, without obvious tissue inflammatory reaction or evident toxic effect, which differed little from the Control group (Figure 5A). After IHC staining, P50 and HIF-1α were found highly expressed in the Control group. Par inhibited the tissue expressions of P50 and HIF-1α in a dose-dependent manner. A positive correlation was present between the P50 and HIF-1α expressions (Figure 5B, 5C). In the tumor mass detection, Par was found to inhibit the growth of tumor, as manifested by significantly decreased tumor mass than that in the Control group (Figure 5D). Protein assay also revealed that Par inhibited the expressions of p-P50 and HIF-1α in a dose-dependent manner (Figure 5E, 5F).
Through small molecule–protein docking, we found that the binding energy of Par and P50 was −5.9 Kcal/mol. Par showed hydrogen bonding with THR, ARG and SER, as well as alkyl bonding with VAL and LEU. Small molecules bound near the phosphorylation sites (Figure 6A, 6B). In the pull-down experiment, we found that P50 could bind to Par (Figure 6C, 6D).
Glycolysis is the main energy source for tumor cells. Hypoxia, as a common feature of solid tumors, plays a pivotal role in tumor progression [8]. Otto Warburg et al. found that many tumor cells produce excessive lactate even under normoxia, which is known as pseudohypoxia [9]. Enhanced glycolytic activity has been shown to be closely associated with elevated level of HIF-1α. Subsequent research has also confirmed that the HIF-1α family is the primary mediator of change in energy metabolism of tumor cells from aerobic phosphorylation to aerobic glycolysis [10]. Mediated by HIF-1α, tumor cells up-regulate a range of genes related to the glycolytic metabolism, angiogenesis, tumor cell survival and erythropoiesis [11], including vascular endothelial growth factor (VEGF) [12], erythrogenin (EPO), glucose transporter (GLUT) [13] and some other glycolytic enzymes, ultimately promoting the Warburg effect. Thus, HIF-1α is one of the major promoters of the Warburg effect, which is also an important therapeutic target for abnormal glucose metabolism in tumors [14]. The expression of HIF-1α is regulated by NF-κB signaling. Hypoxia can also activate the intracellular NF-κB pathway [15]. NF-κB is a series of dimeric transcription factors composed of different subunits p65 (RelA), RelB, c-Rel, NF-κB1 (p105/p50) and NF-κB2 (p100/p52), which plays an important role in regulating cell survival and immune response [16]. Activated NF-κB in HCC cells has been found to promote the expressions of inflammatory cytokines (e.g., IL-6, IL-1β), which creates a cancer-promoting inflammatory microenvironment and is involved in the carcinogenesis and progression of HCC [17]. The interaction between HIF-1α and NF-κB may play a crucial role in maintaining the hypoxia response of cells [18]. According to previous reports, the NF-κB subunits p50 and p65 can stimulate the HIF-1α transcription [19]. Meanwhile, HIF-1α also participates in regulating the NF-κB activation. It can be said that HIF-1α and NF-κB are mutually interactive. Par is a sesquiterpene lactone isolated from Tsoongiodendron odorum Chun. and Chrysanthemum parthenium [20]. Domestic and foreign research has shown a strong in-vitro anti-tumor activity of Par, which has an anti-proliferative effect on multiple tumors, including colon cancer, liver cancer, cholangiocarcinoma, acute/chronic leukemia and multiple myeloma [21]. According to the latest research, Par can preferentially act on acute myelogenous leukemia (AML) stem/progenitor cells, and induce potent apoptosis of primary human AML cells and acute-phase cells of chronic myelogenous leukemia (CML) without affecting normal hematopoietic cells, which is more specific to leukemia cells than cytarabine [22]. In our study, we explored the effect and role of Par on the glucose metabolism reprogramming using a HCC model. H22 is a common invasive cell line. Par can inhibit the ability of H22 cells to metastasize and invade while suppressing relevant clone formation. This suggests certain inhibitory effect of Par on the proliferation and growth of H22 cells. Our Brdu results showed that Par reduced the positive cell count, which also corroborated our assumption. Detection of glycolytic ability found that Par could inhibit the cellular absorption of glucose, lower the intracellular level of glucose and reduce the lactate production [23], implying the association of Par’s anti-H22 effect with the glucose metabolism reprogramming. According to our P50 and HIF-1α measurements, Par inhibited the expressions of P50 and HIF-1α. To clarify the role of P50, which is an important protein of NF-κB and an upstream signal of HIF-1α [24], we silenced P50 in H22 cells and found that P50 silencing could inhibit the cellular metastasis and invasion, and suppress the glycolysis. RNAi treatment inhibited the glucose absorption and lactate production by cells, as well as the HIF-1α level. Hence, we confirm that P50 can regulate the expression of HIF-1α by adjusting cell glucose metabolism. Through small molecule–protein docking, we found that Par exerted its effect primarily by binding to P50. The Pull-down experiment also verified this point. Our Par treatment of RNAi-treated H22 cells showed that Par was no longer effective after P50 inhibition, with insignificantly down-regulated abilities of cellular metastasis and invasion and no change in glycolytic ability. This further proves that Par exerts its effect by targeting P50. In animal model, Par could inhibit tumor growth, as manifested by significantly smaller tumor mass than the Control group. Meanwhile, Par could also inhibit the cellular expressions of P50 and HIF-1α, showing agreement with the cell experiment results. All the above effects were dose-dependent. In H&E staining, Par showed insignificant toxic effect on tissues, which might be attributable to the dose. Thus, we are more certain that metabolic reprogramming is the primary anti-HCC mechanism of Par.
This study discovers that Par can target P50 to inhibit the expression of downstream HIF-1α, a key factor in tumor glycolysis. Hence, Par regulates the glycolysis of HCC cells by inhibiting NF-κB-HIF-1α pathway, ultimately inhibiting the HCC cell metastasis and invasion. P50 has potential as a novel therapeutic target for HCC, while Par can be a candidate drug for HCC treatment. |
PMC9648797 | 36309899 | Shanshan Chen,Yuan Zhan,Jinkun Chen,Jixing Wu,Yiya Gu,Qian Huang,Zhesong Deng,Xiaojie Wu,Yongman Lv,Jungang Xie | Identification and validation of genetic signature associated with aging in chronic obstructive pulmonary disease | 28-10-2022 | chronic obstructive pulmonary disease,aging,bioinformatics analysis,genetic signature | Aging plays an essential role in the development for chronic obstructive pulmonary disease (COPD). The aim of this study was to identify and validate the potential aging-related genes of COPD through bioinformatics analysis and experimental validation. Firstly, we compared the gene expression profiles of aged and young COPD patients using two datasets (GSE76925 and GSE47460) from Gene Expression Omnibus (GEO), and identified 244 aging-related different expressed genes (DEGs), with 132 up-regulated and 112 down-regulated. Then, by analyzing the data for cigarette smoke-induced COPD mouse model (GSE125521), a total of 783 DEGs were identified between aged and young COPD mice, with 402 genes increased and 381 genes decreased. Additionally, functional enrichment analysis revealed that these DEGs were actively involved in COPD-related biological processes and function pathways. Meanwhile, six genes were identified as the core aging-related genes in COPD after combining the human DEGs and mouse DEGs. Eventually, five out of six core genes were validated to be up-regulated in the lung tissues collected from aged COPD patients than young COPD patients, namely NKG7, CKLF, LRP4, GDPD3 and CXCL9. Thereinto, the expressions of NKG7 and CKLF were negatively associated with lung function. These results may expand the understanding for aging in COPD. | Identification and validation of genetic signature associated with aging in chronic obstructive pulmonary disease
Aging plays an essential role in the development for chronic obstructive pulmonary disease (COPD). The aim of this study was to identify and validate the potential aging-related genes of COPD through bioinformatics analysis and experimental validation. Firstly, we compared the gene expression profiles of aged and young COPD patients using two datasets (GSE76925 and GSE47460) from Gene Expression Omnibus (GEO), and identified 244 aging-related different expressed genes (DEGs), with 132 up-regulated and 112 down-regulated. Then, by analyzing the data for cigarette smoke-induced COPD mouse model (GSE125521), a total of 783 DEGs were identified between aged and young COPD mice, with 402 genes increased and 381 genes decreased. Additionally, functional enrichment analysis revealed that these DEGs were actively involved in COPD-related biological processes and function pathways. Meanwhile, six genes were identified as the core aging-related genes in COPD after combining the human DEGs and mouse DEGs. Eventually, five out of six core genes were validated to be up-regulated in the lung tissues collected from aged COPD patients than young COPD patients, namely NKG7, CKLF, LRP4, GDPD3 and CXCL9. Thereinto, the expressions of NKG7 and CKLF were negatively associated with lung function. These results may expand the understanding for aging in COPD.
Chronic obstructive pulmonary disease (COPD) is a progressive and debilitating respiratory disease that causes a heavy burden both medically and economically [1]. By 2050, the life expectancy of the global population will be extended, and about 15% of the world’s population will reach 70 years or more [2]. According to the Global Burden of Disease Study (the GBD study), the incidence and mortality of COPD patients over 70 years old are significantly higher than those of young patients. It can be discovered that aging increases the clinical incidence and treatment difficulty of COPD [3–6]. Therefore, a better understanding of the pathophysiology of COPD and knowing how the aging process is related to the COPD development are beneficial and necessary for the clinical treatment of this disease. Although the association between aging and COPD has been repeatedly proposed, the mechanism of this association remains unclear [5, 6]. Lopez-Otin et al. summarized 9 manifestations associated with aging, including genomic instability, telomere loss, epigenetic changes, loss of protein homeostasis, demodulating nutritional sensing, mitochondrial dysfunction, cell senescence, stem cell failure and changes in intercellular communication [7]. Studies have shown that the telomere length of patients with COPD is shorter than that of healthy people [8, 9]. In addition, cigarette smoke has been shown to induce the expression of aging markers in lung epithelial cells and fibroblasts, including senescence-related secretory phenotypes, p21 and p16 [10, 11]. However, although it is generally believed that there is a link between aging and COPD, the underlying mechanism is not clear [12–14]. In the current study, in order to explore the underlying mechanism between aging and COPD development, using gene expression profiles of COPD patients and COPD model mice, we analyzed the genetic signature related to aging and corresponding enrichment biological processes, as well as the validations in the clinical specimens.
To determine the aging-related core gene profiles of COPD patients, two microarray datasets were obtained from GEO. The information included in the study was listed in Table 1. According to age criteria, this study included 11 young patients with COPD and 66 aged patients with COPD, and the detailed information regarding the included subjects can be accessed in Supplementary File 1. Comparing the gene expression profiles of the two groups, 244 DEGs were generated, of which 132 were up-regulated and 112 were down-regulated (Figure 1A). In order to further understand the functions related to the 244 DEGs, GO enrichment analysis was carried out, including BP, MF and CC. In BP analysis, the DEGs were found to associate with “collagen fibril organization”, “extracellular matrix organization”, and “regulation of potassium ion transmembrane transport” (Figure 1B). In the CC category, the DEGs were related to the “extracellular space”, “extracellular region”, and “cell surface” (Figure 1C). Moreover, DEGs were enriched in the MF category related to “calcium ion binding”, “heparin binding”, and “protease binding” (Figure 1D). For KEGG pathway enrichment analysis, the top three significant KEGG pathways of the DEGs included “PI3K-AKT signaling pathway”, “Cytokine-cytokine receptor interaction”, and “Focal adhesion” (Figure 1E).
To further identify the expression characteristics related to aging in COPD model mice, we obtained a dataset from GEO and compared the DEGs between aged and young COPD mice, and the detailed information regarding the included subjects can be accessed in Supplementary File 2. A total of 783 DEGs were identified, of which 402 genes increased and 381 genes decreased (Figure 2A). Slightly different from the results obtained from patients with COPD, the top three BP terms were enriched in “extracellular matrix organization”, “cell adhesion”, and “inflammatory response” (Figure 2B). In the CC category, the DEGs were associated with “proteinaceous extracellular matrix”, “extracellular exosome”, “extracellular matrix”, “basement membrane”, and “extracellular region” (Figure 2C). “Calcium ion binding”, “actin binding”, “enzyme binding”, and “transcription factor binding” were the most important MF terms (Figure 2D). Furthermore, KEGG pathway analysis showed enrichments in “PI3K-Akt signaling pathway”, “Focal adhesion”, and “ECM-receptor interaction” (Figure 2E).
To comprehensively explore the gene expression profile in COPD related to aging, we combined the results of COPD patients and COPD model mice. As shown in the Venn diagram, six common DEGs were observed both in aged COPD patients and aged COPD mice (Figure 3A). The heat maps showed the gene expression profile of six aging-related genes in COPD patients and COPD mice (Figure 3B, 3C). Details of the six core genes were shown in Table 2.
The clinical characteristics of all subjects were displayed in Table 3. In all, 32 subjects were included in the current study containing 16 young COPD patients and 16 aged COPD patients. The subjects presented no obvious differences by gender, body mass index (BMI) and smoke status. The patients in aged COPD group had significantly lower FEV1% of predicted and FEV1/FVC, compared with those in the young COPD group. Meanwhile, the aged COPD group had a greater proportion of patients with higher GOLD grade (including grade II and III), relative to the young COPD group. Collectively, the aged COPD patients had worse lung function and more severe disease progression.
The expressions of the 6 aging-related DEGs (NKG7, CKLF, LRP4, MLF1, GDPD3 and CXCL9) were validated in the lung tissues from aged COPD patients and young COPD patients by qRT-PCR. Compared with young COPD patients, the expressions of NKG7, CKLF, LRP4, GDPD3 and CXCL9 were significantly enhanced in aged COPD patients, whereas there’s no marked difference in terms of MLF1 expression (Figure 4). Thereafter, the correlation analysis between lung function and the expression of confirmed core genes was performed. Thereinto, NKG7 and CKLF were negatively correlated with lung function (Figure 5).
Aging is a strong risk factor and independent prognostic biomarker for progressive COPD [15]. However, there is a lack of comprehensive analysis based on gene expression profiles to investigate the role of aging in COPD. In this study, using two human COPD datasets, we identified 244 aging-related DEGs in COPD patients. Similarly, through the analysis of the dataset of COPD model mice, we identified 783 DEGs related to aging. Subsequently, GO and KEGG analyses were conducted regarding these DEGs respectively. Finally, through the combination of DEGs between human and mouse datasets, we identified six common aging-related genes in COPD, of which five genes were confirmed to be up-regulated in clinical specimens, and the expressions of two genes were negatively correlated with lung function in COPD patients. In this study, 244 aging-related DEGs in COPD patients were involved in the regulation of potassium channel transport in BP function. Previous studies have also shown that potassium exchange was slower in patients with chronic obstructive pulmonary disease than in healthy individuals, taking at least 48 hours [16]. Qiunan Zuo et al. also found that COPD was related to “collagen fibril organization” and “extracellular matrix organization” [17]. Presynaptic components involved most of the top five enriched CC terms, indicating that the nervous system could play an important role in aged patients with COPD. Animal studies have shown that α 2-adrenoceptor agonists may help reduce airway responsiveness in chronic obstructive pulmonary disease, especially when nerve-mediated airway reflexes may be triggered [18]. In addition, a randomized controlled trial found that lumbar percutaneous nerve stimulation improved motor performance in patients with COPD [19]. Platelet growth factor is a significantly enriched MF term. Genes related to platelet growth factor activity were observed in elderly-related diseases [20]. KEGG analysis showed that the DEGs were enriched in PI3K-AKT signaling pathway. Some studies have shown that PTEN/PI3K/AKT can regulate the polarization of macrophages in emphysema mice [21] and induce apoptosis of alveolar epithelial cells in COPD by regulating autophagy through PI3K/AKT/mTOR pathway [22]. Extracellular matrix promotes the proliferation, migration and adhesion of airway smooth muscle cells in rat model of chronic obstructive pulmonary disease through up-regulation of PI3K/AKT signal pathway [22]. Different from the DEGs from COPD patients, the GO enrichment of DEGs in COPD model mice was significant in extracellular matrix (ECM) organization and inflammatory response. Sustained ECM accumulation was thought to be closely involved in the development of several diseases, including COPD, making it difficult to reverse the disease progression of these diseases [23, 24]. Although the relationship between ECM disorders and the aging process is widely recognized, the underlying mechanism remains unclear. The extensive induction of inflammation was revealed to be associated with the remodeling of the epigenome and transcriptome landscape of mouse aging [25]. Age-related chronic inflammation is the main cause of different chronic diseases with the increase of age [26, 27]. There is extensive evidence that persistent inflammation exists in the elderly and that age-related inflammation can occur in the lungs [28]. In our study, we identified six critical aging-related genes in COPD through the bioinformatic analysis with combination of human DEGs and mouse DEGs, and five out of these six genes were validated to be up-regulated in the lung tissues of aged COPD patients, of which NKG7 and CKLF presented remarkably negative associations with lung function. Meanwhile, these coincident genes were more or less related to inflammatory response. In specific, NKG7 was reported to be expressed in various immune cells including activated T cells and NK cells [29], and played important roles in multiple immune-related diseases, like controlling intratumor T-cell accumulation and activation, the cytotoxicity of lymphocytes and regulation of inflammation [30–32]. Future researches are required to validate the possible regulation of NKG7 in airway inflammation, the typical phenotype in COPD pathogenesis. Moreover, CKLF was a novel cytokine which had a crucial role in immune and inflammatory responses [33]. The enhanced CKLF expression was observed to associate with excess production of numerous inflammatory cytokines and graver autoimmune injury in rheumatoid arthritis and asthma [34, 35]. And meanwhile CKLF was also reported to be involved in chemokine activity and neutrophil chemotaxis [36, 37]. In addition, it has been reported that CXCL9 is also associated with chemokine activity and neutrophil chemotaxis [38, 39]. Many studies have proved that neutrophil chemotaxis is simultaneously one of the most important hallmarks and closely related to COPD development [40, 41]. Moreover, CXCL9 was an inflammatory cytokine that played an important role in multiple inflammatory diseases. CXCL9 has been reported to be upregulated in inflammatory colitis [42], and the increased CXCL9 expression was obviously associated with the survival and prognosis of patients with idiopathic pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary arterial hypertension (CTEPAH) [43]. Besides, LRP4 and GDPD3 were reported to exert important regulations in lung development. LRP4 is a regulator of Wnt signaling, regulating the differentiation of goblet cells during the lung development and repairment [44, 45]. And meanwhile, goblet cell metaplasia and mucus hypersecretion are the critical features of respiratory diseases, including COPD [46, 47]. In addition, tobacco smoke exposure can promote the disease progress by affecting the expression of Huntingtin-Interacting Protein 1 (HIP1), which could influence GDPD3 expression [48, 49]. Therefore, these 5 aging-related genes are more or less associated with COPD development. But the specific roles of these genes in COPD merit more investigations, which may provide new insights for seeking the therapeutic targets for aged COPD patients. There are still some limitations in this study. First, we cannot evaluate the role of other aging markers in COPD, such as telomere loss, epigenetic changes and genomic instability, which can be more beneficial to understanding the roles of aging in COPD development. While we only explored the role of aging in COPD from the perspective of related gene transcription. Secondly, the number of clinical samples included in our study is limited, and we need to confirm our conclusions in a larger COPD cohort. Third, we only verified the expression level of the differentially expressed aging-related genes in clinical samples, but did not explore the potential mechanism of these genes in COPD by in vitro cell experiments and in vivo mouse models. Therefore, on the basis of the above studies, considering the key role of these six aging-related genes, further research may focus on exploring the corresponding mechanisms underlying the association between aging and COPD. In summary, it is generally believed that aging is a dependent risk factor for COPD. From the point of view of gene expression profile, the pathogenesis of COPD in young patients and aged patients is not exactly the same. Through the combination of bioinformatic analysis, clinical experiment validations and correlation analysis with lung function, our results may provide new potential therapeutic targets for COPD patients with old age.
To identify the genes associated with aging in COPD patients, two microarray datasets (GSE76925 and GSE47460) were downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) [50, 51]. Young COPD patients were defined as ≤ 50 years old, and aged COPD patients were defined as ≥ 70 years old. To explore age-related genes in COPD model mice, we downloaded a microarray dataset (GSE125521) from GEO. In this dataset, the mice were divided into young group (4-month-old) and aged group (15-month-old) respectively. The details of the GSE datasets were summarized in Table 1. The preprocessing expression matrix and detector annotation files of three GSE datasets were obtained from the GEO repository. The official genetic symbols were used to list probes from different datasets. The multiple expression results of the gene were replaced by the median of the expression results. All the log2 folding changes (log2FC) of the expressed results were normalized using limma R packages [52]. Different expression genes (DEGs) were screened according to the cut-off values of p<0.05 and |log2FC|>0.5. Gene ontology (GO) function analysis and Kyoto encyclopedia of genes and genomes (KEGG) analysis were also. In order to identify the DEGs between the young and aged groups, the limma R packages (http://www.bioconductor.org/packages) was used to perform the negative binomial distribution method according to the two standard deviations where the absolute value of FC was greater than the median of FC. According to the hypergeometric distribution algorithm, the pathway enrichment analysis was carried out by querying GO biological process (BP), molecular function (MF) and cellular composition (CC) by DAVID (https://david.ncifcrf.gov). The same method was used in KEGG analysis. The cut-off value was p<0.05. Bioinformatics and Evolutionary Genomics (http://bioinformatics.psb.ugent.be/webtools/Venn) was used to select central or core genes.
All lung tissues in this study were recruited from Tongji Hospital, Wuhan, China, between 2019 and 2021. All the patients involved were negative for SARS-CoV2 examinations. Thereinto, the lung specimens were collected from patients who underwent surgical resection for pulmonary lump in the department of thoracic surgery. All included participants were classified into two groups based on age, namely young COPD (≤ 50 years) and aged COPD (≥ 70 years). COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2021 criteria. Patients with a post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity ratio of less than 70% were enrolled. Participants were excluded if they suffered from asthma, severe lung infections, or other obstructive lung diseases.
Total RNA of the lung tissue was extracted by RNAiso plus kit (TaKaRa) and reversely transcribed to cDNA using the cDNA RT-PCR Kit (Takara, Japan). The mRNA expression was assessed by real-time quantitative polymerase chain reaction (RT-qPCR) using TB GreenR Premix Ex TaqTM II (Takara, Japan) on BioRad CFX384 (Bio-Rad, CA, United States). The parameters were as follows: 40 cycles at 95° C for 10s, 59° C for 20s, and 72° C for 30s. Data were analyzed using the 2−ΔΔCt method with β-actin as control. The primers were as follow: NKG7 (5’-CCAGAAGCCCTGAGCTTATCCC-3’ and 5’-AGTGAGCACCCAGGCTCAGGG-3’), CKLF (5’-TCGCTTCGCAGAACCTACTCA-3’ and 5’-TATTTTCGGCTGCACGTTATCC-3’), LRP4 (5’-GCCGCCAAGTCATTATCT-3’ and 5’-TCAGCACCTTCCTCTTACT-3’), MLF1 (5’-TCGTTTTTCCAATCTGTCCGC-3’ and 5’-GATACTGAGCAAGTCTCTTCC-3’), GDPD3 (5’-GCCAGTCGGGCCTAAACAG-3’ and 5’- GTCCTCCAGACGAACCATGC-3’), CXCL9 (5’- TGCAAGGAACCCCAGTAGTGA-3’ and 5’- GGTGGATAGTCCCTTGGTTGG-3’) and β-actin (5’- AGAAAATCTGGCACCACACCT-3’ and 5’- GATAGCACAGCCTGGATAGCA-3’).
Data analysis was carried out by Rstudio (version 4.1.2) and GraphPad Prism 8 Software (GraphPad Software, San Diego, CA, United States). The microarray data were analyzed by different R packages. Results were expressed as mean ± SD or median (P25 quartile, P75 quartile). Statistical significance was determined using Student’s t-test for two groups. The correlations were analyzed by Pearson correlation. A two-sided p-value <0.05 was considered as statistically significant. |
PMC9648798 | 36227148 | Liana V. Basova,Nikki Bortell,Bruno Conti,Howard S. Fox,Richard Milner,Maria Cecilia Garibaldi Marcondes | Age-associated changes in microglia activation and Sirtuin-1- chromatin binding patterns | 10-10-2022 | aging,brain,rhesus macaques,microglia,Sirtuin-1 | The aging process is associated with changes in mechanisms maintaining physiology, influenced by genetics and lifestyle, and impacting late life quality and longevity. Brain health is critical in healthy aging. Sirtuin 1 (Sirt1), a histone deacetylase with silencing properties, is one of the molecular determinants experimentally linked to health and longevity. We compared brain pathogenesis and Sirt1-chromatin binding dynamics in brain pre-frontal cortex from 2 groups of elder rhesus macaques, divided by age of necropsy: shorter-lived animals (18-20 years old (yo)), equivalent to 60-70 human yo; and longer-lived animals (23-29 yo), corresponding to 80-100 human yo and modeling successful aging. These were compared with young adult brains (4-7 yo). Our findings indicated drastic differences in the microglia marker Iba1, along with factors influencing Sirt1 levels and activity, such as CD38 (an enzyme limiting NAD that controls Sirt1 activity) and mir142 (a microRNA targeting Sirt1 transcription) between the elder groups. Iba1 was lower in shorter-lived animals than in the other groups, while CD38 was higher in both aging groups compared to young. mir142 and Sirt1 levels were inversely correlated in longer-lived brains (>23yo), but not in shorter-lived brains (18-20 yo). We also found that Sirt1 binding showed signs of better efficiency in longer-lived animals compared to shorter-lived ones, in genes associated with nuclear activity and senescence. Overall, differences in neuroinflammation and Sirt1 interactions with chromatin distinguished shorter- and longer-lived animals, suggesting the importance of preserving microglia and Sirt1 functional efficiency for longevity. | Age-associated changes in microglia activation and Sirtuin-1- chromatin binding patterns
The aging process is associated with changes in mechanisms maintaining physiology, influenced by genetics and lifestyle, and impacting late life quality and longevity. Brain health is critical in healthy aging. Sirtuin 1 (Sirt1), a histone deacetylase with silencing properties, is one of the molecular determinants experimentally linked to health and longevity. We compared brain pathogenesis and Sirt1-chromatin binding dynamics in brain pre-frontal cortex from 2 groups of elder rhesus macaques, divided by age of necropsy: shorter-lived animals (18-20 years old (yo)), equivalent to 60-70 human yo; and longer-lived animals (23-29 yo), corresponding to 80-100 human yo and modeling successful aging. These were compared with young adult brains (4-7 yo). Our findings indicated drastic differences in the microglia marker Iba1, along with factors influencing Sirt1 levels and activity, such as CD38 (an enzyme limiting NAD that controls Sirt1 activity) and mir142 (a microRNA targeting Sirt1 transcription) between the elder groups. Iba1 was lower in shorter-lived animals than in the other groups, while CD38 was higher in both aging groups compared to young. mir142 and Sirt1 levels were inversely correlated in longer-lived brains (>23yo), but not in shorter-lived brains (18-20 yo). We also found that Sirt1 binding showed signs of better efficiency in longer-lived animals compared to shorter-lived ones, in genes associated with nuclear activity and senescence. Overall, differences in neuroinflammation and Sirt1 interactions with chromatin distinguished shorter- and longer-lived animals, suggesting the importance of preserving microglia and Sirt1 functional efficiency for longevity.
The aging process is associated with changes in a number of mechanisms maintaining physiology, subjected to the influence of genetics and life style, and leading to a range of outcomes that impact the quality of late life and longevity [1]. Brain health is a critical aspect of healthy aging. Neurological disorders have for several years remained as a leading cause of disability and the second leading cause of death globally [2], and an intrinsic problem of aging [2–4]. Normal aging includes reduction in the efficiency of DNA repair, inflammation, and changes in processes affecting neuronal circuitry [1]. Studies in animal models have suggested the beneficial contribution of genes that modulate lifespan by means that allow survival in conditions of energy availability [5]. In addition to the right genetic variants, acquired epigenetic control may also play a critical role. One of the genes that has been linked to longevity and successful aging, daf16, also known as FOXO, is responsive to the insulin growth factor 1 (IGF1) [6], but also to Sirtuin-1 (Sirt1) [7, 8], an epigenetic regulator, both regarded as molecular determinants of healthy aging. Sirt1 has gained attention as a type III deacetylase acting on proteins and chromatin histones, to regulate molecular functions and to silence gene transcription in the presence of nicotinamide dinucleotide (NAD+) [9–13]. Sirt1 deacetylates histones H3, H4 and H1 and more than 50 non-histone proteins, including transcription factors and DNA repair proteins [14]. Sirt1 properties contribute to preventing disease by reverting cellular senescence, maintaining genomic integrity and promoting longevity. Increased Sirt1 expression promotes survival in a mouse model of genomic instability and suppresses age-dependent transcriptional changes [15], including of inflammatory genes. In the brain, Sirt1 levels and function have shown to be compromised in neurodegenerative conditions, particularly the ones associated with aging [16, 17]. Sirt1 is also a key factor in blood brain barrier (BBB) integrity and permeability, both directly in microvascular endothelium and indirectly via microglia [18–20]. In infections of the Central Nervous System (CNS), including with Simian Immunodeficiency Virus (SIV) [21], which is a model of Human Immunodeficiency Virus (HIV) [22–25] triggering cellular senescence markers, a drastic decrease in Sirt1 levels and changes in its activity are detectable in isolated microglia cells [21, 26]. The changes in Sirt1 dynamics identified in SIV infection were similar to what was observed in the brain of uninfected macaques with advanced age [26]. In spite of the evidence of Sirt1 as a factor in successful aging, a comparative analysis in subgroups of aged subjects, with animals that differ in health, inflammation, and longevity, has never been previously performed. Sirt1 has been suggested as one of the mediators of the benefits of calorie restriction to longevity [27], associated to decreased intracellular nicotinamide (NAM) [28, 29] and increased levels of nicotinamidases that regenerate NAD+ levels [30], linked to energy metabolism [31]. Sirt1 deficits on the other hand, show increased inflammation, cellular stress, cancer, disrupted glucose and fatty acid metabolism, and unhealthy aging phenotypes [32]. Here, we have compared brain pathogenesis and Sirt1 dynamic chromatin binding differences in brain pre-frontal cortex (PFC) from elder macaques, divided in 2 groups based on the age of necropsy and health conditions. One group consisted of shorter-lived elder animals between 18 and 20 years old (yo), equivalent to 60-70 human yo. Another group consisted of longer-lived elder animals between 23 and 29 yo, corresponding to 80-100 human yo and modeling successful aging. These groups were also compared with young adult 4-7 yo macaques’ brains. We compared neuroinflammatory markers and factors that interfere with Sirt1 levels and activity in the prefrontal cortex (PFC), which is a critical area controlling cognitive functions, including sustained and selective attention, inhibitory control, working memory, and multitasking abilities, which are all impacted by aging [33–35]. Microglia and inflammatory markers included Iba1, CD163 and also CD38, an enzyme that regulates its cellular NAD substrate with consequences to Sirt1 functional activation [36, 37]. Blood brain barrier integrity was accessed by fibrinogen. We also measured transcription of mir142-5p, a micro RNA that targets Sirt1 gene transcription [21]. Sirt1 chromatin binding patterns were compared in total PFC between the two groups of aged rhesus macaques. This allowed the identification of networks of genes and biological processes that may influence longevity in a Sirt1-dependent manner.
SIV-negative, simian retrovirus type D-negative, and herpes B virus-free rhesus macaques with 4-7 years old, purchased from Valley Biosystems (West Sacramento, CA, USA) as controls to other studies, were included in the comparison of molecular and pathological findings across the lifespan. At necropsy, the young animals were terminally anesthetized, and perfused intracardially with sterile PBS containing 1 U/mL of heparin, prior to brain harvest. Young brain frontal cortex samples were frozen and formalin-fixed for histology and used in this study. The brains from 8 elder macaques with ages between 18 and 29 years old were kindly donated by the NIH National Institute of Aging Non-Human Primate Tissue Repository, at the Wisconsin National Primate Center, which is a source of archived tissue from aged nonhuman primates, collected under approved protocols. Upon tissue request, animals that were found dead were excluded to prevent issues with tissue quality. Frozen tissue and paraffin embedded pre-frontal cortex (PFC) sections were made available from animals subjected to necropsy following veterinary recommendations, euthanized using Beuthanasia D (Intervet/Merck Animal Health), under Wisconsin National Primate Center guidelines that are available in primatedatabase.org. The experiments performed in at the San Diego Biomedical Research Institute using primate brain tissues were exempt from Institutional Animal Care and Use Committee, on grounds of repurposing specimens from other approved protocols, and approved by the Institutional Review Board and Biosafety Committees at SDBRI, with Biological Hazard Registration (BHR #20-001-MCM), following National Institutes of Health guidelines. The characteristics of the animals can be visualized in Table 1.
RNA and miRNA were extracted from 0.5cm3 PFC tissue fragments, using RNeasy and miRNeasy kits, respectively. The qPCR primers for mir-142-5p, mir-142-3p and mir-34a were from the Qiagen miScript Primer Assay using U6 small nuclear RNA (snRNA) as housekeeping control. For qRT-PCR, the RT2 SYBR green qPCR master mix was used with Sirt1 primers and GAPDH was used as housekeeping control. All reagents were Qiagen.
ChIP was performed in ~0.6cm3 tissue fragments by Active Motif (Carlsbad, CA, USA). A ChIP reaction was carried out with 32ug of chromatin (pooled 8ug from each animal per group in duplicate) and anti-Sirt-1 antibody (Millipore). The ChIP DNA was processed into an Illumina ChIP-Seq library and sequenced +/10000 bb, to generate >2 million reads, which were aligned to the M.mulatta genome annotation (MacaM/December 2019 assembly) and >15 million unique aligns (removed duplicates) were obtained. A signal map showing fragment densities along the genome was visualized in the Integrated Genome Browser (IGB) and MACS peak finding was used to identify peaks. Control data was derived from 5.1 million (positive control) and 5.8 million (negative control) alignments. With default settings, 307 Sirt-1 meaningful peaks genome-wide consistent to promoter regions in all samples, were identified. Raw data and metadata are available at GEO GSE95793.
Pathway assignments and functional annotations were analyzed using DAVID Bioinformatics Database [38]. To complete the bioinformatics analysis, two knowledge base resources were queried: the Ingenuity Knowledge Base [39] and interaction repositories based on cpath [40–42] containing interactions that have been curated by GeneGo (http://www.genego.com), the Kyoto Encyclopedia of Genes and Genomes (KEGG - http://www.genome.jp/kegg/), and Ingenuity. Benjamini False Discovery Rate (FDR) adjusted values <0.01 and p values < 0.05 (provided by DAVID) were utilized as conservative filters for identification of true values. Cluster analysis and networks were obtained and visualized using Cytoscape 3.9.1 [43]. Pathway and genetic interaction-based connections between significantly different genes were assembled and visualized using GeneMania. Active pathways were identified using JActive Modules based on score and low (0.8) overlap threshold.
Formalin-fixed, paraffin embedded brain tissue sectioned in 7u slices was used for the detection of molecular markers using antibodies against Iba-1 (AIF1 - WAKO, Richmond, VA, USA), CD163 (Invitrogen), Fibrinogen (Millipore, Temecula, CA, USA) and CD38 (Novus Biologicals, Centenial, CO, USA), using standard procedures [44]. The incubation with biotinylated secondary antibodies (Vector Labs, Burlingame, CA, USA) was followed by Streptavidin-HRP, and development was performed using NovaRed (Vector Labs), and counterstaining with Gill’s hematoxylin.
Results are expressed as Mean ± SD. One-way analysis of variance with Bonferroni post hoc test and Student’s t test were performed in Prism 8 (GraphPad Software LLC). Pearson analysis, graph builder properties and full factorial analysis were performed in JMP Pro15. P < 0.05 was considered significant.
Paraffin-embedded tissue from animals in Table 1 was used for identifying differences in the expression of microglia markers using IHC (Figure 1). Iba-1 (AIF1), and CD38 were measured and quantified using Image J. We found that PFC from elder animals, regardless of group assignment, differed significantly from young PFC (Figure 1). However, these differences occurred in different ways within the aged group, in an age-dependent fashion. For instance, shorter-lived elder animals, which died between 18 and 20 years of age, showed a significantly smaller number of Iba1+ cells compared to young ones, and to longer-lived animals, indicating that severe microglial loss was a characteristic of the group with shorter lifespan (F2,12=189.2, p<0.0001). Microglia morphology and Iba1 quantification in longer-lived elder animals indicated some impact of age, with significant although less severe microglial loss compared to shorter-lived animals. The results of Iba1+ cell morphology and density suggested that the ability to maintain the microglia population may be important for longevity. CD38 is a marker of immune activation and a NAD limiting factor. We found an effect of age on the expression of CD38 measured by IHC on PFC sections (F2,12=4.995, p=0.0347). This marker expressed at higher levels in all elder animals compared to young controls (Figure 2D). Young animals had few CD38+ cells associated with vessels and few diffuse in the parenchyma (Figure 2A). Although the two elder groups expressed similar CD38 intensity levels (p=0.998), which were higher than young (p=0.05), distribution patterns differed significantly between them (Figure 2B–2D). In shorter-lived elders, CD38+ cells were strongly stained and were mostly clustered in perivascular foci, associated with signs of edema and tissue damage. On the other hand, in longer-lived animals these cells were diffuse, some perivascular, but no severe pathology. CD163 expression in myeloid cells characterizes response to inflammation and was restricted to the perivascular domain (Figure 2E–2G). Although blood vessels were enlarged in both elder groups, shorter-lived animals had significantly more detectable CD38+ cells compared to young, while in longer-lived animals the increase was not significantly different from the other groups (Figures 2H). The loss of integrity of the BBB is a critical component of aging, which can be detectable in tissue sections by the staining against fibrinogen, which is normally maintained within blood vessels by strong endothelial junctions [45]. Fibrinogen staining marked the microvasculature and, when found in the extravascular space, it indicated loss of BBB integrity and leaks (Figure 3). In both elder groups, extravascular fibrinogen was occasionally detected, showing leaks from blood vessels to the brain tissue (Figure 3B, 3C). Larger leaks were observed in the longer-lived group (Figure 3C), compared to the shorter-lived group (Figure 3B).
We previously identified mir142 as a critical contributor to the collapse of Sirt1 transcription and function in macaques that develop neuropathology as a result of infection with the Simian Immunodeficiency Virus [21]. Given the role of Sirt1 in aging, we examined the transcription of mir-142-5p, mir-142-3p and Sirt1 genes in mRNA extracted from the PFC of all young and long-lived aged animals, which were made available to us by the NIA Non-Human Primate Tissue Repository (n=4/group). Mir-142-3p was not detectable (data not shown). Regarding mir-142-5p, shorter-lived elder animals did not differ in its transcription compared to young animals, while longer-lived ones showed significantly higher transcription compared to both young and shorter-lived elders (Figure 4A). Sirt1 transcription was decreased in both elder groups compared to young, however shorter-lived elders had a significantly lower Sirt1 expression compared to longer-lived animals (Figure 4B), indicating a correlation between longevity and maintenance of Sirt1 transcriptional activity.
In order to estimate the link between longevity, Sirt1 transcription and chromatin binding activity, we examined differences in Sirt1 dynamics and binding to chromatin in both groups of elder animals. The comparison and analysis of Sirt1 binding patterns and target genes was indicative of epigenetic silencing activity and signatures associated with longevity and maintenance of microglia cells, in spite of inflammation and vascular leaks. In spite of higher transcription of Sirt1, the absolute number of Sirt1 peaks was reduced in long-lived aged (>23yo) animals compared to old (18-20) (Table 2). However, the distribution of peaks indicated a smaller diversity in the genes regulated by Sirt1 in long-lived animals compared to old, characterized by gaps in the genomic intervals presenting Sirt1 peak reads in long-lived animals (Figure 5A). On the other hand, the detailed analysis of intervals also indicated and enrichment of in-gene binding sites in old animals compared to long-lived (Figure 5B). The implications of these differences are unknown but may reflect more frequent disruptions in the transcriptional process occurring in old PFC, in addition to the active silencing at regulatory regions. Figure 5C shows an example of these functional amendments in the AIF1 gene that encodes Iba1, indicating a concentration of Sirt1 peak signal on promoter and regulatory regions (resembling controls [26]) in long-lived animals, and a spread of Sirt1 signal in the same gene in old PFC. A detailed analysis of changes in Sirt1 binding between PFCs from shorter- and from longer-lived elder animals, regardless of position, indicated differences in the activity of Sirt1 on genes strongly associated by pathway (Figure 6), visualized as fold change (old/long-lived) in Genemania and analyzed using JActive Modules for the identification of active pathway connections between gene clusters with low overlapping threshold. Two main modular networks were identified (Figure 6). Module A connected 90 genes with a score 5.9 (Figure 6A). Module B connected 99 genes with a score 5.52 (Figure 6B). The enrichment in Sirt1 activity in these networks may signify processes that are actively disrupted or downregulated in shorter-lived animals compared to longed-lived elders, and vice-versa. Module A (Figure 6A) contained 47 out of 90 genes that interacted through pathway which exhibited Sirt1 binding activity significantly increased above 1.5-fold in shorter- compared to longer-lived, indicating that they are more likely to being silenced in shorter-lived animals but active in longer-lived ones. These genes were annotated to transferase molecular functions (p=0.0063), mitochondrion as a cellular component (p=0.0068), and several biological processes, including transport (p=0.0011), heme biosynthesis (p=0.0033(p=0.0046) and protein transport (p=0.0052). KEGG pathway assessments indicated that these genes were involved in EGFR tyrosine kinase inhibitor resistance, endometrial cancer, longevity regulation, prolactin signaling, HIF-1 signaling, Neurotrophin signaling, Thyroid hormone signaling, FoxO signaling, cellular senescence, and the JAK/STAT pathway (Table 3). In the same module A, 17 genes had a -1.5 decrease in Sirt1 binding in shorter- versus longer-lived animals, indicating that these genes are more active in shorter-lived animals, but being more tightly regulated in longer-lived PFCs. These were annotated to pathways such as RNA binding molecular functions (p=0.0035), nucleus as a cellular component (p=0.039), and mRNA processing biological process (p=0.0058). Module B contained 57 out of 99 genes interacting through pathway (Table 4) which exhibited Sirt1 binding activity increased above 1.5-fold, indicating that they may be silenced in shorter-lived PFCs but active in longer-lived ones. Mitochondrion was annotated as a cellular component (p=0.011). Biological processes associated with these genes (Table 4) were protein kinase activity (p=0.009), heme biosynthesis (p=0.04) and iron transport (p=0.049). The EGFR tyrosine kinase biosynthesis was the only pathway annotation identified for these genes (p=0.02). In module B, 19 genes had a -1.5 decrease in Sirt1 binding, indicating that they were active in shorter-lived animals but being likely regulated in longer-lived PFC. Similar to module A, these were annotated to nucleus as a cellular component (p=0.008), with differentiation as a biological process (p=0.0052). The genes with highest Sirt1 peaks in shorter-lived PFCs compared to longer-lived ones (most likely silenced) included Myosin 1E (MYO1E, 4-fold), Kruppel-like factor 9 (KLF9, 3.75-fold), Forkhead box O3 (FOXO3, 2.86-fold) and Interleukin 6 Receptor (IL6R, 3.6-fold). Genes with highest Sirt1 peaks in longer-lived animals compared to shorter-lived (indicating likely suppression in longer-lived) included the ribosomal protein 13a (RPL13A, 0.13-fold), the ribosomal protein S11 (RPS11, 0.13-fold), the matrix metallopeptidase 19 (MMP19, 0.19-fold), Tropomodulin 4 (TMOD4, 0.19-fold), N-acylneuraminate cytidylyltransferase (CMAS, 0.23-fold) and Cyclin Y (CCNY, 0.25-fold), associated with RNA binding (p=0.011) and acetylation processes (p=0.053), all regulated by Sirt1. Of interest to the regulation of the longevity process, FOXO3 (2.86-fold) and AKT serine/threonine kinase (AKT1, 1.9-fold) had Sirt1 peaks in shorter- versus longer-lived animals. The TSC complex subunit 1 (TSC1) and cAMP responsive element binding protein 3 (CREBP3) had significantly less (0.7-fold) Sirt1 binding in shorter- versus longer-lived PFCs.
Differences in the Iba1 microglia compartment between shorter – and longer-lived PFC and compared to young indicated that microglia loss may be a component affecting longevity. Shorter-lived animals not only had significantly less microglial cells than longer-lived and young, but also showed more signs of tissue damage with edema and perivascular CD163+ cells, as well as lower transcription of Sirt1. Interestingly, both elder groups had similar levels of CD38+ cells, which was higher than young, and BBB leaks. Microglia activation and BBB integrity both contribute to small vessel disease that is commonly found in aging, although not necessarily in an interdependent manner [46]. However, microglial activation has been observed around vascular leaks, including in models that replicate the aging brain such as mild hypoxia [47]. Importantly, microglial depletion drastically increases loss of tight junction proteins that characterize vascular integrity, largely aggravating leaks [48]. Microglia cells may interact with extravascular fibrinogen to promote protective signals [48], balancing pro-inflammatory responses. Paradoxically, occasional but larger leaks were observed in longer-lived animals, which had microglial cell at levels that were higher than shorter-lived ones. This observation could support a role for fibrinogen and microglia signal interactions on maintaining the homeostasis of brain cell populations and BBB integrity. Whether microglial cell numbers and Sirt1 transcription and activity determine protection and survival outcomes, remains to be addressed. The CD38+ cells, previously linked to neurodegenerative and neuroinflammatory insults of aging [49], were present in both elderly groups at levels that were higher than in young PFC. Yet, their distribution differed considerably, being associated to perivascular edema and tissue damage in shorter-lived animals, but diffuse and lightly stained in longer-lived animals. CD38 can be expressed by both T cells and macrophages, playing a critical role in pro-inflammatory responses strongly linked to its enzymatic activity over NAD, and as a prognosis of pathogenic outcome [50, 51]. Importantly, NAD serves as a neuroprotective agent [36] by activating Sirtuins activity [52]. The regulation of Sirt1 and its gene silencing functions, can result from both transcriptional changes, as well as changes impacting its functional dynamics. We have previously described the role of mir-142 in controlling Sirt1 transcription in microglia [21]. However, the results indicate that mir-142 may be one of the factors controlling Sirt1 transcription. For instance, longer-lived animals had higher levels of mir-142, yet Sirt1 transcription was lowest in shorter-lived animals. Other microRNAs have been suggested to regulate Sirt1, such as mir-34a [53], which occurred at low levels in these specimens and did not differ between groups (data not shown). Sirt1 transcription could be influenced by transcription factors not addressed in this study, such as E2F1 and HIC1, particularly in conditions of oxidative stress [54, 55]. The comparison between elderly groups of Sirt1 chromatin binding sites and frequency in bulk PFC tissue by ChIP-seq served as an indirect measure of epigenetic function and silencing activity. Chromatin binding peaks differed significantly in quality, as well as in numbers, between the two elderly groups indicating divergent patterns and regulated processes. The comparison between PFC of shorter-lived animals, young (4-7yo) controls, as well as young rhesus macaques infected with SIV, has been previously described by us [26]. That comparison indicated that a decrease in Sirt1 activity in conditions of SIV infection was similar to age [26]. The comparison of aging subgroups performed here indicated qualitative differences associated with longevity outcomes in uninfected animals. Interestingly, shorter- and longer-lived animals showed Sirt1 peak differences in genes associated with aging, metabolism and nuclear activity. Sirt1 binding to in-gene sequences was significantly enriched in shorter-lived elder animals, with no changes in upstream and downstream activity was similar between the elder groups. The implications of in-gene silencing are not well defined. It is possible that this may serve as a mechanism of transcriptional disruption, generating truncated or non-functional RNA, in addition to the active silencing in promoters and regulatory intron regions, and resulting in transcript degradation [56]. Whether this is a factor that results from or contributes to the lack of microglia cells, remains to be addressed. Although Sirt1 activity was concentrated in genes involved in aging pathways (HIF, senescence, longevity, etc.), interesting differences were detected in individual genes between the two elder groups. Shorter-lived animals, for example, had strong Sirt1 enrichment in the IL6R gene, indicating its silencing. This could be a consequence of lower microglia cell numbers, or defective inflammatory responses. Enrichment patterns indicate that longer-lived animals have Sirt1 activity in RNA binding and acetylation genes, but lower activity in nuclear and differentiation pathways. Microglia are the first cells to populate the brain during development [57, 58], preceding neurogenesis and the formation of BBB [59, 60]. RNAseq studies have suggested that subsets of microglia become more prevalent with age, which may be associated with protection [61]. This study has limitations due to the small number of animals. Moreover, the differences between elderly groups could be linked to clinical observations leading to necropsy and to post-mortem findings. For instance, long-lived animals had compromised bones and joints, and internal adhesions, but did not have tumors, diabetes or signs of metabolic disorders, which were rather found in the shorter-lived elderly group. Thus, microglial loss could also result from systemic disease more frequently observed in shorter-lived animals. Age-matching controls, particularly to the shorter-lived animals’ group was not available, limiting conclusions. Yet, it is unlikely that the loss of microglia is just a transient age-effect, while the maintenance of microglia numbers in animals that lived longer suggests this may be a hallmark of healthy aging. The correlation between microglia and age was striking, suggesting that supporting this population may be critical to longevity. Successful aging is largely associated with preserved cognition [62], for which data is not available in the animals studied here. This work suggests that preservation of microglia and Sirt1 binding activity and patterns, as well as the directionality of pathways regulated by Sirt1, are prognostic of long-living. This work suggests the importance of microglia and epigenetic cross-talk in aging processes, influenced by and influencing pathogenesis. |
PMC9648799 | 36309912 | Xiao-Lin Jiang,He Tai,Jin-Song Kuang,Jing-Yi Zhang,Shi-Chao Cui,Yu-Xuan Lu,Shu-Bo Qi,Shi-Yu Zhang,Shun-Min Li,Jian-Ping Chen,Xian-Sheng Meng | Jian-Pi-Yi-Shen decoction inhibits mitochondria-dependent granulosa cell apoptosis in a rat model of POF | 27-10-2022 | Jian-Pi-Yi-Shen,premature ovarian failure,mitochondrial dysfunction,granulosa cell,apoptosis | As a widely applied traditional Chinese medicine (TCM), Jian-Pi-Yi-Shen (JPYS) decoction maybe applied in curing premature ovarian failure (POF) besides chronic kidney disease (CKD). In vivo experiments, 40 female SD (8-week-old) rats were randomized into four groups, namely, control group (negative control), POF model group, JPYS treatment group, and triptorelin treatment group (positive control). JPYS group was treated with JPYS decoction (oral, 11 g/kg) for 60 days, and the triptorelin group was treated with triptorelin (injection, 1.5 mg/kg) for 10 days before the administration of cyclophosphamide (CTX) (50 mg/kg body weight) to establish POF model. We examined apoptosis, mitochondrial function, and target gene (ASK1/JNK pathway and mitochondrial fusion/fission) expression. In vitro experiments, the KGN human granulosa cell line was used. Cells were pretreated with CTX (20, 40, and 60 μg/mL) for 24 h, followed by JPYS-containing serum (2, 4, and 8 %) for 24 h. Thereafter, these cells were employed to assess apoptosis, mitochondrial function, and target gene levels of protein and mRNA. In vivo, JPYS alleviated injury and suppressed apoptosis in POF rats. In addition, JPYS improved ovarian function. JPYS inhibit apoptosis of granulosa cells through improving mitochondrial function by activating ASK1/JNK pathway. In vitro, JPYS inhibited KGN cell apoptosis through inhibited ASK1/JNK pathway and improved mitochondrial function. The effects of GS-49977 were similar to those of JPYS. During POF, mitochondrial dysfunction occurs in the ovary and leads to granulosa cell apoptosis. JPYS decoction improves mitochondrial function and alleviates apoptosis through ASK1/JNK pathway. | Jian-Pi-Yi-Shen decoction inhibits mitochondria-dependent granulosa cell apoptosis in a rat model of POF
As a widely applied traditional Chinese medicine (TCM), Jian-Pi-Yi-Shen (JPYS) decoction maybe applied in curing premature ovarian failure (POF) besides chronic kidney disease (CKD). In vivo experiments, 40 female SD (8-week-old) rats were randomized into four groups, namely, control group (negative control), POF model group, JPYS treatment group, and triptorelin treatment group (positive control). JPYS group was treated with JPYS decoction (oral, 11 g/kg) for 60 days, and the triptorelin group was treated with triptorelin (injection, 1.5 mg/kg) for 10 days before the administration of cyclophosphamide (CTX) (50 mg/kg body weight) to establish POF model. We examined apoptosis, mitochondrial function, and target gene (ASK1/JNK pathway and mitochondrial fusion/fission) expression. In vitro experiments, the KGN human granulosa cell line was used. Cells were pretreated with CTX (20, 40, and 60 μg/mL) for 24 h, followed by JPYS-containing serum (2, 4, and 8 %) for 24 h. Thereafter, these cells were employed to assess apoptosis, mitochondrial function, and target gene levels of protein and mRNA. In vivo, JPYS alleviated injury and suppressed apoptosis in POF rats. In addition, JPYS improved ovarian function. JPYS inhibit apoptosis of granulosa cells through improving mitochondrial function by activating ASK1/JNK pathway. In vitro, JPYS inhibited KGN cell apoptosis through inhibited ASK1/JNK pathway and improved mitochondrial function. The effects of GS-49977 were similar to those of JPYS. During POF, mitochondrial dysfunction occurs in the ovary and leads to granulosa cell apoptosis. JPYS decoction improves mitochondrial function and alleviates apoptosis through ASK1/JNK pathway.
Premature ovarian failure (POF) is a gynecological disease that associates with many complications, and it has undesirable effects on fertility and quality of life in women. POF incidence rises with age increasing and continues to rise annually [1]. Women with POF show infertility, reproductive organ atrophy, neurological/urogenital system dysfunction, cardiovascular risk, and osteoporosis. POF can be induced by chromosomal abnormalities, Fragile-X premutations, autosomal mutations, iatrogenic injuries, such as that caused during chemotherapy and radiotherapy, enzyme inactivity, autoimmune disorders, and unknown etiologies [2]. With the elevated incidence of cancer in young women, the incidence of chemotherapy-induced POF has also increased. Different chemotherapeutic drugs have different mechanisms of action. As an alkylating agent that cross-links DNA, thereby disrupting the cell cycle, cyclophosphamide (CTX) is highly toxic to the ovaries. CTX up-regulates Bax level and down-regulates Bcl-2 level, which can decrease the MMP, disrupt deliver of Cyt-c, and then affect the production of ROS, thereby causing apoptosis [3, 4]. Few methods are available to preserve the ovarian reserve, such as GnRH-a injection and cryopreservation (oocyte), but these methods are far from perfect in that only 30% of transferred embryos result in clinical pregnancy [5] and only 23 % of transferred embryos result in a live birth [6]. Jian-Pi-Yi-Shen (JPYS) decoction is a mixture of two traditional Chinese medicines, namely, Yu-Ping-Feng-San (YPFS) and Da-Huang-Gan-Cao-Tang (DHGCT). The use of YPFS was recorded in Dan Xi Xin Fa by Dan-xi Zhu, and it was used to complement “Qi”. The use of DHGCT was kept in Jin Gui Yao Lue in Han Dynasty, and it was used to remove excessive fluid or static blood through the bowels. As such, POF can be treated by YPFS to replenish “Qi” and by DHGCT to induce purgation. For the past two decades, JPYS has been clinically prescribed as a basic decoction for curing chronic kidney disease (CKD) [7]. According to the teachings of TCM, oocytes originated from the kidneys, so the pathogenesis of POF is associated with an inadequacy in kidney essence. According to these teachings, JPYS can “resolve stasis and activate blood” and “tonify the kidney and fortify the spleen” [7]. As a Chinese traditional medicine, JPYS is comprised of 8 medicinal herbs in Table 1 [8]. In summary, JPYS decoction can used in curing POF.
JPYS decoction (Beijing Kang-ren-tang Pharmaceutical Co., Ltd in China) were dissolved in deionized water (60° C) to acquire stock solution (1.1 g/mL). The stock solution was conserved in 4° C refrigerator. CTX, triptorelin, and selonsertib were obtained from Solarbio Life Sciences Co., Ltd. (China).
JPYS contains eight different medicinal herbs (Table 1). HPLC combined with UPLC-QQQ-MS were used to detect main chemical components of JPYS in previous research, 71 compounds were tentatively identified in JPYS and then using HPLC–QQQ–MS/MS to further qualitative and quantitative 12 compounds [8]. 5 μL injection quantity, 20° C, 0.3 ml/min flow rate) (HPLC) and authenticated with standards. The process was operated in both positive and negative ionization forms (Scan: 100-1500 Da; Fragment: 80-185 V; eV: 4-80 eV). The data were analyzed via SCIEX OS software. Major components of JPYS were validated by real standards. Forms of mass spectrometry and chromatographic are presented in Figure 1.
SD rats (body weight, 180–220 g; age, 8 weeks old, 40 female rats and 16 male rats) were used. Rats were housed in cages in room maintained humidity of 45-65 % and a temperature of 20° C, 12-h light/12-h dark (dark at 18:00 h), and administered a pellets and water freely. 40 female rats were randomly divided into 4 groups, namely, control group, POF group, JPYS treatment group, and triptorelin treatment group. For triptorelin group, rats were intramuscularly injected with triptorelin (1.5 mg/kg body weight) on day 1. Triptorelin was dissolved into saline to obtain a 0.75 mg/mL stock solution of. All rats in the other groups were received 0.5 mL saline. Dissolving CTX into saline to obtain stock solutions with 0.5/5 mg/mL. On day 11, all rats, except control group rats, were intraperitoneally injected with CTX (50 mg/kg). For days 12 to 25, all rats, except control group rats, were intraperitoneally injected with CTX (50 mg/kg body weight), whereas control group rats were received saline. With regard to concentration conversion between humans and rats, in terms of the surface area, it was approximately 6.17, and we calculated the dose for intragastric administration. For days 26 to 85, all rats in the JPYS treatment group were intragastrically treated with JPYS (11.0 g/kg body weight), whereas other groups rats were received saline. The treatment protocol is shown in Table 2. To examine the estrous cycle, vaginal smears were obtained from rats in the morning hours (estrous interval period: mostly white blood cells; preestrus period: mostly nuclear epithelial cells; estrous cycle: mostly keratinized epithelial cells; and late estrous cycle: keratinized epithelial cells and white blood cells) (Figure 2A). Approximately 1–5 days following the last irrigation, collecting abdominal aorta blood from all groups rats during the non-estrus period with anesthesia (isoflurane via inhalation anesthesia), followed by oophorectomy. Rats were euthanized. Blood was let to coagulate (1 h) in room temperature, and then serum was acquired through centrifugation. Samples were conserved at -80° C. Ovarian tissues were placed in paraformaldehyde (4%), and some ovarian tissues were frozen by liquid nitrogen and then conserved at -80° C for molecular/protein studies. Lastly, four female rats from each group were mated with male rats with 1:1 ratio for 12 h. A vaginal plug was an indicator of successful pregnancy, and this was considered day 0.5 of gestation. Pregnant rats were euthanized on day 15.5 of gestation.
After anesthesia, the rats were euthanized, and bilateral ovaries were harvested and placed in ice-cold buffer. Fatty tissue surrounding the ovaries was removed, and ovaries were measured the diameter and weighed. The ovarian index was calculated as follows: wet weight of bilateral ovaries (mg)/body weight (g) × 100 % [9].
Ovarian tissues were fasten by paraformaldehyde (4 %) for 24 h, dehydrated in a graded ethanol series, and then embedded in paraffin. Tissue blocks were cross-sectioned to generate 5-μm-thick sections, and then sections were stained by HE [10]. Sections were observed by Olympus CX33 light microscope and follicle development (primary, secondary, and atretic follicles), as well as the integrity of the corpora lutea were assessed.
Arterial blood samples were collected to and levels of FSH and E2 were detected using ELISA (Cusabio Biotech Co., Ltd.).
Ovarian tissues were harvested immediately, cut into smaller pieces of 1 mm3, fixed in 4% glutaraldehyde at 4° C for approximately 2 h, and washed with 0.1 M sodium dimethyl arsenate buffer three times. Specimens were placed in osmium tetroxide (1 %) at 4° C for 90 min and washed with distilled water three times. Specimens were dehydrated, and then dehydrated in 100% propionaldehyde two times. The dewatering time of each concentration gradient was 10–15 min. Specimens were embedded in a graded epoxy series (1:3, 1:1, and 3:1) and polymerized at 35° C for 24 h, 45° C for 24 h, and 60° C for 24 h. Tissue blocks were sectioned to generate 70–90 nm-thick sections, which were double stained with lead citrate/uranyl acetate, and then observed with a Hitachi H-7650 electron microscope.
TUNEL was applied to assess cell apoptosis. Five-micron-thick cross-sections were deparaffinized, rehydrated, and treated with protease K (10 μg/mL) for 15 min. Cross-sections were incubated in TUNEL reaction mixture in 37° C for 60 min, then followed by washing. Nuclei were stained with 0.1 μg/mL DAPI, and cross-sections were mounted. Cross-sections were examined under a Canon fluorescent microscope. Eight random fields per sample were examined by investigators, and then number of TUNEL-positive cells was determined.
Caspase-3/9 activity of ovarian tissues was measured using related kit. A piece of ovarian tissue (10 mg) was put into the reaction buffer, and then incubated (37° C) for 2 h. A fluorimeter was used to quantify the release of the catalyzed enzyme at an absorbance of 405 nm.
After anesthesia, the rats were killed, and ovaries were harvested, and then placed in PBS. After trimming, approximately 50 mg of ovarian tissues were homogenized in isolation buffer, following centrifugation with 700 × g (10 min). The supernate was gathered, and then centrifuged with 7000 × g (10 min). Discarding the supernate, using isolation buffer to wash mitochondrial pellet, and then sample was centrifuged with 7000 × g (10 min) two times. The purified mitochondrial pellet was resuspended to obtain a protein solution (5 mg/mL). The protein concentration was in the range of 100 μg/mL–1000 μg/mL, then mitochondrial suspension was emploied to measure MMP [11], opening of mPTP [12], ROS production, injuried mtDNA [13], mitochondrial oxygen consumption rate [14], RCR [14], mitochondrial respiratory chain complex enzymes [15], and ATP [15].
Total RNA was separated with Trizol, then the RNA integrity was assessed by spectrophotometry at a wavelength of 260 nm. For quantitative polymerase chain reactions (PCR), and the reaction volume was 40 μL. Cycling conditions were performed. For semi-quantitative PCR [16], and the reaction system was 20 μL. Cycling conditions were according to following: 95° C for 10 min and 95° C for 10 s, then 40 cycles of 60° C for 15 s, 72° C for 20 s, and 72° C for 10 min. The GAPDH mRNA level was emploied for target gene normalization [17]. Primer sequences are shown in Table 3.
Total proteins were extracted, and then protein level was detected with BCA. Equivalent quantity of total protein were subjected to 8–12%. Following blocking membranes with skim milk, they were incubated separately overnight (Table 4). Immunoreactive proteins were observed with chemiluminescence kit and analyzed with software.
Female SD rats were intragastrically treated with saline or JPYS (11.0 g/kg/d) for 7 days. 2 hours after the last treatment, then the blood was extracted from the aorta ventralis, reserved in 4° C (1 h), and then centrifuged (2000 rpm/min) for 30 min. Serum samples were inactivated in a water bath (56° C) for 30 min.
The KGN human granulosa cell line was purchased from Fenghui Bio-Company (cat. no. CL0544, Changsha, China). Cells (2 × 106 cells) were plated in 100-mm culture dishes and cultured in medium with incubator at 37° C with humidified air containing 5% CO2. Medium was replaced every other day. Cells were treated with CTX with different concentrations (20, 40, and 60 μg/mL) and/or JPYS-containing serum at different concentrations (2, 4, and 8 %).
Cells were divided into 4 groups including control group, CTX (20, 40, and 60 μg/mL) -induced POF model groups, POF + JPYS (JPYS-containing serum, 2, 4, and 8 %) treatment groups, and POF + GS-49977 (5 μM) treatment group and used for in vitro experiments. Then we detect the cell viability through CCK-8.
Cells were fixed in paraformaldehyde (4 %) for 10 min, followed by washing with PBS and stained by Hoechst 33258 for 5 min. Images were observed with fuorescent microscope with an 350 nm excitation wavelength and an 460 nm emission wavelength.
Cells (1 × 105 cells/mL) were seeded and incubated overnight, and then cells were intervened with JPYS (2, 4, and 8 %) and GS-49977 (5 μM) for 24 h, followed by CTX with different levels (20, 40, and 60 μg/mL) for 24 h. Cells were analyzed via flow cytometry.
Using flow cytometry was emploied to measure ROS levels of different groups. Cells in every group were intervened, collected and suspended by DCFH-DA (10 μmol/L) with a final cell (1 × 106 cells/mL), then cells were incubated for 30 min in 37° C with CO2 (5 %), and blended every 5 min. The flow cytometer was used to measure the fluorescence intensity.
Cells were fixed with glutaraldehyde (4 %) in 4° C (2 h), and then washed with 0.1 M sodium dimethyl arsenate, and then centrifuged between the washing steps. The remaining steps was similar to in vivo study.
Mitochondria were seperated via related kit and reserved on ice for using. Mitochondria were resuspended in buffer. KGN cells (1 × 105 cells/mL) were plated in 6-well plates, and then centrifuged (600 × g) for 3–4 min. Throwing away supernate. Finally, cells were stained by JC-1 staining buffer, and then supernate was throwed away. Detecting MMP and mPTP [18, 11].
Statistical analysis was carried out by SPSS 17.0 Software. ANOVA was emploied to compare 4/5 independent groups. Two-to-two comparison among groups was emploied to analyze variance. The LSD-t test was emploied to compare multiple comparisons between 4/5 groups. We defineded P < 0.05 represent having statistically significance.
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
The chemical components of JPYS were measured by HPLC-Q-TOF-MS/MS. The 15 chemical components including: Calycosin-7-O-β-D-glucoside (contents: 12.58 μg/g; CAS: 20633-67-4), liquiritin (contents: 64.56 μg/g; CAS: 551-15-5), nobiletin (contents: 1.41 μg/g; CAS: 478-01-3), hesperetin (contents: 1.04 μg/g; CAS: 69097-99-0), Tanshinone IIA (contents: 18.40 μg/g; CAS: 568-72-9), calycosin (contents: 6.64 μg/g; CAS: 20575-57-9), formononetin (contents: 7.26 μg/g; CAS: 485-72-3), atractylenolide I (contents: 0.40 μg/g; CAS: 73069-13-3), stachydrine (contents: 0.90 μg/g; CAS: 471-87-4), betaine (contents: 17.79 μg/g; CAS: 478-01-3), dioscin (contents: 9.82 μg/g; CAS: 19057-60-4), astragaloside IV (contents: 28.88 μg/g; CAS: 96574-01-5), acteoside (contents: 24.58 μg/g; CAS: 61276-17-3), rosmarinic acid (contents: 28.86 μg/g; CAS: 20283-92-5), and rhein (contents: 6.17 μg/g; CAS: 478-43-3). The extracted-ion chromatograms are described in Figure 1A, and ESI-Q-TOF MS/MS spectra of the five components are described in Figure 1B. 2D structure of chemical components were seeked in https://pubchem.ncbi.nlm.nih.gov/.
To evaluate degree of ovarian injury and protective effects of JPYS, we examined the estrous cycle length (Figure 2A), rate of pregnancy (Figure 2B), and abnormal estrous cycle rate (Figure 2C). The estrous cycle length increased in POF group (p < 0.05) but reduced by treatment with JPYS and pretreatment with triptorelin (p < 0.05) (Figure 2A). The pregnancy rate decreased in POF group (p < 0.05) but increased after JPYS treatment and triptorelin pretreatment (p < 0.05) (Figure 2B). The abnormal estrous cycle rate increased in the POF group (p < 0.05) but decreased after JPYS treatment and triptorelin pretreatment (p < 0.05) (Figure 2C).
To further evaluate degree of ovarian injury and protective effects of JPYS, we examined the diameter of bilateral ovaries and ovarian index (Figure 3A), ovarian function (Figure 3B), and follicle development (Figure 3C). The diameter of bilateral ovaries and ovarian index decreased in the POF group (p < 0.05) but increased by treatment with JPYS and pretreatment with triptorelin (p < 0.05) (Figure 3A). Ovarian function decreased (FSH increased; E2 decreased) in the POF group (p < 0.05) but increased (FSH decreased; E2 increased) after JPYS treatment and triptorelin pretreatment (p < 0.05) (Figure 3B). The number of primary and secondary follicles, as well as the surface area of the corpora lutea, decreased in POF group (p < 0.05) but increased after JPYS treatment and triptorelin pretreatment (p < 0.05). The number of atretic follicles increased in POF group (p < 0.05) but reduced following JPYS treatment and triptorelin pretreatment (p < 0.05) (Figure 3C).
To assess mitochondrial function and examine the protective effects of JPYS, electron microscopy was used (Figure 4). Compared with the ovarian tissues of the control group, which showed normal mitochondria, the results of electron microscopy revealed that ovarian tissues of the POF group showed abnormal mitochondria, with membrane swelling and rupture. The percentage of damaged mitochondria in the POF group was higher than that of mitochondria in the control group (p < 0.05). JPYS treatment and triptorelin pretreatment decreased the percentage of damaged mitochondria (Figure 4A). Next, we measured MMP, mPTP opening (%), ROS production, damaged mtDNA, the mitochondrial oxygen consumption rate, RCR, ATP, and mitochondrial respiratory chain complex enzyme in isolated mitochondria. Compared to the control group, ROS production and mPTP opening (%) increased in the POF group (p < 0.05). However, ROS production decreased by JPYS treatment and triptorelin pretreatment (p < 0.05). Compared to the control group, the mitochondrial oxygen consumption rate, RCR, and the MMP decreased in the POF group (p < 0.05). However, JPYS treatment and triptorelin pretreatment increased these indices (p < 0.05). Real-time qPCR was used to measure the degree of damaged mtDNA. Compared to the control group, ratio of long/short fragments decreased in the POF group (p < 0.05), whereas JPYS treatment and triptorelin pretreatment increased the ratio (p < 0.05). Furthermore, the activity of mitochondrial respiratory chain complex enzymes and ATP level were decreased in POF group compared with control group (p < 0.05), whereas JPYS treatment and triptorelin pretreatment increased these indices (p < 0.05) (Figure 4B).
Real-time PCR and Western blotting were used to examine different aspects of mitochondrial function. OPA1, Mfn1, and Mfn2 were selected as markers of mitochondrial biogenesis, PGC-1α was selected as a marker of mitochondrial fusion, and Drp1 and Fis1 were selected as markers of mitochondrial fission. Compared to control group, OPA1, Mfn1, and Mfn2 levels decreased in POF group (p < 0.05), whereas JPYS treatment and triptorelin pretreatment increased their mRNA/protein levels (p < 0.05). Compared to control group, Drp1 and Fis1 levels increased in POF group (p < 0.05), whereas JPYS treatment and triptorelin pretreatment decreased mRNA/protein levels (p < 0.05). Compared to the control, PGC-1α levels decreased in POF group (p < 0.05), whereas JPYS treatment and triptorelin pretreatment their mRNA/protein levels (p < 0.05) (Figure 5).
Real-time qPCR and western blotting were used to examine the levels of target genes/proteins within the ASK1/JNK pathway. Compared to the control group, caspase-3/9, Bax, ASK1, JNK, and Cyt-c expression increased (p < 0.05) in the POF group. However, the mRNA level of these genes decreased after JPYS treatment and triptorelin pretreatment (p < 0.05). Compared to the control, Bcl-2 expression decreased in the POF group (p < 0.05). However, the mRNA level of Bcl-2 increased after JPYS treatment and triptorelin pretreatment (p < 0.05). JPYS treatment and triptorelin pretreatment inhibited ASK1 and JNK phosphorylation (decreased p-ASK1/ASK1 and p-JNK/JNK) and caspase-3/9 activation (decreased cleaved/caspase-3/9) (Figure 6B, 6C).
The TUNEL assay was emploied to evaluate protective effects of JPYS on ovarian cell apoptosis in POF rats (Figure 6D). The number of apoptotic ovarian cells in the POF group increased compared with control group (p < 0.05). However, treatment with JPYS and pretreatment with triptorelin decreased the number of apoptotic ovarian cells (Figure 6D). The activity of caspase-3/9 in ovarian tissues in the POF group increased compared with control group (p < 0.05) but decreased after JPYS treatment and triptorelin pretreatment (Figure 6E, 6F).
The results of compound analysis were as follows: rhein (0.127 μg/mL), salvianolic acid A (1.868 μg/mL), liquiritin (0.068 μg/mL), acteoside (0.000 μg/mL, calycosin-7-O-β-D-glucoside (0.134 μg/mL), rosmarinic acid (0.812 μg/mL), formononetin (0.010 μg/mL), calycosin (0.049 μg/mL), astragaloside IV (24.989 μg/mL), atractylenolide I (0.016 μg/mL), dioscin (1.873 μg/mL), tanshinone IIA (0.043 μg/mL), narirutin (5.145 μg/mL), nobiletin (0.064 μg/mL), hesperetin (0.467 μg/mL), stachydrine (0.015 μg/mL), and betaine (5.508 μg/mL) (Figure 7). 2D structure of chemical components were seeked in https://pubchem.ncbi.nlm.nih.gov/.
To assess effects of JPYS-containing serum and CTX on cell function, a cell viability assay was emploied. Compared to control group, KGN cell viability decreased through intrevening with CTX (20, 40, and 60 μg/mL) (p < 0.05). The results of light microscopy revealed normal cells with long fusiform shapes in control group. Nevertheless, CTX treatment altered cell shape by shrinking cells and disrupting cell–cell interactions. Cell viability was not affected by JPYS-containing serum (2, 4, 8 %) but it was affected by pretreatment with CTX (20 μg/mL) followed by treatment with JPYS (2, 4, 8 %). At the two lowest concentrations but not at the highest concentration, JPYS-containing serum attenuated cell viability dose-dependently (p < 0.05) (Figure 8A–8C). Compared to control group, CTX (20 μg/mL) induced apoptosis. At the two lowest concentrations but not at highest concentration, JPYS-containing serum attenuated the apoptosis dose-dependently (p < 0.05) (Figure 8D). Compared to control group, the apoptotic rate increased in the POF group (p < 0.05). The apoptotic rate was lower in cells pretreated with JPYS-containing serum at concentrations of 2 and 4 % than that in untreated cells (p < 0.05), and the effects were dose-dependent (p < 0.05). However, JPYS-containing serum at its highest concentration did not protect cells from apoptosis (Figure 8E). The results of electron microscopy revealed that untreated cells were healthy, whereas CTX caused apoptosis, and JPYS-containing serum (2 and 4 %) can inhibit apoptosis induced by CTX (Figure 8F).
The percentage of damaged mitochondria was higher in the CTX group than that in the control group (p < 0.05). The percentage of damaged mitochondria decreased after treatment with JPYS-containing serum at concentrations of 2 or 4 % but not at a concentration of 8 % (p < 0.05) (Figure 9A). Compared to control group, the MMP decreased in CTX group (p < 0.05). Cells pretreated with 2 or 4 %, but not 8 %, JPYS-containing serum had an increased MMP (p < 0.05) (Figure 9B). Compared to control group, the mPTP (%) increased in CTX group (p < 0.05). Cells pretreated with 2 or 4 %, but not 8 % JPYS-containing serum had a decreased mPTP (%) (p < 0.05) (Figure 9C). Compared to the control group, the ROS levels increased in the CTX group (p < 0.05), cells pretreated with 2 or 4 %, but not 8 % JPYS-containing serum had an decreased ROS levels (p < 0.05) (Figure 9D). As such, 4 % JPYS-containing serum was selected for subsequent experiments.
To research whether anti-apoptotic effects of JPYS-containing serum were associated with ASK1/JNK pathway, GS-49977 (ASK1 inhibitor) was emploied. Compared to control group, CTX increased ASK1 and JNK phosphorylation (p < 0.05), whereas JPYS and GS-49977 decreased ASK1 and JNK phosphorylation (p < 0.05). Compared to the control group, CTX up-regulated Bax and cleaved caspase-9 levels (p < 0.05), whereas JPYS and GS-49977 down-regulated Bax and cleaved caspase-9 levels (p < 0.05). Compared to the control group, CTX decreased the Bcl-2 level (p < 0.05), whereas PYSP and GS-49977 increased the Bcl-2 level (p < 0.05) (Figure 10A). Compared to the control group, the MMP level (p < 0.05) was decreased but the mPTP level (p < 0.05) was increased in POF group. Compared with CTX group, the MMP level (p < 0.05) was increased but mPTP was decreased after JPYS and GS-49977 treatment (p < 0.05) (Figure 10B, 10C). Compared to the control group, the ROS levels increased in the CTX group (p < 0.05), cells pretreated with JPYS and GS-49977 had an decreased ROS levels (p < 0.05) (Figure 10D). Compared to control group, CTX induced apoptosis, whereas JPYS and GS-49977 inhibited apoptosis (p < 0.05) (Figure 10D).
The main findings of this in vitro and in vivo study were that granulosa cell apoptosis associated with POF and that JPYS protected ovarian tissues from damage caused by POF. As far as I am concerned, this research is firstly to study protective effects and mechanism about JPYS in POF. Mitochondrial dysfunction can induce granulosa cell apoptosis and JPYS can reduce apoptosis through mitochondrial function improved via the ASK1/JNK pathway. To investigate the damage caused by CTX and the protective mechanism of JPYS, we used CTX to build POF rats and using JPYS to research protective effects. POF, also known as primary ovarian insufficiency (POI), affects the fertility of women of child-bearing age. The difference between menopausal and POF women is that ovaries in POF women still have a reserve. As such, it is important to understand how ovarian function can be maintained in cases of POF [19]. CTX, an alkylating chemotherapeutic drug, is one of the most damaging drugs due to its high toxicity and high risk of POF [20]. CTX can damage primordial follicles and reduce the ovarian reserve, bringing about POF and inducing cellular changes regardless of the stage of cell cycle such as genomic alterations, morphological damage, and apoptosis [21]. Many of these changes are irreversible. CTX is also used in the treatment of non-malignant diseases such as systemic lupus erythematosus [22]. Apoptosis is a type of ATP-dependent cell death induced by various extracellular and intracellular signals, and it involves several proteins and pathways such as cell death receptor as well as mitochondrial and endoplasmic reticulum pathways, with the mitochondrial pathway having the greatest role in apoptosis [23–25]. Granulosa cells produce many peptides and proteins that are related to synthesis of progesterone and estrogen. Follicular atresia, which is a key feature of POF, is caused by apoptosis of granulosa cells and oocytes [24, 26]. Thus, granulosa cell apoptosis is an initiating factor in the occurrence of POF [27]. Most traditional Chinese medicines remain uncharacterized. Astragali Radix, which was previously identified as one of the compounds in JPYS by profiling analysis [8], is used in the promotion of energy and in the improvement of hypoxia [28]. For the past two decades, it has also been widely prescribed for CKD patients. According to what I know, this study is firstly to use JPYS JPYS in the treatment of POF. At cellular level, JPYS can improve mitochondrial dynamics, promote the balance of fission/fusion, and then inhibit mitophagy and autophagy [29]. Astragali Radix is a flavonoid, and this group of compounds has been reported to improve mitochondrial function [30]. In this study, we detected the chemical components of JPYS, qualitative and quantitative analysis of 15 chemical components in decoction and qualitative and quantitative analysis of 17 chemical components in containing serum, those chemical components are very complex (Figures 1, 7). Cyt-c release and ROS generation can induce apoptosis, and the opening of the mPTP is critical for these cellular processes. Thus, mitochondrial function is improved and apoptosis is inhibited when the opening of the mPTP is suppressed [12], and this may provide new insights into development of anti-cancer drugs such as those that target ovarian cancer [31]. The opening of mPTP, which is mediated by binding to mitochondrial inner membrane protein cyclophilin D, decreases the membrane potential, induces mitochondrial swelling, and inhibits oxidative phosphorylation. A previous study has reported that cyclosporine A binds to cyclophilin D, thereby suppressing the opening of mPTP and reducing injury [32]. Therefore, we hypothesized that JPYS has a protective function in the ovaries by improving mitochondrial function (inhibiting the opening of mPTP) in POF rats. Rats were intraperitoneally injected with CTX (50 mg/kg) to induce granulosa cell apoptosis. The effects of JPYS on the reproductive system were assessed by measuring the levels of serum E2 and FSH. FSH, a pituitary hormone that binds to FSHR and activates aromatase to produce E2, acts on the ovaries, although E2 can inhibit the production of FSH through a negative feedback loop [33]. Both hormones are critical for follicle growth. In POF rats, CTX affected the estrous cycle by extending the cycle length, increasing the abnormal estrous cycle rate, and decreasing the pregnancy rate, whereas opposite findings were observed after JPYS treatment, suggesting that JPYS can improve the ovarian reserve. In addition, CTX decreased the diameter of bilateral ovaries and ovarian index and ovarian function (FSH increased, E2 decreased, and FSH increased), whereas JPYS increased both indices. HE staining showed that CTX decreased the number of primary/secondary follicles, as well as surface area of corpora lutea, and increased the number of atretic follicles, whereas opposite findings were observed for JPYS and triptorelin, similar to Zuogui pills (ZGP) that have been previously used to treat POF in rats [34]. With regard to cell death, the number of apoptotic cells in ovarian tissues in the POF group increased. JPYS performed its anti-apoptotic effect by inhibiting expression of Bax (pro-apoptotic protein) and the activity of caspase-9/3, and increasing expression of Bcl-2 (anti-apoptotic protein) in vivo and in vitro. The decreased Bax shifted to mitochondria to integrate with increased Bcl-2, thereby forming the Bax/Bcl-2 heterodimer on the mitochondrial membrane. The Bcl-2 family is comprised of pro-apoptotic proteins (Bad, Bax, and Bid) and anti-apoptotic proteins (Bcl-xL, Bcl-2, and Bel-w) [35]. Bcl-2, a proto-oncogene located on chromosome 18a21, inhibits cancer cell apoptosis, whereas Bax is a pro-apoptotic gene located on chromosome 19q13. Both the Bax/Bcl-2 heterodimer and Bcl-2/Bcl-2 homodimer can preserve permeability of the mitochondrial membrane, and the Bax/Bax homodimer can decrease MMP. As such, the ratio of Bax and Bcl-2 in cells controls whether they will live or die [36]. The underlying mechanism of ovarian injury remains unknown, even as studies have reported the involvement of senescence and apoptosis in the loss of the ovarian reserve [37]. Mitochondrial dysfunction often associates with granulosa cell injury in POF mice [38]. The disruption of mitochondrial pathways (NF-jB/p53/PUMA and PI3K/Akt/Bad) can cause mitochondrial dysfunction [18, 39, 40]. In this study, CTX caused mitochondrial swelling and membrane rupturing, and increased the number of damaged mitochondria, whereas JPYS had a protective effect in vivo and vitro (Figures 4A, 9A). There is an association between the disruption in mitochondrial homeostasis and the pathophysiology of POF [41], as impaired mitochondrial function and increased oxidative stress are key aspects of POF [38]. JPYS decoction can decrease the chronic kidney disease (CKD)-induced imblance of mitochondrial quality control processes, via increasing mitochondrial biogenesis, renewing the balance between fusion and fission, and decreasing autophagy-lysosome pathway (mitophagy) in past research [29]. We established and used the POF rat model to examine mitochondrial function. RCR, the MMP, oxygen consumption rate, mitochondrial respiratory chain complex enzymes and ATP were reduced in POF group, whereas ROS production and mPTP opening (%) were increased in the POF group. JPYS treatment improved the mitochondrial function indices in vitro and in vivo. Others have used the copy number of mtDNA to evaluate the number of mitochondria [13, 42]. Although the mechanism of mtDNA repair is unknown, mtDNA is near the respiratory chain, so mtDNA is more vulnerable when exposed to oxidative stress. We calculated ratio of long/short fragments as an indicator of mtDNA integrity, and the ratio of long/short fragments was decreased in the POF group, but increased in the JPYS treatment group. Mitochondrial biogenesis involves many genes such as OPA1, Mfn1, and Mfn2. As a regulatory gene, OPA1 regulates mitochondrial dynamics. L-OPA1 overexpression can reduce neuronal apoptosis through increasing Bcl-2 level and decreasing Bax level, as well as caspase-3 activation, so those genens are related with apoptosis. Furthermore, L-OPA1 overexpression can modulate mitochondrial dysfunction by reversing mitochondrial damage, reducing oxidative stress and energy deficits, preserving mitochondrial integrity, and promoting mitochondrial biogenesis in the brain [43]. Other proteins involved in mitochondrial dynamics are Drp1 and Mfn [44]. Mfn2, a conserved dynamin-like GTPase situated on outer membrane of mitochondria, affects mitochondrial structure and function by modulating fission and fusion [45]. Many studies have demonstrated that Mfn2 can control the respiratory chain, MMP, metabolic processes, and apoptosis [46]. Furthermore, Mfn2 performs a critical function in preserving the integrity of mtDNA [47]. In our research, OPA1 and Mfn1/2 mRNA and protein levels decreased in the POF group, whereas JPYS treatment and triptorelin pretreatment increased these mRNA and protein levels. It also decreased the ratio of long/short fragments, consistent with results of a previous study [47]. Mitochondria undergo fission and fusion, and these dynamic processes are critical for the maintenance of mitochondrial size, shape, and organization. PGC-1α, an important transcriptional co-activator, can modulate key factors, including Nrf1 and Tfam, and they are considered to up-regulate mitochondrial biogenesis [48]. Drp1, a major regulatory factor of mitochondrial fission. Drp1 link to mitochondria increases apoptosis [49]. Fis1 can induce apoptosis by interacting with endoplasmic reticulum-localized Bap31 in Bax/Bak-mediated permeabilization in mitochondrial outer membrane, resulting in cyt-c release even cell death [50]. Fis1 has been be closely related with reducing GTPase activity of Mfn1/2 and OPA1 [51]. Our results demonstrated that the levels of Drp1 and Fis1 increased, yet PGC-1α level decreased in POF group, showing an imbalance of fision and fusion in mitochondria. As the two main pathways (extrinsic death receptor and intrinsic mitochondrial pathways) of the regulating cell apoptosis, mitochondrial apoptosis pathway control the mPTP (relese the ROS and Cty-c) to induce apoptosis. Furthermore, mitochondrial dynamics can regulate the cell apoptosis, fission and fusion are process of mutual cooperation which can modulate the morphology and the number of mitochondria, this process determine mitochondrial mass (removing poor quality), and mitochondrial mass determine cell apoptosis. ASK1 is a ubiquitously expressed MAP3K that is activated by several stimuli, as well as overexpressed in neurodegenerative disorders, cancer, and infammatory diseases [52]. Activated ASK1 activates downstream kinases, such as JNK and p38, leading to infammatory cytokine expression and apoptosis [53]. The ASK1 inhibitor selonsertib (GS-49977) is a latent therapeutic medication for early curing ALF, in that it reduces JNK-mediated Drp1 translocation in mitochondria and then alleviated mitochondrial injury [54]. The ASK1/JNK signaling pathway exerts a critical role in the initiation of mitochondria-mediated apoptosis, ASK1/JNK pathway can improve the MMP and control the opening of mPTP (Figure 6) [55]. Our results showed that JPYS affected the expression of ASK1/JNK pathway-related proteins, inhibited ASK1 and JNK phosphorylation, and decreased Cyt-c and cleaved caspase-3/9 expression. GS-49977 elicited similar effects in vitro (Figure 10). Taken collectively, these results showed that JPYS can improve mitochondrial function by decreasing injury and increasing mitochondrial function, and inhibiting CTX-induced apoptosis by inhibiting the ASK1/JNK pathway.
In this study, we isolated mitochondria and showed that CTX damaged mtDNA, disrupted mitochondrial respiratory function, and produced ROS, thereby causing ovarian injury. Mitochondrial swelling was led by the opening of mPTP in POF rats. The opening of mPTP can induce the flow back of protons from the mitochondrial membrane to the matrix, and then decreasing ATP synthesis and the MMP and leading to metabolic abnormalities. However, JPYS inhibited apoptosis through regulating mitochondrial function via the inhibition of the ASK1/JNK pathway in POF rats. We considered that JPYS may be emploied as a potential therapeutic medication for POF, and mitochondria can be seen as a potential therapeutic target. However, mechanism research is not deep enough and we did not employ the bioinformatics methods, those will be performed in future. |
PMC9648802 | 36260869 | Stanley R. Primmer,Chen-Yu Liao,Oona M.P. Kummert,Brian K. Kennedy | Lamin A to Z in normal aging | 17-10-2022 | lamin A,prelamin A,zmpste24,mTOR,aging | Almost since the discovery that mutations in the LMNA gene, encoding the nuclear structure components lamin A and C, lead to Hutchinson-Gilford progeria syndrome, people have speculated that lamins may have a role in normal aging. The most common HPGS mutation creates a splice variant of lamin A, progerin, which promotes accelerated aging pathology. While some evidence exists that progerin accumulates with normal aging, an increasing body of work indicates that prelamin A, a precursor of lamin A prior to C-terminal proteolytic processing, accumulates with age and may be a driver of normal aging. Prelamin A shares properties with progerin and is also linked to a rare progeroid disease, restrictive dermopathy. Here, we describe mechanisms underlying changes in prelamin A with aging and lay out the case that this unprocessed protein impacts normative aging. This is important since intervention strategies can be developed to modify this pathway as a means to extend healthspan and lifespan. | Lamin A to Z in normal aging
Almost since the discovery that mutations in the LMNA gene, encoding the nuclear structure components lamin A and C, lead to Hutchinson-Gilford progeria syndrome, people have speculated that lamins may have a role in normal aging. The most common HPGS mutation creates a splice variant of lamin A, progerin, which promotes accelerated aging pathology. While some evidence exists that progerin accumulates with normal aging, an increasing body of work indicates that prelamin A, a precursor of lamin A prior to C-terminal proteolytic processing, accumulates with age and may be a driver of normal aging. Prelamin A shares properties with progerin and is also linked to a rare progeroid disease, restrictive dermopathy. Here, we describe mechanisms underlying changes in prelamin A with aging and lay out the case that this unprocessed protein impacts normative aging. This is important since intervention strategies can be developed to modify this pathway as a means to extend healthspan and lifespan.
The LMNA locus is complex, with variations in polyadenylation and splicing leading to the generation of all A-type lamins, which are linked to myriad nuclear structural roles and functional properties [1–3]. B-type lamins are encoded by other loci and have been attributed overlapping functions. A-type lamins have received the bulk of the attention, however, since mutations at this locus have been linked to a variety of dystrophic and progeroid syndromes [1–3]. The purpose of this review is not to provide a thorough overview of A-type lamin functions and their disease connections, but rather to evaluate one hypothesis: that altered processing of lamin A due to a reduction in Zmpste24 (aka FACE1) with age promotes aspects of the normal aging process. Several reviews have been cited herein for readers interested in broader questions around A-type lamin function and dysfunction. Transcription of the LMNA gene produces mRNAs primarily for two proteins: prelamin A and lamin C. Prelamin A undergoes 4 post-translational steps to create mature lamin A for which the endoprotease, Zmpste24, is essential [4–7]. The prelamin A protein contains a CAAX (Cysteine-Alipathic-Alipathic-any amino acid) box at its C-terminal end (see [8, 9] for review). In humans and mice, the CAAX box of prelamin A is CSIM. The first step is farnesylation of cysteine, which is followed by cleavage of the terminal 3 amino acids by Zmpste24 or Rce1. The third step is carboxymethylation of the same cysteine. Zmpste24 is then the only known protease that can delete an additional 15 amino acids to form mature lamin A protein. If there is an imbalance with insufficient Zmpste24 to process all of the prelamin A, prelamin A accumulates in the nucleus [10]. The mutation most associated with Hutchinson-Gilford progeria syndrome is a non-coding single amino acid substitution that activates a rarely used splice site in the C-terminus of the protein. This interferes with C-terminal cleavage of lamin A by Zmpste24 resulting in a permanently farnesylated lamin A, termed progerin, which acts in a dominant fashion to promote a range of accelerated aging phenotypes comprising Hutchinson Gilford progeria syndrome (HGPS) [2, 3]. Much debate has centered on whether progerin, which is generated at low levels in absence of HGPS mutations, contributes to normal aging [11–14]. Progerin expression has been observed at very low levels in normal cells and may accumulate with age, although the latter assertion has been hard to verify [15–17]. The toxic effects of nuclear prelamin A are similar to that of progerin and resemble aspects of premature aging, but with a slower onset than progerin [18, 19]. Here, we present evidence supporting the hypothesis that declining levels of Zmpste24 with age contribute to an elevated level of prelamin A and it may be this lamin A variant that drives aspects of normal aging pathology.
In both human patients and animal models, mutations leading to the expression of progerin cause a segmental progeria syndrome in which a subset of features of accelerated aging are present [11, 20]. This is also the case for mice lacking ZMPSTE24−/−, which survive about 5 months of age [6] and show both molecular and physiologic features of accelerated aging. These include genome instability [21], age-related bone loss [18], oxidative damage [22], cell senescence [19], altered epigenetic patterns [23], similarities in skeletal muscle decline [24], reduced adult stem cell function [25, 26], and altered age-related cell signaling pathways [27, 28]. Many of these phenotypes are described in more detail below (Table 1). Patients with the laminopathy, restrictive dermopathy (RD), have mutations in either ZMPSTE24 or LMNA, the latter associated with altered processing and the accumulation of prelamin A [4, 29]. RD has some phenotypes of accelerated aging; however, the condition is often very early onset and severe, making comparison with normal aging more challenging.
Much of the current data supporting a decline in Zmpste24 protein levels during the aging process comes from analysis of cells ex vivo. Here, we summarize that data while emphasizing the need for in vivo studies in mice and human tissue samples. Figure 1 details the regulatory events that dictate Zmpste24 levels and activity during aging.
A study of skin fibroblasts collected from young (8–35) and older (65–80) individuals, as well as centenarians (95–105) was performed and, after 6 passages in culture, the cells were assayed for aging properties [30]. Evident in cells from centenarians (over 100), but not 65–80 year old individuals, is reduced mRNA and protein levels of Zmpste24 and an accompanying increase in unprocessed lamin A. One challenge of studying centenarians is that the control group, people from the same generation who did not age well, are not around from which to collect material. Thus, when changes are observed such as those described above, two potential explanations are possible. First, it may be that ZMPSTE24 expression declines with age, implying that this is a property of normal aging. Since levels are not reduced in the 65–80 year old group, this would presumably be a late event in aging. Second, it is possible that centenarians have low levels throughout life and that this serves some protective role. We favor the former, given that other evidence indicates that Zmpste24 levels decline with age, as discussed below, but clearly more studies need to be performed.
Human primary fibroblasts derived from fetal lung are often used for cell senescence studies. These cells will undergo senescence after prolonged serial passaging, or in response to a range of other induction methods. The level to which in vitro replicative senescence resembles aspects of normal physiologic aging remains debated [31]; however, recent studies indicate that in vivo cell senescence is a significant driver of aging in large part because of their unique secretory profiles termed the Senescence Associated Secretory Profile (SASP) [32]. One report has indicated that ZMPSTE24 (FACE-1) mRNA levels decline during cell senescence induced through serial passaging in these cells [33]. This finding is intriguing and although correlative, the decrease of Zmpste24 protein and resulting increase in prelamin A indicate cause and effect, but studies should be repeated in other primary fibroblast isolates and also when senescence is induced through other methods.
Mesenchymal stem cells (MSCs), which can be isolated from bone marrow, adipose and other tissues, are multi-potent, giving rise to cells in a range of tissues. These include bone, adipose, smooth muscle, cardiomyocytes, and other tissues that overlap significantly with those affected in laminopathies, making them interesting cells to study in this context. In addition, expression of progerin alters the differentiation properties of MSCs in vitro, impairing adipose differentiation. This is intriguing given that loss of adipose tissue is a hallmark of many laminopathies [34–36]. Also intriguing is that overexpression of wild-type lamin A conferred similar phenotypes to that of progerin, although to a lesser extent. One potential reason for this is that in the context of overexpression, the amount of lamin A produced may outpace the ability of Zmpste24 to confer processing, leading to elevated prelamin A. However, it cannot be ruled out that excess mature lamin A may disrupt nuclear functions through other mechanisms. Enforced overexpression of prelamin A in human MSCs has also been reported to lead to elevated levels of proteins associated with osteogenesis [37]. This is surprising since ZMPSTE24−/− mice have higher levels of adipogenesis [18, 38] and osteoporosis [39]. Moreover, cells in aging bone marrow are thought to skew toward adipogenesis and away from osteogenesis [40, 41]. One clue to explain this apparent discrepancy may come from observations that older wild-type mice were found to have low levels of mature lamin A/C in osteoblasts [38], which could be an indirect consequence of loss of ZMPSTE24, at least with regard to mature lamin A. Knockdown of Lamin A/C was associated with increased adipogenesis [38]. Low levels of mature lamin A may counteract the effects of increased osteogenic factors observed in prelamin A overexpression. The MSCs are not immortal in culture and undergo replicative senescence. As with fibroblasts, senescence in these cells is associated with down-regulation of ZMPSTE24 and nuclear accumulation of prelamin A [42]. The mechanism involves upregulation of the microRNA miR-141-3p, which targets the 3′ UTR of ZMPSTE24, leading to reduced expression. Enforced expression of miR-141-3p induced senescence in cultured MSCs and injection of the microRNA in mice led to decreased liver expression of ZMPSTE24. The activity of HDAC1 and HDAC2 declines during replicative senescence and decreases the expression of ZMPSTE24 by upregulating miR-141-3p [42]. Increased prelamin A expression has also been proposed as a senescence marker to screen MSCs in vitro before clinical application [43]. These findings reinforce the studies in fibroblasts that reduced expression of ZMPSTE24 is associated with cell senescence and call for a wider analysis of the microRNA in tissues from aging animals. A more recent paper found reduced Zmpste24 levels and prelamin A accumulation during cell senescence in subchondral bone mesenchymal stem cells [44]. Enforced expression of prelamin A in these cells accelerated senescence, which was associated with DNA damage, including at telomeres, and increased expression of inflammatory factors. Interestingly, the accelerated senescence phenotype could be suppressed by vitamin C, which also reduced inflammatory factors.
Premature senescence in primary human epithelial cells isolated from human umbilical vein or cord blood, can also be induced by elevated levels of prelamin A, this time induced by exposure of cells to protease inhibitors that inhibit activity of Zmpste24 [45], which evokes a similar phenotype in human bone marrow-derived MSCs [46]. Interestingly, this phenomenon was observed in both precursor and mature endothelial cells.
The Shanahan lab found that nuclear accumulation of prelamin A in VSMC of arterial media with age and in vitro was correlated with the down regulation of ZMPSTE24 (See Figure 1 in [47]). These effects occurred both in vitro and in cells from old individuals processed ex vivo. The decline of ZMPSTE24 and the increase in nuclear prelamin A occurred before cellular senescence. The presenescent phase in VSMC included modification of migrational characteristics [48] and calcification due to increased levels of the osteogenic markers Runx2, ALP, and osteocalcin resulting primarily from greater DNA damage [49]. SASP factors were secreted by VSMC with higher levels of prelamin A. This finding is consistent with an early study that protease inhibitors that impair Zmpste24 function lead to premature senescence in VSMC [50]. The importance of the role of VSMC in atherosclerosis is increased by the report that VSMC may transdifferentiate into macrophage-like cells when proliferating into plaque in the intima in response to arterial damage or stress [51]. VSMC contribute with macrophages to foam cells in lesions in arteries [51, 52]. Prelamin A may have a role in these events. When endothelial cells from human umbilical vein or cord blood were treated with the HIV protease inhibitor Atazanavir, which inhibits Zmpste24, the authors observed nuclear accumulation of prelamin A leading to irregularly shaped nuclei, premature cellular senescence, and an increase in monocyte adhesion. The complexity of atherosclerosis involves many factors in addition to prelamin A in VSMC, foam cells, and endothelial cells as described in many articles [45, 53, 54]. The review of the role of lamins in atherosclerosis by Jiang and Ji provides additional useful information [55].
We have recently examined the levels of Zmpste24 in several tissues comparing mice of 9 and 22 months of age. While not definitive at this point, the studies strongly suggest age-associated changes in a tissue-dependent manner and call for further studies to be performed. We have included them to support the overall hypothesis and stimulate further research. Four tissues were examined: brain, liver, heart and gastrocnemius (skeletal muscle) and results are shown in Figure 2. In both brain and liver, we detect a significant decline in Zmpste24 levels, although this was not detected in heart and skeletal muscle. This finding calls for studies across a wider age range and in more tissues, and the levels of Zmpste24 should be correlated with prelamin A.
Studies above indicate that declining Zmpste24 levels lead to senescent pathology in a variety of cellular contexts, raising the question of how this occurs at the mechanistic level. Several models have been proposed. Classical pathways associated with senescence involve the p53 and pRB-p16INK4A pathways. Activation of p53 and expression of p16INK4A lead to cell cycle arrest and senescence, and ablation of these pathways allow cells to escape senescence and become immortalized. p16INK4A is induced in multiple contexts associated with enhanced prelamin A expression [42, 47, 49, 56, 57], as is induction of p53 [19, 58]. Notably, induction of p16INK4A is associated with all methods to induce senescence in cell culture and, therefore, it is not surprising in the prelamin A context. This raises the question of what is happening upstream of induction of these factors. Casein kinase 2 may be a factor linking prelamin A accumulation to cellular senescence. CK2 has long been known to be nuclear matrix-associated [59], but a recent paper shows more direct connections with lamin A, which binds to CK2 and inhibits its activity. Loss of lamin A expression leads to enhanced CK2 activity, where prelamin A accumulation is associated with its inhibition [60], in turn leading to senescence induction [60–62]. Down-regulation of CK2 is also associated with accelerated aging and oxidative stress in C. elegans [63]. Interestingly, the pro-longevity compound spermidine, which has been reported to activate CK2, was found to suppress cellular senescence in ZMPSTE24−/− MEFs and to extend the lifespan of the mice from which the cells were derived [60]. Another pathway linking prelamin A to senescence involves p62, a component of the autophagic machinery that has been linked to the aging process [64]. Enhanced autophagy, a process that involves the clearance of damaged cellular macromolecules and structures, has been implicated as a mechanism by which progerin, and more recently prelamin A, is degraded by mTOR inhibition (see below). In the context of exogenous progerin expression or reduced ZMPSTE24 expression in MSCs, DNA damage permits GATA4 to avoid p62 binding and selective degradation [65, 66]. Stabilization of the transcription factor GATA4 leads to monocyte chemoattractant protein-1 (MCP-1 expression), induction of the senescence-associated secretory phenotype and paracrine senescence [65].
One major candidate mechanism for the manner by which prelamin A promotes cellular senescence involves the induction of DNA damage. DNA damage, in multiple forms, or the induction of the DNA damage response have long been reported to induce cellular senescence. Several reports link expression of prelamin A to DNA damage [67], and some mechanistic studies have been conducted. For instance, cells from mice lacking ZMPSTE24 were reported early on to have elevated levels of DNA damage [21]. This phenotype has also been observed in fibroblasts from Restrictive Dermopathy patients, which have homozygous mutations in the enzyme [68]. In particular, the latter study was able to identify an increase in DNA double strand breaks [68], and both studies found defective DNA damage responses. A later study also reported increased DNA damage in smooth muscle cells expressing prelamin A [47]. Follow up studies in these cells point to a possible mechanism whereby lamin A/C is part of the DNA damage response and accumulation of prelamin A interferes with the normal role of the intermediate filament proteins [69]. DNA replication stress, in some respects a specialized form of replication stress, is not often considered. However, studies of replicative lifespan in yeast have indicated that a major driver of aging is DNA replication stress [70, 71]. Nuclear lamins have long been suspected to facilitate DNA replication [72], and recent studies have indicated that progerin or prelamin A can induce replication fork stalling, which leads to DNA breaks [73–75]. Interestingly, this leads to activation of the cGAS/STING cytosolic DNA sensing pathway and an interferon response [76]. This is perhaps consistent with increased inflammation associated with loss of ZMPSTE24 or expression of progerin, which has been previously reported [77]. Treatment of progerin expressing cells with calcitrol, an active version of vitamin D, reduces replication stress and the associated innate inflammatory response [78]. cGAS/STING signaling has been recently linked to cellular senescence and aging [79, 80], making this a pathway to explore in more detail in progeria and normal aging.
Links between oxidative stress and aging date back to the famous hypothesis by Denham Harman [81], although whether oxygen free radicals drive aging remains a matter of debate. While free radicals drive damage to a variety of cellular molecules, they also mediate critical signaling pathways, making it difficult to interpret their net effect on the aging process. Oxidative stress is also linked to a range of age-related diseases and can induce cellular senescence [82]. Increased oxidative stress is linked to reduced Zmpste24 activity [57, 83, 84], and overexpression of prelamin A in mesenchymal stem cells [85]. Interestingly, oxidative stress leads to reduced levels of Zmpste24 [47, 84, 86], possibly through upregulation of miR-141 [42]. This creates a possible feed-forward loop with oxidative damage that accumulates during aging leading to a reduction in Zmpste24 activity, which, in turn, results in more oxidative damage. More studies need to be performed in physiologic oxygen of 2 to 5% rather than in atmospheric oxygen conditions, in order to more closely resemble the in vivo environment.
A-type lamins have been reported to interact with multiple Sirtuins, protein deacetylases linked to control of healthspan and lifespan [87]. With regard to SIRT1, lamin A binding leads to SIRT1 activation; however, the interaction is reduced in the presence of prelamin A, contributing to adult stem cell decline in ZMPSTE24−/− mice [88]. Lamin A also binds to and activates SIRT6, which leads to enhanced DNA repair. Again, this interaction is compromised at least in the presence of progerin (prelamin A was not reported). This may be particularly relevant as SIRT6 deficiency leads to a progeroid phenotype and overexpression of the deacetylase leads to lifespan extension [89, 90]. In culture, HGPS fibroblasts have reduced SIRT6 levels and restoration of its expression led to reduced senescence phenotypes [91]. Interestingly, SIRT6 is known to suppress LINE1 retrotransposon activation [92], which has been shown to mediate progression of cellular senescence both in SIRT6−/− and aging wild-type mice in a manner involving cGAS/STING signaling (see DNA damage section) [93, 94]. SIRT7 also represses LINE1 elements and interacts with lamin A, although whether prelamin A shows altered binding has not been reported [95].
The mTOR signaling pathway, of which regulation of autophagy is one major downstream pathway, is highly linked to aging [96, 97]. mTOR is a nutrient-responsive kinase that evidence indicates is aberrantly upregulated during aging. Reduced mTOR signaling, mediated genetically or with the highly specific drug rapamycin, extends lifespan in a wide range of model organisms. Evidence suggests that mTOR inhibition can also reverse aspects of aging in human studies [98, 99]. These findings make it obvious that mTOR signaling would be examined in progeria models. Initial studies in fibroblasts expressing progerin indicate that rapamycin can enhance autophagy, which is beneficial at least in part because it facilitates clearance of progerin itself [100, 101]. It was later shown to enhance cellular proliferation and reduce levels of cell senescence [102]. These studies have primarily focused on progerin, but a recent study indicates that expression of prelamin A confers similar phenotypes, and adds to previous observations by showing that the protein stimulates mTOR activation and impairs autophagy [27]. These findings suggest that more studies are needed to understand the role of mTOR in progeria and prelamin A-related normal aging.
When there is insufficient Zmpste24 to process prelamin A to the mature form, the nucleus may become dysmorphic [68, 103], display evaginations [104], or contain nucleoplasmic reticulum [105–107]. The term nucleoplasmic reticulum is applied to long, tubular channels that extend deep into the nucleoplasm or even pass entirely through the nucleus [107]. Some are short stubs while others are complex, branching structures. Some terminate at or near nucleoli. The nucleoplasmic reticulum is of particular interest here because it is formed during interphase by excess nuclear prelamin A, as demonstrated when Interphase prelamin A was experimentally produced by suppressing ZMPSTE24, either by siRNA or by application of an HIV protease inhibitor (PI) such as saquinavir [105, 106]. When saquinavir was removed from the media, processing resulted in mature lamin A and the number of nucleoplasmic reticulum invaginations was markedly reduced [106]. The role of nucleoplasmic reticulum formation in aging remains poorly understood and more studies are needed in aging organisms. Stepping back, there are also numerous studies linking progerin, and to a lesser extent prelamin A, expression to loss of heterochromatin. Links to progerin are thoroughly described in a recent review [108]. Regarding prelamin A, early studies linked prelamin A to loss of chromatin organization [109, 110]. More recently, studies of lamina-associated domains (LADs) in Zmpste24−/− mice indicate altered associations with transcription factors, including Foxa2 that are similar to those of old wild-type mice [111]. More studies are needed, but understanding the heterochromatin alterations associated with altered lamin A function remains a vital area of research.
Given that loss-of-function mutations in ZMPSTE24 give rise to phenotypes resembling those associated with LMNA mutations, many have assumed that the role of Zmpste24 is restricted to modifying processing of Lamin A; yet this may be too simple as other functions of Zmpste24 are known. For instance, other substrates of Zmpste24 have been identified, including proteins that are not prenylated, although the significance of these events remain largely unknown [112]. Notably, Zmpste24 assists in helping translocon pores in the endoplasmic reticulum function smoothly [113, 114]. Translocons can become clogged when proteins enter but fail to properly transverse the pore. Under these conditions, Zmpste24 cleaves clogged proteins into peptide fragments for clearance. This function may have roles in aging, where protein misfolding is increased. A recent study, however, found that ZMPSTE24 disease mutations all affected lamin A processing but only some mutants interfered with the ability of the enzyme to clear clogged proteins from the translocon [115]. Independently of its enzymatic functions, Zmpste24 also interacts with the interferon-induced transmembrane protein (IFITM) family and facilitates the role of these proteins in blocking entry of enveloped RNA and DNA viruses [116, 117]. Zmpste24 appears to impair entry of a range of viruses, including influenza A, Zika and COVID-19 [118, 119]. As a result, mice lacking ZMPSTE24 have increased viral loads and show sensitivity to influenza infection. Whether reduced Zmpste24 levels with age contribute to the increased sensitively of older individuals to viruses remains to be determined.
We propose the following: (1) A-type nuclear lamins are involved in normal aging as well as progeria and (2) it is prelamin A resulting from declining levels of Zmpste24 that drives aspects of normal aging more than expression of progerin. We have described a variety of supporting evidence; however, comprehensive studies remain to be performed to validate this approach. It is critical to address this question given the dramatic increase in the aging population worldwide and the accompanying chronic diseases for which aging is the biggest risk factor. If the lamin A processing pathway can be validated as a driver of normal aging, a variety of new therapeutic approaches will be feasible to extend healthspan. Moreover, this may explain the longevity benefits associated with known interventions, including mTOR inhibitors and Sirtuin activators, among others. To validate this theory, more comprehensive studies are needed to confirm that Zmpste24 levels and activity decline with aging in animal models and humans and that this is associated with elevated prelamin A levels. These studies need to be performed under optimal conditions (for instance physiological oxygen levels for cell culture) and with the best possible reagents without which interpretation of results is more complicated. Antibodies specific for prelamin A include 3C8 [120] and PL-1C7 [9]. Antibodies specific for Zmpste24 include PA1-16965 [113], 205-8C10 (Daiichi Chemical), and ab38450 (Abcam). We also note that many publications show prelamin A levels without accompanying levels of lamin A and/or without detection of a slower migrating prelamin A band. This precludes determination of ratios of different lamin A isoforms. It may be the ratio of prelamin A to mature A-type lamins that best define associated phenotypes and this should be measured in research studies, if at all possible. In mice, Zmpste24 and prelamin A levels should be carefully examined in multiple tissues of mice at a variety of ages. Ideally, a variety of associated factors should be analyzed, including levels of (1) other lamin isoforms, (2) microRNAs associated with A-type lamin and ZMPSTE24 expression, (3) senescence factors and (4) markers of the activity of related pathways such as mTOR. If, as expected, declines in Zmpste24 levels are observed, it may be worth engineering mice lacking the 3′UTR sites for miR-141-3p and miR-335 binding in ZMPSTE24 [42]. Another genetically modified mouse model of interest would be engineered to overexpress ZMPSTE24 systemically or in a tissue-specific manner. The prediction would be that these mice would have improved healthspan and lifespan. Human studies are also needed, including further analysis of cells isolated from humans at different age ranges for the same parameters as those described for murine studies. In addition, muscle or skin biopsies should be tested from different age ranges. Muscle may be of particular importance since mTOR signaling is known to increase in this tissue with age [121]. Finally, human longevity intervention studies should consider examining the levels of prelamin A and Zmpste24 whenever possible. Interventions could include microRNA therapeutics, for instance mimicking the effects of miR-9 to reduce LMNA expression [122, 123] or inhibiting miR-141-3p through antimiRs [42, 124], strategies to enhance degradation of prelamin A, for instance by driving autophagic clearance [101], or altering transcription levels of ZMPSTE24. The appropriate strategy will clearly await a better mechanistic understanding regarding the reasons Zmpste24 levels decline with age. Understanding the pathways that modulate the aging process is critical toward developing strategies to extend human healthspan, and to assist individuals with progeroid disorders. It has long been debated to what extent the mechanisms of aging and progeria overlap. Loss of Zmpste24 may be a connecting feature and, if correct, serve as a target for present and future longevity interventions. |
PMC9648803 | 36279396 | He Tai,Xiao-Zheng Cui,Jia He,Zhi-Ming Lan,Shun-Min Li,Ling-Bing Li,Si-Cheng Yao,Xiao-Lin Jiang,Xian-Sheng Meng,Jin-Song Kuang | Renoprotective effect of Tanshinone IIA against kidney injury induced by ischemia-reperfusion in obese rats | 20-10-2022 | renal ischemia-reperfusion,acute kidney injury,Tanshinone IIA,mitochondrial dysfunction,apoptosis | Objective: Obesity enhances the frequency and severity of acute kidney injury (AKI) induced by renal ischemia-reperfusion (IR). Tanshinone IIA (TIIA) pre-treatment was used to alleviate renal injury induced by renal IR, and whether TIIA can attenuate renal cell apoptosis via modulating mitochondrial function through PI3K/Akt/Bad pathway in obese rats was examined. Methods: Male rates were fed a high-fat diet for 8 weeks to generate obesity, followed by 30 min of kidney ischemia and 24 h reperfusion induced AKI. The male obese rates were given TIIA (5 mg/kg.d, 10 mg/kg.d, and 20 mg/kg.d) for 2 weeks before renal IR. Results: TIIA alleviated the pathohistological injury and apoptosis induced by IR. In addition, TIIA improved renal function, inflammatory factor, and balance of oxidation and antioxidation in obese rats after renal IR. At the same time, TIIA can inhibit cell apoptosis by improving mitochondrial function through the PI3K/Akt/Bad pathway. Mitochondrial dysfunction was supported by decreasing intracellular ATP, respiration controlling rate (RCR), mitochondrial membrane potential (MMP), and mitochondrial respiratory chain complex enzymes, and by increasing ROS, the opening of mitochondrial permeability transition pore (mPTP), and the mtDNA damage. The injury to mitochondrial dynamic function was assessed by decreasing Drp1, and increasing Mfn1/2; and the injury of mitochondrial biogenesis was assessed by decreasing PGC-1, Nrf1, and TFam. Conclusions: Renal mitochondrial dysfunction occurs along with renal IR and can induce renal cell apoptosis. Obesity can aggravate apoptosis. TIIA can attenuate renal cell apoptosis via modulating mitochondrial function through PI3K/Akt/Bad pathway in obese rats. | Renoprotective effect of Tanshinone IIA against kidney injury induced by ischemia-reperfusion in obese rats
Objective: Obesity enhances the frequency and severity of acute kidney injury (AKI) induced by renal ischemia-reperfusion (IR). Tanshinone IIA (TIIA) pre-treatment was used to alleviate renal injury induced by renal IR, and whether TIIA can attenuate renal cell apoptosis via modulating mitochondrial function through PI3K/Akt/Bad pathway in obese rats was examined. Methods: Male rates were fed a high-fat diet for 8 weeks to generate obesity, followed by 30 min of kidney ischemia and 24 h reperfusion induced AKI. The male obese rates were given TIIA (5 mg/kg.d, 10 mg/kg.d, and 20 mg/kg.d) for 2 weeks before renal IR. Results: TIIA alleviated the pathohistological injury and apoptosis induced by IR. In addition, TIIA improved renal function, inflammatory factor, and balance of oxidation and antioxidation in obese rats after renal IR. At the same time, TIIA can inhibit cell apoptosis by improving mitochondrial function through the PI3K/Akt/Bad pathway. Mitochondrial dysfunction was supported by decreasing intracellular ATP, respiration controlling rate (RCR), mitochondrial membrane potential (MMP), and mitochondrial respiratory chain complex enzymes, and by increasing ROS, the opening of mitochondrial permeability transition pore (mPTP), and the mtDNA damage. The injury to mitochondrial dynamic function was assessed by decreasing Drp1, and increasing Mfn1/2; and the injury of mitochondrial biogenesis was assessed by decreasing PGC-1, Nrf1, and TFam. Conclusions: Renal mitochondrial dysfunction occurs along with renal IR and can induce renal cell apoptosis. Obesity can aggravate apoptosis. TIIA can attenuate renal cell apoptosis via modulating mitochondrial function through PI3K/Akt/Bad pathway in obese rats.
As a common complication of the critically ill, acute kidney injury (AKI) presents a critical condition that can even cause danger to life [1]. As a significant health problem, AKI can be induced by the damage of ischemia/reperfusion (IR) induced by transplanted and native kidneys, and by increasing mortality or morbidity [2]. Because hyperuricemia, diabetes and hyperlipidaemia are tightly related with obesity which leads to insulin resistance (IR) and hypertension, they are thought to be chronic hyperinflammatory [3], which raises the incidence and severity of the diseases [4]. As a proverbial hazard factor for cardiovascular morbidity [5], hyperlipidemia can promote the process of all kinds of glomerular diseases [6]. Several lipid lowering tests have demonstrated that hyperlipidemia treatment may be an important means for managing chronic kidney diseases in early period [7]. In addition, the early period of AKI can occasionally combine with hyperlipidemia [8], while the pathobiological sense of hyperlipidemia in renal IR is indecipherable. Numerous studies demonstrated that the mechanism of IR (especially in the nerve and heart) is intently related to mitochondrial dysfunction [9, 10]. Nevertheless, there is no study available to explore the mitochondrial dysfunction of renal cells caused by renal IR. As a main active composition of Salvia miltiorrhiza Bunge, the reduction of oxidant stress and inflammatory response are the major biological functions of Tanshinone IIA (TIIA, C19H18O3, CAS: 568-72-9) [11]. Besides, TIIA can play a protective function on myocardial ischemia [12]. Some researchers demonstrated that TIIA can repress the mitochondrial permeability transition pore (mPTP) opening, thus achieving the goal of liver protection and cardioprotection [13, 14]. TIIA pre-treatment attenuates IR-induced renal injury through antioxidizing capability and anti-inflammatory activity [15]. The survival effect is performed by a phosphoinositide-3 kinase (PI3K), which relies on Akt activation and kinase phosphorylation. Bad phosphorylation can be restrained. Besides, PI3K plays a significant role on the signaling of growth factors. With the activation of multiple physiochemical and cytokines factors, PI3K can generate myoinositol which can be seen as the second messenger; moreover, Akt performs a significant action on a lot of biological courses including cell growth, cell cycle, apoptosis, and metabolism [16]. Inhibiting mitochondria-mediated apoptosis can be achieved via activating PI3K/Akt/Bad signaling pathway [17]. Nevertheless, there are few studies about the renal protection via controlling mitochondrial function through the PI3K/Akt/Bad pathway. In this context, the current research focuses on exploring a way of AKI (caused by IR) induced mitochondrial dysfunction in the kidney, employing PI3K/Akt/Bad, to cross the bridge between mitochondrial dysfunction and apoptosis to find a novel treatment plan.
We dissolved TIIA (The first Pharmaceutical company in Shanghai) with deionized water to get the 5 mg/ml storage solution. All the storage solution must be used promptly.
SD male rats (weighing 180 g-220 g and aged 8 weeks) were applied to proceed our studies. The cages with a stable environment of 20 ± 3° C temperature, 45-65 % humidity, and dark (12 h) /light (12 h) (lights on 06:00 h-18:00 h) cycle were used to keep the rats. The rats were raised with water combined with pellet diet. We divided 60 rats into 6 groups as following: sham operation group, IR/IR (obese) groups, and TIIA (5, 10, and 20 mg/kg.d) groups. Each group contained 10 rats. All the 60 rats in the 6 groups were given conventional maintenance feed for 2 weeks. Two groups of rats (IR and sham groups) were still fed with conventional feed for 8 weeks; however, the remaining 4 groups were given high-fat diet (HFD) feed for 8 weeks. The rats (sham group and IR/IR (obese) groups) were provided with deionized water. We supplied the rats in the remaining 3 groups with different dosages of TIIA for 2 weeks before renal IR. The compositions of HFD are as following: 13 % fiber, 11 % unsaturated fat, 25 % fat containing 44 % carbohydrate, 18 % protein, ash, and other constituents [18]. The rats which had 30 % increase in body weight were chosen for the subsequent research [19].
We anesthetized rats (isoflurane through inhalation anesthesia), applied pinching paw/tail to assess the effect of anesthetic, and then dissected the abdomen in order to reveal the right kidney. Then, we detached renal pedicle to reveal vessels and ligating renal vessel with 3-0 silk suture, and then right kidney was resected. We exposed the left kidney and separated the vessels, followed by clamping off the renal artery (left) for 30 min in order to establish an ischemia model. After ischemia for 30 min, we removed clip to reperfuse 24 h. We observed the left kidneys for 15 min in order to ensure the reperfusion that showed the color resumed red [20]. We used 3-0 silk suture to close an abdominal incision and used the heat pad to preserve stable 37° C throughout the whole surgical process. Rats of sham group underwent the whole surgical procedure without clamping off the left artery [21].
We used arterial blood to detect the renal function containing BUN and serum Cr with kits (Tiangen Biotech Co., Ltd., Beijing, China). Arterial blood (0.5 ml) was extracted from abdominal aorta (after the reperfusion period) to detect IL-1β and TNF-α via related kits (KHB, Shanghai, China). The concentration of MDA and the activity of SOD were detected using commercial kits (Beyotime, Shanghai, China).
The method of hematoxylin-eosin (HE) staining was managed according to previous experiments; after embedding with paraffin, the renal tissues were incised into thin sections (5 μm). The renal tissues were infiltrated with 4 % paraformaldehyde (24 h) and then diverted to 70 % ethanol. Then, H&E staining was applied to stain the renal tissues for detecting the renal tissues with light microscopy [21]. After immersing with 4 % paraformaldehyde (24 h) and then distracted to 70 % ethanol, the renal tissues were stained with H&E staining to observe renal tissues with light microscopy [21]. We assessed the degree of injury of kidney tissues (outer medulla, cortex, and inner medulla) by light microscopy in the means as follows: the grading of tissues injury was operated in terms of cell necrosis, cast formation in tubules, Bowman space enlargement, and vascular congestion. The highest grading of enlargement in Bowman space was compared with the sham operation group which was graded as 100% injury; the other rats were compared correspondingly. Vascular congestion, cell necrosis and tubular cast formation were graded in light of the percentage of the involved area as follows: 0, no damage; 1, 25 %; 2, 25–50 %; 3, 50 –75 %; and 4, 75 % [22].
We took out the renal tissues from abdominal cavity immediately following anesthesia, and then sliced the renal tissues into snippets (1 mm3). The specific process was conducted based on previous studies [23].
Renal tissues were embedded with paraffin and then cut into thin sections (5 mm). Then, PBS was used to incubate the sections for 2 h at room temperature followed by incubation overnight (4° C) via the following two antibodies: rabbit anti-Bax (1: 50) and rabbit anti-Bcl-2 (1: 100). Finally, we used the secondary antibodies (Servicebio, Wuhan, China) to incubate the thin sections for 2 h at 4° C, and used a fluorescence microscope (Leica) to observe the results [23].
We used the TUNEL assay to examine apoptosis. As recorded in HE staining [24], thin sections (5 μm) were used for such staining. After deparaffinization and rehydration, the samples were added to TUNEL reaction mixture using the protease K (10 μg/ mL) for handling the sections (15 min), and then were incubated at 37° C for 60 min in dark. Following cleaning, DAPI (0.1 μg/mL) was employed to stain cell nuclei. Then, a fluorescence microscope (T2130, Solarbio, Beijing, China) was employed to observe the samples. We observed eight random visual fields in a blinded manner to calculate the number of TUNEL-positive cells.
A fluorescent caspase-specific Detection Kit (Solarbio, Beijing, China) was employed to examine the activity of caspase-3/9 in the renal tissues. We added 10 mg portion of renal tissue proteins to the reaction buffer for incubation (2 h) at 37° C. Then, a fluorimeter (405 nm) was employed to quantify the releasing of Enzyme-catalyzed.
According to previous studies on the liver [25], the rats were sacrificed after anesthetizing with 120 mg/kg thiopental sodium through intra-peritoneal injection and drawing blood from the abdominal aorta. Then, the kidney was quickly removed and laid on the ice-cold isolated buffer (PH 7.4). After shearing, renal tissues (50-100 mg) were rinsed in an isolated buffer. In order to preserve mitochondrial wholeness, the whole procedure was operated at 4° C. With centrifugation (700×g) for 10 min, we collected supernate then centrifuged (7000×g) for 10 min twice. Then, we threw away supernate, resuspending to wash the mitochondria pellet with isolated buffer (5 ml) and centrifuged (7000×g, 10 min) twice. We obtained clean mitochondrial suspension for preservation in mitochondrial preservation solution (1 mM EDTA, 20 mM sucrose, 5 mM HEPES, 100 mM KCl, 10 mM KH2PO4, and 2 mM MgCl2) to generate a mitochondria suspension (the protein concentration is 5 mg/ml) which was placed on ice for timely use. Bicinchoninic acid (BCA) test kit (Beyotime, Shanghai, China) was used to detect the concentration of protein in the renal mitochondrial suspension for ensuring a stable protein level (100-1000 μg/ml). We used mitochondria suspension to measure MMP [25], the opening of mPTP [26], damaged mtDNA, ROS [27], mitochondrial oxygen consumption rate [28], RCR [28], mitochondrial respiratory chain complex enzymes (I, II, III, IV, and V) [23], and ATP.
We used a Trizol kit to separate total genome RNA. We used spectrophotometry (260 nm) to assess the quality of isolated RNA. M-MLV Reverse Transcriptase Kit and total RNA (1 μg) were used to operate the reverse transcription. In brief, total reaction volume (40 μL) was applied in PCR system according to the following reaction process: at 72° C for 3 min, 42° C for 90 min, and 70° C for 15 min, and then preservation at 4° C. The method of RT-qPCR was employed to measure the copy number of specific gene transcription level with templates of cDNA. The PCR was operated on a Rotor-Gene Q Sequence Detection System with SYBR Premix Ex TaqII [29] in 20 μL system (including 10 μL SYBR Premix Ex Taq II + 1μL synthetic cDNA and 0.5 μM primers) according to the following procedure: 95° C (10 min), 95° C (10 s), 40 cycles, 60° C (15 s), 72° C (20 s), and 72° C (10 min). The level was counted with GAPDH as control [30]. The applied PCR primer sequences (two pairs) were presented in Table 1.
We used RIPA Lysis Buffer to extract the total proteins from renal tissues. The protein level was detected using BCA Protein kit. An equal gauge of total protein was dominated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (8–12 %); and then the proteins were transferred to the PVDF membrane. After sealing in skim milk solution, the membrane was incubated overnight respectively with anti-GAPDH, anti-PI3K, anti-p-Akt, anti-Akt, anti-p-Bad, anti-Bad, anti-Bax, anti-Bcl-2, anti-Cyt-c, anti-PARP, anti-caspase-9/3, anti-Drp1, anti-Mfn1/2, anti-Tfam, anti-PGC-1, and anti-Nrf1 antibodies (Table 2 demonstrated all the antibodies), and then incubated with secondary HRP-conjugated goat anti-rabbit antibodies. We used an augmented chemiluminescence kit to visualize the proteins, and performed the densitometric analysis using Alpha View software.
SPSS statistical package was employed to operate statistical analysis. Data was displayed in form of mean ± standard deviation. One-way analysis of variance was conducted to compare among 6 separate groups; while the two-to-two comparison among groups was employed to analyse variance. We used Lsd-t test to perform multiplicate comparison between 6 groups. The difference with p < 0.05 was defined to be statistically significant.
The datasets analyzed during the current study are available from all the authors on reasonable request.
In order to further evaluate the renal damage and protective action of TIIA, we measured the renal function (Figure 1A), oxidizing substance (SOD and MDA) (Figure 1B), and inflammatory factor (Figure 1C). BUN and Cr were elevated in IR/IR (obese) groups (p < 0.05), while BUN and Cr were reduced via pre-treatment with TIIA, especially with the dose of 20 mg/kg.d TIIA (p < 0.05) (Figure 1A). Rats renal SOD was decreased in IR/IR (obese) groups, especially in the IR (obese) group (p < 0.05). Nevertheless, it was elevated via pre-treatment with TIIA, especially with the dose of 20 mg/kg.d TIIA (p < 0.05) (Figure 1B). Inflammatory factor (TNF-α and IL-1β) levels of the rats were elevated in the IR/IR (obese) groups, especially in the IR (obese) group (p < 0.05), yet were decreased via pre-treatment with TIIA, especially with the dose of 20 mg/kg.d TIIA (p < 0.05) (Figure 1C).
We evaluated protective action of TIIA on renal damage following IR of obese rats and observed kidney tissue using the hematoxylin-eosin (HE) staining (Figure 1D). IR (obese) and IR led to a significant expansion in Bowman space. Cortical thick ascending limb of the loop of Henle and proximal tubule had a significant injury. IR (obese) and IR caused significant injury in the outer medulla (tubular cast formation and vascular congestion, medullary thick ascending limb of loop of Henle, and perirenal tubule) and inner medulla (vascular congestion and tubular cast formation). Pre-treatment with TIIA significantly decreased the degree of renal tissue injury in the inner, outer medulla, and cortex. IR (obese) and IR led to a significant increase in total histopathologic scale (p < 0.05). The total histopathologic scale was reduced via pre-treatment with TIIA, especially with the dose of 20 mg/kg.d TIIA (p < 0.05), where more renal tubules were swollen (paired yellow arrow) were showed following IR (Figure 1D) and Table 3.
We used the TUNEL assay to evaluate the protective action of TIIA on renal tissue cell apoptosis induced by IR (Figure 2A). Apoptotic cell numbers of renal tissues in IR/IR (obese) groups were increased compared with those in sham group (p < 0.05). However, pre-treatment with TIIA reduced cell apoptosis in renal tissues (Figure 3A). Caspase-9/3 was significantly activated in the IR/IR (obese) groups compared with sham group (p < 0.05). Nevertheless, pre-treatment with TIIA decreased the activity of caspase-9/3 (Figure 2B, 2C). The cleaved caspase-9/3 in renal tissues in IR/IR (obese) groups were elevated compared with sham group (p < 0.05). Nevertheless, pre-treatment with TIIA reduced the relative levels of cleaved caspase-9/3 (p < 0.05) (Figure 2D).
Bax in mRNA and protein levels were increased in IR/IR (obese) groups (p < 0.05). Bax in mRNA and protein levels were decreased following pre-treatment with TIIA (p < 0.05). Bcl-2 in mRNA and protein levels were decreased in IR/IR (obese) groups (markedly reduced in the IR (obese) group) (p < 0.05) (Figures 3A, 3B). The immunofluorescence results of Bax/ Bcl-2 levels of renal tissues demonstrated that Bax levels increased. However, Bcl-2 levels were reduced in IR/IR (obese) groups. Bax level was reduced, yet Bcl-2 was increased, following the pre-treatment with TIIA (Figure 3C).
IR increased ROS concentration and opening of mPTP (%) compared with sham group (p < 0.05). The ROS concentration was decreased via pre-treatment with TIIA (p < 0.05). IR decreased RCR, mitochondrial oxygen consumption rate, and MMP compared with the sham group (p < 0.05). Pre-treatment with TIIA significantly increased these factors (p < 0.05). The ratio of long/short fragments was reduced in IR/IR (obese) groups (p < 0.05). Pre-treatment with TIIA increased the ratio of long/short mtDNA fragments (p < 0.05) (Figure 4A). IR reduced ATP and mitochondrial respiratory chain complex enzymes compared with sham group (p < 0.05). Pre-treatment with TIIA significantly elevated the activity of these enzymes (p < 0.05) (Figure 4B). We assessed mitochondrial function to further evaluate renal damage and protective action of TIIA. A transmission electron microscope was used to observe the changes in the morphology of the mitochondria (Figure 4C). The photographs of electron microscopic (10,000× and 40,000×) of renal tissues demonstrated that renal cells in IR/IR (obese) groups displayed abnormal mitochondrial morphology in the form of swelling and even membrane rupture (paired yellow arrows) following renal IR. The sham group displayed normal mitochondrial morphology (single yellow arrow). The percentage of damaged mitochondria of renal tissue in IR/IR (obese) groups was increased (p < 0.05). Nevertheless, the pre-treatment with TIIA can reduce the percentage of damaged mitochondria (Figure 4C).
The nucleo respiratory factor1 (Nrf1), PPARγ coactivator-1-α (PGC-1α), and transcription factor A of mitochondrial (Tfam) were chosen to indicate biogenesis. Mitofusins (Mfns (fusion); Mfn1/2) and dynamin-related protein 1 (Drp1; fission). The results demonstrated that Nrf1, PGC-1α, Drp1, and Tfam mRNA levels were reduced in the IR/IR (obese) groups (p < 0.05). Pre-treatment with TIIA increased these mRNA levels (p < 0.05). mRNA levels of Mfn1/2 were elevated in the IR/IR (obese) groups (p < 0.05). The mRNA levels of those indexes were reduced via pre-treatment with TIIA (p < 0.05) (Figure 5A). Western blot of dynamics and biogenesis indexes demonstrated similar results (Figure 5B).
IR/ IR (obese) increased the mRNA level of PARP and Cyt-c significantly (p < 0.05). The mRNA level of these indexes was decreased following pre-treatment with TIIA (p < 0.05). IR/ IR (obese) decreased the mRNA level of PI3K, Bad, and Akt significantly (p < 0.05). The mRNA level of these indexes was increased following pre-treatment with TIIA (p < 0.05) (Figure 6A). Western blot of those genes showed similar results (Figure 6B). TIIA activated phosphorylation of Akt and Bad (increasing ratio of p-Akt/Akt and p-Bad/Bad) (Figure 6B).
The new findings of our research include the aggravation of renal injury following I/R when accompanied with obesity, the potential protective action, and mechanisms of TIIA on renal I/R damage. We confirmed this using in vivo model. As compared with non-obesity rats, mitochondrial function was hypofunction in obese rats. This became more obvious following I/R. Cell injury and apoptosis were shown to be more severe in obese rats following I/R. According to the above results, it is the first study that explored the combined influence of obesity and renal I/R on mitochondrial function in renal tissues. Oxidative stress raised the levels of ROS and inflammation via releasing proinflammatory mediators during the reperfusion period. These indexes perform a meaningful function on the pathophysiological process of renal IR. For injury mechanism of renal IR above, a few anti-inflammatory and anti-oxidants medicaments, which can increase the survivability of IR damage [31], were found to be effectively decreased. Modern medical techniques have confirmed that as a well-known herbal medicine, Salvia miltiorrhiza Bunge can eliminate the toxic substance in the blood, accelerate the activity of a fibrinolytic enzymes, reduce the blood viscosity, and protect the cardiovascular system [32]. TIIA is the primary active component in Salvia miltiorrhiza Bunge. TIIA pre-treatment can relieve renal damage caused by IR via reducing the expression of inflammation, caspase-9/3, and myeloperoxidase (MPO) [15]. TIIA can decrease the release of Cyt-c and the production of ROS, inactivate caspase-3, reduce apoptotic, and inhibit mPTP opening. Inhibited opening of mPTP is the key for improving mitochondrial function. so it has been fundamental to hint that mPTP can be seen as an objective for curing, which can be ameliorated at the beginning of reperfusion [33]. It is common knowledge that mitochondrial dysfunction (especially the opening of mPTP) performs an important function in raising damage after renal IRI [34]. The opening of mPTP causes decreased MMP and the mitochondrial swelling which inhibits oxidative phosphorylation. The opening of mPTP is performed via binding to the CyP-D. Previous studies on the heart have shown that CsA repressed IRI via binding CyP, independently of anti-calcineurin properties, hence repressing mPTP opening [35]. In this research, we assumed TIIA can perform a protective role in AKI induced by IR via improving the mitochondrial function (inhibiting mPTP opening) and downregulation of inflammation. Our research showed that feeding rats with HFD for 8 weeks induced obesity in modality of weight gain and perirenal fat cumulation. HFD could lead to inflammation, hyperlipidemia, and imbalance of oxidative stress but failed to injury renal histological structure and function [21]. However, with the condition of IR, hyperlipidemia could aggravate the damage of renal histological structure compared with non-hyperlipidemia in our study. In addition, we found that IR could induce the imbalance between SOD and MDA (reducing the SOD and increasing MDA); and with the condition of IR, hyperlipidemia could aggravate the imbalance. When using the TIIA as the protective agent, the imbalance induced by renal IR combined with hyperlipidemia could be improved (Figure 1B). HE staining for rat renal tissue showed that IR and IR (obese) led to significant enlargement in Bowman space. The proximal tubule and cortical thick ascending limb of the loop of Henle had a significant injury. IR and IR (obese) caused significant injury in the outer medulla (vascular congestion, tubular cast formation, perirenal tubule, and medullary thick ascending limb of loop of Henle) and inner medulla (tubular cast formation and vascular congestion). Pre-treatment with TIIA could significantly decrease the degree of damage (Figure 1D and Table 3). TUNEL assay demonstrated that renal IRI led to cell apoptosis (Figure 2A). Nevertheless, TIIA reduced the number of apoptotic cells (Figure 2A). Caspase-9/3 are the influential performers of apoptosis correlated to terminal period of apoptosis. The activity of caspase-9/3, which was detected in the present study, is a significant signal of apoptosis. IR and IR+obesity can significantly activate Caspase-9/3, particularly the IR+obesity (p < 0.05). Nevertheless, TIIA decreased the activity of caspase-9/3 in renal tissues (Figure 2B, 2C). We detected the relative levels of cleaved caspase-9/3 (Figure 2D) as the active form of caspase-9/3. Cleaved caspase-9/3 can guide apoptosis, while IR can increase the levels of cleaved caspase-9/3. However, pre-treatment with TIIA can reduce the levels of cleaved caspase-9/3 (p < 0.05) (Figure 2D). Unlike other organs [36], kidney is surrounded by perirenal adipose tissue, which may also be the primary source for inducing a subclinical inflammatory response in patients with chronic kidney disease [37]. In our study, I/R increased the TNF-α and IL-1β values. With the condition of IR, hyperlipidemia increased the IL-1β and TNF-α levels further, and they were reduced by pre-treatment with TIIA (Figure 1C). As the leading cause of the AKI, I/R induced apparent renal injury at 24 hr, presenting significant increase of Cr and BUN in our research. With the condition of IR, hyperlipidemia could increase Cr and BUN, they were decreased by pre-treatment with TIIA (Figure 1A). In comparison, I/R induced apparent renal injury at 48 hr in previous studies [22]. Although there are various types of researches about IR-induced AKI, the potential mechanism of injury has not been absolutely understood [38]. Renal I/R damage evokes correlated series of events in the kidney, which lead to renal damage and even death of renal tubular cells. Tubular cell damage and endothelial dysfunction via the apoptotic pathway, imbalance between oxidation and antioxidation, ATP depletion, and proinflammatory cytokines have been explored [39]. Mitochondrial apoptotic signaling pathways (NF-jB/p53/PUMA) perform an important function in renal I/R injury, while renal I/R can induce mitochondrial dysfunction [40]. In this study, IR could induce the abnormal mitochondrial morphology (membrane rupture or swelling), and then increase the percentage of damaged mitochondria; whereas TIIA could protect the mitochondria from renal IR combined with hyperlipidemia (Figure 4C). In the future, we will study the correlation between NF-jB/p53/PUMA and PI3K/Akt/Bad. Mitochondrial oxygen reduction equilibrium and homeostasis play a crucial role in the ischemic AKI of pathophysiological changes. Impaired mitochondrial function and imbalance between oxidation and antioxidation are the key factors of renal I/R injury [41]. With the ischemic condition, anoxia and substrates repress mitochondrial respiration. Then, renal tissues must change to glycolysis metabolism, which dramatically decreases the ability for producing ATP quickly. Cell swelling caused by ATP deficiency raises an osmotic gradient, which actuates water into the mitochondrial matrix [42]. Furthermore, hyperlipidemia induces a marked generation of deleterious mitochondrial ROS cand causes oxidative damage, which activates caspase-dependent apoptosis through mitochondrial pathway; this further impairs MMP and then raises renal podocyte apoptosis [43]. The outcomes of mitochondrial dysfunction induces renal IR injury. In our research, we built model of renal IRI in obese rats followed by pre-treatment with TIIA. We detected the MMP, opening of the mPTP (%), mtDNA, ROS, RCR, ATP, and so on. Those indexes were thought to be the sign of evaluating mitochondrial function. In our study, RCR, MMP, oxygen consumption rate, ATP, and mitochondrial respiratory chain complex enzymes were reduced by IR/IR (obese). However, ROS and opening of the mPTP (%) were increased by IR/IR (obese). In comparison, pre-treatment with TIIA could improve the above indexes of mitochondrial function (Figure 4A, 4B). The copy number of mtDNA of every mitochondrion remains steady. Therefore, total copy number of mtDNA can be used to appraise mitochondrial quantity [44, 45]. Despite the fact that the mechanism of scathed mtDNA is ambiguous, mtDNA is close to respiratory chain. Therefore, mtDNA is more susceptible when revealed to oxidative reaction. We studied the damage degree of mtDNA via counting the ratio of long/short fragments in this study. The ratio of long/short fragments was obviously increased by IR (obese); but after pre-treatment with TIIA, the ratio could be increased (Figure 4A). As a significant adaptation of exposing to chronic energy deficiency, mitochondrial biogenesis could be operated by many complicated factors, such as Tfam and Nrf1. The Nrf1 can foster the expression of transcription of nuclei-encoded mitochondrial proteins. Containing these refers to oxidative phosphorylation and respiratory complexes. Tfam increases gene transcription and DNA replication in mitochondria through directly binding to the mitochondrial genome. As a crucial transcriptional co-activator, PGC-1α can operate critical elements containing Tfam and Nrf1, which can increase mitochondrial biogenesis [46]. When the levels of above gene vary, mitochondrial biogenesis will get chaotic. As shown in Figure 5, IR could reduce the expression of Nrf1, Tfam, and PGC-1α. TIIA increased the level of Nrf1, PGC-1α, and Tfam. After TIIA interference, an ample power supply reduced ROS and increased mitochondria biogenesis elements, which performed synergistically to give rise to improved lack of intracellular energy furnish. Normally, deleterious irritation containing aging, energy limitation, and oxidizing reaction can damage mitochondria to be closed up in autophagosomes, fused to lysosomes, and finally degraded. Abnormalities of lautophagy in mitochondria can raise the number of damaged mitochondria [25]. Typically, mitochondria go through the dynamic course containing fission and fusion. The course is significant to preserve stable varieties of dimensions, fashion, and networks in mitochondria, which are governed by related proteins containing Drp1 and Mfn1/2 [47]. Our results (Figure 5) showed that the expressions of Drp1 and Mfn1/2 changed in opposite orientations following IR, demonstrating a disordered balance of fission-fusion of mitochondria. Mfn1/2 and Drp1 were thought to perform a critical function respectively on mitochondrial fusion and fission. As such, we found an increase of Mfn1/2 and a decrease of Drp1 following renal IR in renal tissues. PI3K/Akt/Bad signalling pathway plays a crucial function in repressing apoptosis regulated by mitochondria [17]. Because of their close connection, the effect of PI3K was studied in this study. As a phosphatidylinositol kinase, PI3K can activate phosphatidylinositol kinase and threonine-specific proteins kinase [48]. After being activated, phosphatidylinositol family members in cell membrane can be phosphorylated and Akt (downstream molecule) can be activated, which can then activate Akt phosphorylates (Ser136/Ser112) residues Bad protein [49]. Phosphorylated Bad segregates from the apoptosis-promoting complex and then shapes the protein complex (14-3-3), inducing inactivating the apoptosis-promoting role and then restraining apoptosis [50]. The results showed that TIIA effectively upregulated the PI3K/Akt/Bad pathway proteins and then upregulates PI3K and p-Akt proteins and downregulates the proteins (Cyt-c and PARP) (Figure 6). The above results showed TIIA could adjust mitochondrial function, and then inhibit cells apoptosis caused by renal IRI through activating PI3K/Akt/Bad pathway. Immunofluorescence results of the proteins (Bax/Bcl-2) expression of kidney tissues showed that renal IR increased Bax and decreased Bcl-2. Nevertheless, Bax can be decreased, while Bcl-2 increased, by pre-treatment with TIIA (Figure 3). In our study, we used isolated mitochondria and showed that IR accelerated ROS, which injured mtDNA, and by such means injured mitochondrial respiratory function, biogenesis, and dynamic function, and produced ROS. The abnormal mitochondria were caused by opening mPTP following renal IR. The opening of mPTP could induce flow back of proton from mitochondrial membrane space to matrix, hence decreasing MMP and ATP and leading to metabolic abnormalities. The reducing of ATP and MMP and the increasing of Cyt-c were caused by opening mPTP, inducing metabolic abnormalities and apoptosis. Nevertheless, pre-treatment with TIIA could restrain apoptosis via regulating mitochondrial function through activating the PI3K/Akt/Bad pathway of obese rats (Figure 7). |
PMC9648804 | 36309909 | Shofiul Azam,In-Su Kim,Dong-Kug Choi | α-Synuclein upregulates bim-mediated apoptosis by negatively regulating endogenous GCN5 | 27-10-2022 | synucleinopathy,Parkinson’s disease,histone acetyltransferase,α-synuclein | α-synuclein (αS) is a β-sheet intracellular protein that has been implicated as a major pathological hallmark of Parkinson’s disease (PD). Several studies have shown that overexpression of αS causes dopaminergic cell loss; however, the role of αS in apoptosis remains not fully known. Therefore, this study aims to address the mechanisms of the αS overexpression model in apoptosis and to its correlation with PD pathogenesis. Here, we used a human αS (hαS) plasmid to characterize the role of ectopic αS in neuronal apoptosis in sporadic PD in vitro. We found that overexpression of αS transcriptionally upregulated Bim-mediated apoptosis in neuronal SH-SY5Y cells. Interestingly, αS overexpression inhibited general control non-depressible 5 (GCN5), a histone acetyltransferase (HAT), and promoted transcriptional upregulation of Bim. Consequently, co-overexpression of GCN5 in the αS overexpressed model showed a reversal of αS toxicity in neuronal cells. These in vitro findings support the hypothesis of αS-mediated histone deacetylation and dopaminergic neuronal loss in PD. Moreover, our study indicates that therapeutic activation/homeostasis of GCN5 may benefit PD and other α-synucleinopathies. | α-Synuclein upregulates bim-mediated apoptosis by negatively regulating endogenous GCN5
α-synuclein (αS) is a β-sheet intracellular protein that has been implicated as a major pathological hallmark of Parkinson’s disease (PD). Several studies have shown that overexpression of αS causes dopaminergic cell loss; however, the role of αS in apoptosis remains not fully known. Therefore, this study aims to address the mechanisms of the αS overexpression model in apoptosis and to its correlation with PD pathogenesis. Here, we used a human αS (hαS) plasmid to characterize the role of ectopic αS in neuronal apoptosis in sporadic PD in vitro. We found that overexpression of αS transcriptionally upregulated Bim-mediated apoptosis in neuronal SH-SY5Y cells. Interestingly, αS overexpression inhibited general control non-depressible 5 (GCN5), a histone acetyltransferase (HAT), and promoted transcriptional upregulation of Bim. Consequently, co-overexpression of GCN5 in the αS overexpressed model showed a reversal of αS toxicity in neuronal cells. These in vitro findings support the hypothesis of αS-mediated histone deacetylation and dopaminergic neuronal loss in PD. Moreover, our study indicates that therapeutic activation/homeostasis of GCN5 may benefit PD and other α-synucleinopathies.
Parkinson’s disease (PD) is the second most prevalent disease among people over 65 years of age, affecting at least 1% of the population [1, 2]. Lewy pathology, α-synuclein--rich proteinaceous cytoplasmic inclusions, is a histological hallmark of clinical PD and other synucleinopathy [3–5]. α-synuclein (αS) is a cytosolic intracellular protein [6] that is normally expressed at the presynaptic terminals. Several studies have noted nuclear localization of this protein in experimental models [7–9] and in patients with multiple system atrophy, a distinct form of synucleinopathy [10, 11]. One study has shown the physiological role of αS at the nucleus and mechanistically reported the role of the histone-αS complex in neurotoxicity [12]. However, there is a fundamental question that remains unanswered: does αS form any complex with endogenous histone acetyltransferase (HAT). The kinetic balance between HAT and histone deacetylase (HDAC) activities regulates the steady acetylation of histone and non-histone proteins [13], resulting in cellular homeostasis. This balance also regulates cellular fate [14] by regulating different gene expressions and repression. Loss of neuronal homeostasis by losing the HAT: HDAC balance has been viewed as being related to neuronal apoptosis and neurodegeneration [15, 16]. In addition, loss of acetylation is also related to the loss of important HAT members—for example, the general control non-derepressible 5 (GCN5) [17]—resulting in apoptosis. GCN5 was the first identified enzyme with intrinsic HAT activities that is also capable of linking histone acetylation to transcriptional regulation [18, 19]. Supporting the neuroprotective role of GCN5, a study showed that GCN5-mediated acetylation of peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α), protects neuronal cells against MPP+-induced oxidative stress [20]. GCN5−/− mice died during embryogenesis due to a combination of excessive apoptosis [21, 22] and loss of GCN5 transcriptionally upregulated BH3-only protein (Bim) and caspase-dependent neuronal apoptosis [17]. Although the histone-αS complex formation and its role in neurotoxicity are known, the interaction of αS with GCN5 has yet to be discovered. In this study, we investigated the involvement of GCN5 in synucleinopathy. To do so, we used (human) αS-transfected neuroblastoma (SH-SY5Y) cells, in which αS disrupted cell viability. Further, we found that overexpression of αS interferes with the GCN5 basal level, which we predicted could play a critical role in αS-mediated neurotoxicity. Our effort to understand αS-GCN5 interaction has found important pathogenesis of α-synucleinopathy and PD.
Several studies indicated that ectopic αS is a major hallmark of neuronal cell death in PD pathology [23] and that αS overexpression promotes cell death via changing sub-cellular localization [12]. Therefore, we investigated whether our αS (human) plasmid overexpression (Figure 1A) affected the viability of SH-SY5Y neuroblastoma (Figure 1B, 1C). We used DNA fragmentation (DNA ladder) and DAPI staining assay of transiently transfected vectors or αS plasmids. After 48 hours of transfection, αS showed significant chromatin aggregation and increased DNA fragmentation. αS at the nucleus masks H3 histone and inhibits H3 activities at the chromatin level [12]. Also, overexpression of sporadic or familial (A53T and A30P) αS phosphorylates at ser129 reduces cell viability via aggregation [24]. Corroborating previous findings, our data indicate that ectopic αS regulates neuronal cell fate.
The BH3-only protein Bim is a pro-apoptotic protein that has been previously shown to upregulate during apoptosis. Bim induces apoptosis by binding to Bcl-2 or Bcl-xl and antagonizes their anti-apoptotic functions. In the αS+/+ model, both the mRNA and protein levels of Bim increased substantially (Figure 2A, 2B). A previous study demonstrated that Bim genes are transcriptionally regulated by Egr-1 and E2F1 [17, 25]. Therefore, we tested the impact of αS overexpression on Bim transcriptional factors E2F1 and Egr-1 (Figure 2A, 2B). We found that, in both mRNA and protein levels, overexpression of αS significantly upregulated E2F1 but not Egr-1. This was further corroborated by co-immunoprecipitating both transcription factors in non-transfected SH-SY5Y cells (Figure 2C). Immunoprecipitation showed that anti-E2F1 endogenously precipitated anti-αS more intensely than anti-Egr-1. Moreover, Bim is an upstream regulator of the caspase-3-mediated apoptotic signaling pathway. αS overexpression increases the Bim level that inhibits Bcl-2, frees Bax, and activates caspase-3 (Figure 2D).
A recent study reported that GCN5 transcriptionally regulates Bim and downstream caspase-3 activation [17]. Though αS interacts and inhibits neuronally expressed acetyltransferase enzymes CBP, p300, and P/CAF [12, 26], it is generally unknown whether αS and GCN5 endogenously interact. To confirm endogenous binding, we co-immunoprecipitated GCN5 in non-transfected SH-SY5Y cells and probed anti-αS (Figure 3A). Furthermore, we evaluated the impact of αS overexpression on endogenous GCN5 expression in neuronal SH-SY5Y (Figure 3B) and non-neuronal HEK293T cells (Figure 3C). A significant decrease, compared to usual, in GCN5 expression in αS+/+ indicates that GCN5 may be involved in αS-mediated Bim upregulation.
As we found αS+/+ inhibits GCN5 and regulates pro-apoptotic Bim, we tested the hypothesis that GCN5 is involved in Bim’s transcriptional regulation. To test this hypothesis, we co-immunoprecipitated GCN5 and E2F1 and probed anti-αS (Figure 4A). Since the loss of GCN5 upregulates both transcription factors of Bim [17], our results indicate that ectopic αS interacts and inhibits GCN5 and that this upregulates the E2F1 level. Next, we checked whether the knockdown of GCN5 by siRNA affects endogenous αS expression (Figure 4B); however, no significant changes were observed. siGCN5 substantially upregulates Bim, Egr-1, and E2F1 levels but decreases Bcl2 expression in neuronal cells (Figure 4C). These data suggest that GCN5 could be a potential target to reduce neuronal apoptosis in PD and other synucleinopathy.
Until now, this study has indicated that αS overexpression dismantles GCN5 homeostasis, which triggers apoptosis in neuronal cells. Therefore, we hypothesized that GCN5 homeostasis by co-activation could benefit PD patients by ameliorating apoptosis. First, we tested whether GCN5 overexpression (oeGCN5) modulates αS endogenous levels (Figure 5A) and found that oeGCN5 downregulated endogenous αS expression. Later, we co-overexpressed αS and GCN5 (αS+GCN5) in SH-SY5Y cells (Figure 5B) and tested for an apoptotic marker expression (Figure 5C). The co-overexpression of αS and GCN5 were found to substantially reduce transcriptional upregulation of Bim and improve Bcl2 levels, which reduced caspase-3 activation. Subsequently, co-overexpression downregulated neuronal cell death (Figure 5D).
Multiplications of the SNCA gene locus showed autosomal dominant PD, where gene dose was the determinant of severity and onset. This indicates that αS mutations, which alter protein output, are not the determinant of αS pathology—wild-type αS overexpression could also cause the disease. Moreover, overexpression of αS, due to either increased expression or reduced clearance, is a common feature of a sporadic disease condition. Since the first identification of αS, most related studies have focused on αS roles in nerve terminals [27, 28]; however, a definitive role is yet to be found. Nevertheless, some studies have shown that the nuclear localization of αS [7, 8, 12, 29] contributes to its cytotoxic role [12]. In this study, we observed αS immunoreactivity with the important HAT GCN5. We also found that αS suppressed GCN5 endogenous levels in dopaminergic neuronal cells and loss of GCN5 promoted Bim-mediated apoptotic signaling. Bim is a major regulator of the intrinsic apoptosis pathway under both physiological and pathological conditions. Pro-activities and expression of Bim are tightly regulated by several transcription factors, including Fox3a, E2F1, c-Myc, and Egr-1 [30]. Ectopic E2F1 has shown upregulation of Bim expression and neuronal apoptosis [31]. Also, histone deacetylases (HDAC) have been observed targeting the transcription factor E2F1 in the hyperactivation of Bim and apoptosis [32]. αS plays as critical a role as HDAC, which we also demonstrated in the present work. Overexpression of αS activated E2F1 and significantly upregulated Bim expression. In addition, Bim could induce apoptosis directly or indirectly by neutralizing anti-apoptotic Bcl2 and promoting Bax [30]. This indicates that αS overexpression upregulates Bim levels, thus binding Bcl2 and masking Bcl2 anti-apoptotic activities. In the nucleus, αS inhibits H3 histone acetylation [12], promoting neurotoxicity. HAT members—for example, GCN5—are important for maintaining the precious HAT: HDAC balance. Loss of the basal level of HAT might promote histone deacetylation, thus impairing acetylation homeostasis. Moreover, a recent study characterized the anti-apoptotic role of GCN5 [17, 20], which also indicates that inhibition of GCN5 activities could trigger apoptosis. However, until this study, there have been no reports of the direct inhibition of GCN5 by αS. In this study, we found that GCN5 and αS have endogenous protein-protein interaction and that αS+/+ downregulates the basal level of GCN5. Several studies have reported that HAT members modulate neuronal fate: for example, enhanced p3000 HAT activity upregulated PKCδ-mediated neuronal apoptosis [26], and increased Tip60 HAT activities induced APP-mediated apoptosis in Alzheimer’s disease [33]. While the loss of HAT activities promotes apoptosis, loss of the CREB-binding protein (CBP) HAT activity promotes caspase-6-mediated apoptosis [34], and inhibition by MB-3 or knockdown of GCN5 induces Bim-transcription and apoptosis [17]. Our findings indicate that, among the other neurotoxic pathways of αS, GCN5 activity inhibition is involved. We also correlated that loss of GCN5 via αS overexpression enhanced transcription factor E2F1 and activated Bim. Furthermore, αS downregulates anti-apoptotic Bcl-2, frees Bim protein that activates Bax, and permeabilizes the mitochondrial membrane, resulting in caspase-dependent apoptosis [35]. αS has been reported to inhibit multiple HAT activities, but there has been no previous research on GCN5 activities in this context. However, loss of GCN5, but not p300 and Tip60, was found to upregulate apoptosis [17]. Here, we report a protective role of GCN5 in α-synucleinopathy. Co-overexpression of GCN5 rescues neuronal cells via downregulating Bim transcription and reversing caspase-3-mediated apoptosis. In conclusion, we showed a new role of αS overexpression in apoptosis. We also found that αS interacts with and inhibits GCN5 and loss of GCN5 promotes Bim-mediated apoptosis in neuronal cells (Figure 6). Promisingly, HDAC inhibitors have shown anti-apoptotic activities in many neurodegenerative diseases by preventing histone deacetylation. Yet, we found a new pathway in which the development of the GCN5 activator would build HAT: HDAC homeostasis and could reverse neuronal apoptosis in Parkinson’s disease pathogenesis. Since αS acts in the nucleus to promote neurotoxicity, mostly by interfering with histone acetylation and HAT member activities, our findings suggest that the development of GCN5-dependent therapeutics might benefit PD and other synucleinopathy.
Human neuroblastoma SH-SY5Y cells were obtained from the American Type Culture Collection (ATCC; Manassas, VA, USA). The neuroblastoma cells were cultured in DMEM/F12 (1:1) supplemented with 100 U/mL penicillin/streptomycin and 10% (v/v) inactivated FBS in an incubator with the conditions maintained at 37°C and 5% CO2. The cells were trypsinized (0.05% trypsin-EDTA) after reaching 80–90% confluence for sub-culture, and the media was replaced every 2 days. Each experiment was conducted at least three times from three consecutive passages for statistical analysis.
The lentiviral plasmid SNCA (pLenti-Glll-CMV-C-term-HA; cat# LV315044), KAT2A (pLenti-Glll-CMV-C-term-HA; cat# LV194350) and empty vector (pLenti-lll-Blank; cat# LV587) were obtained from Applied Biological Materials Inc. (Crestwood Place, BC, Canada). After transforming into E. coli (DH5α), the large colonies were picked for further amplification and purification using the PureLink plasmid filter kit (Thermo Fisher Scientific, V.A., Lithuania), as per the manufacturer’s instructions. The empty vector served as a control. Amplified and purified plasmids were infected into 70–80% confluent SH-SY5Y cells using a Lipofectamine 3000 (cat# L3000015; Thermo Fisher Scientific) reagent. Post-transfection, cells were kept for 48 hours and then treated with 0.75–1 μg/ml puromycin to select stably transfected cells. The stably transfected αS, GCN5, and Vec were subjected to WB and reverse transcription-PCR (RT-PCR) for further analysis.
The total RNA was extracted and isolated using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA) as described by the supplier. We used the following primers specific for: Bim (forward 5′-CTACCAGATCCCCACTTTTC-3′, reverse 5′-GCCCTCCTCGTGTAAGTCTC-3′); E2F1 (forward 5′-GACTGTGACTTTGGGGACT-3′, reverse 5′-TGTTCACCTTCATTCCC-3′); Egr-1 (forward 5′-CGAGCGAACAACCCTACGAGC-3′, reverse 5′-GAGGCAGAGGAAGACGATGAAGC-3′); αS (forward 5′-AGTGGCTGAGAAGACCAAAG-3′, reverse 5′-GTCAGGATCCACAGGCATATC-3′); GCN5 (forward 5′-AGCGGTTGTTCCCAGCACCC-3′, reverse 5′-GACAGCACAGAAGACAATCT-3′); GAPDH (forward 5′-GCAAAGTGGAGATTGTTGCCATC-3′ and reverse 5′-CATATTTCTCGTGGTTCACACCC-3′). Photos were taken in a Davinch-K Gel Imaging System (MC2000/CG550, Youngin Lab Plus Co., the Republic of Korea).
The cells were washed twice with cold PBS and subjected to lyse using a lysis buffer (1× RIPA lysis buffer containing a protease and phosphatase inhibitor (1:1) cocktail). Whole mixtures were centrifuged at 14,000 rpm at 4°C for 15 minutes, and the supernatants were collected carefully without disturbing the pellets. The total protein obtained from cell lysates was quantified using a DC™ protein assay kit (Bio-Rad, USA) according to manufacturer instructions. Equal amounts of protein (20 μg) were separated electrophoretically using 8–15% sodium dodecyl sulfate-polyacrylamide electrophoresis (SDS-PAGE) gel and then transferred onto polyvinylidene-difluoride (PVDF) membranes (Millipore, Bedford, MA, USA). The membranes were incubated at room temperature for 1 hour with 3% bovine serum albumin in Tris-buffered saline (containing 0.1% Tween 20 buffer) to prevent nonspecific binding. The blots were then incubated overnight at 4°C with specific primary antibodies, including anti-Caspase3 (#H-277; Santa Cruz Biotechnology), anti-clvCaspase3 (#9661; cell signaling technology), anti-alpha-synuclein (#ab212184; Abcam), anti-alpha-synuclein LB509 (#ab27766; Abcam), anti-alpha-synuclein (211) (#sc-12767; Santa Cruz Biotechnology), anti-EGR-1 (#4154; cell signaling technology), anti-E2F1 (#3742; cell signaling technology), anti-Bim (#2819; cell signaling technology), anti-GCN5 (#sc-365321; Santa Cruz Biotechnology), anti-GCN5 (#MA45-14886; Invitrogen) anti-Bcl-2 (#N-19; Santa Cruz Biotechnology), anti-Lamin B1 (#12586; cell signaling technology), anti-Ac Histone H3 (#sc-56616; Santa Cruz Biotechnology), anti-Histone H3 (#sc-517576; Santa Cruz Biotechnology), anti-Ac Lys (#AB3879; Sigma-Aldrich) at 1:1000, anti-β-actin (#C4; Santa Cruz Biotechnology), and anti-β-tubulin (#2146; cell signaling technology) at 1:5000 concentration. Next, each blot was incubated at room temperature with either an anti-mouse or anti-rabbit (1:10,000) secondary antibody. The blots were visualized with an enhanced chemiluminescence detection system (LAS 500; GE Healthcare Bio-Sciences AB, Sweden) per the recommended protocol.
SH-SY5Y or transfected cells were grown to a confluency of 70–80% then washed with cold PBS once, followed by fixation with 4% cold PFA for 15 minutes at room temperature. After discarding PFA, cells were washed twice and permeabilized with 0.1% Triton X-100 for 10 minutes at room temperature. Cells were stained with DAPI (2 μg/ml) for 10 minutes at room temperature and images were immediately captured using a Nikon Eclipse Ts2R fluorescence microscopy system and were processed by NIS-Elements BR-2.01.00 software (which came with the instrument).
siRNA of GCN5 (#sc-37946; Santa Cruz Biotechnology) was used. The interference efficiency was detected by comparing siRNAs with the normal control (NC). Each siRNA was transfected into SH-SY5Y cells by RNAiMax (Invitrogen) according to the manufacturer’s protocol. Forty-eight hours after transfection, cell lysates were harvested and processed for WB to detect the expression of these proteins.
The SH-SY5Y cells were seeded at 5 × 105 density for 48 hours in a P100 plate and lysed in a non-denaturing RIPA buffer for 1 hour of incubation at 4°C on a wheel. After centrifugation, the supernatant was quantified with a DC™ protein assay kit (Bio-Rad, USA), and 0.6 mg of total protein extract was added to 50 μL of A/G agarose containing anti-GCN5, anti-E2F1, and anti-Egr-1 O/N at 4°C (rotation). The next day, the same binding buffer was used to perform four washes by centrifugation at 11,000 × g for 1 minute. The precipitated agarose was finally boiled and used for a western blot with the respective antibodies.
Statistical analyses were performed using GraphPad Prism (version 8.0.1; La Jolla, CA, USA) software. Data are represented as means ± SEM (standard error mean) of the three independent experiments. Student’s two-tailed t-test was used for comparison of the two different groups, and a one-way ANOVA, followed by Sidak’s multiple comparisons, was used for analysis more than two groups. A p-value of <0.05 was considered statistically significant.
The datasets generated and/or analyzed during the current study are publicly available upon acceptance of this manuscript. |
PMC9648805 | 36084952 | Kento Takaya,Toru Asou,Kazuo Kishi | Downregulation of senescence-associated secretory phenotype by knockdown of secreted frizzled-related protein 4 contributes to the prevention of skin aging | 07-09-2022 | skin,fibroblast,SASP,SFRP4 | There is growing evidence that the appearance and texture of the skin that is altered during the aging process are considerably enhanced by the accumulation of senescent dermal fibroblasts. These senescent cells magnify aging via an inflammatory, histolytic, and senescence-associated secretory phenotype (SASP). Secreted frizzled-related protein 4 (SFRP4) was previously determined to be expressed in dermal fibroblasts of aging skin, and its increased expression has been shown to promote cellular senescence. However, its role in the SASP remains unknown. We found that SFRP4 was significantly expressed in p16ink4a-positive human skin fibroblasts and that treatment with recombinant SFRP4 promoted SASP and senescence, whereas siRNA knockdown of SFRP4 suppressed SASP. Furthermore, we found that knockdown of SFRP4 in mouse skin ameliorates age-related reduction of subcutaneous adipose tissue, panniculus carnosus muscle layer, and thinning and dispersion of collagen fibers. These findings suggest a potential candidate for the development of new skin rejuvenation therapies that suppress SASP. | Downregulation of senescence-associated secretory phenotype by knockdown of secreted frizzled-related protein 4 contributes to the prevention of skin aging
There is growing evidence that the appearance and texture of the skin that is altered during the aging process are considerably enhanced by the accumulation of senescent dermal fibroblasts. These senescent cells magnify aging via an inflammatory, histolytic, and senescence-associated secretory phenotype (SASP). Secreted frizzled-related protein 4 (SFRP4) was previously determined to be expressed in dermal fibroblasts of aging skin, and its increased expression has been shown to promote cellular senescence. However, its role in the SASP remains unknown. We found that SFRP4 was significantly expressed in p16ink4a-positive human skin fibroblasts and that treatment with recombinant SFRP4 promoted SASP and senescence, whereas siRNA knockdown of SFRP4 suppressed SASP. Furthermore, we found that knockdown of SFRP4 in mouse skin ameliorates age-related reduction of subcutaneous adipose tissue, panniculus carnosus muscle layer, and thinning and dispersion of collagen fibers. These findings suggest a potential candidate for the development of new skin rejuvenation therapies that suppress SASP.
The skin is the outermost protective barrier of an organism and is composed of two main layers: the epidermis and dermis. The dermis lies beneath the epidermis and plays an important role in the structure and function of the skin [1]. It primarily consists of an extracellular matrix (ECM) generated by numerous fibroblasts [2]. Dermal fibroblasts are actively involved in the immune response of the skin, wound healing, and communication with the nervous and vascular systems [3–5]. Interestingly, it has been suggested that aging fibroblasts may lead to loss of function or dysfunction [6, 7] and may even contribute to visible clinical signs of aging skin, such as decreased swelling pressure and increased wrinkles [8]. Emerging hypotheses of skin aging postulate that senescence of fibroblasts primarily promotes skin fading and aging by irreversibly arresting proliferation and enhancing the release of the senescence-associated secretory phenotype (SASP) [9, 10]. The SASP induces chronic inflammation via chemokines and proinflammatory factors, inhibits proliferation by impairing the release of essential growth factors, and promotes ECM degradation by enhancing the activation of proteolytic enzymes, including matrix-degrading metalloproteinases [11]. Human fibroblast-derived SASP consists of increased concentrations of interleukins, matrix metalloproteinases (MMPs), and various chemokines, among many other factors [12, 13]. These secretions contribute to the progression of various age-related diseases [14], malignancies including squamous cell carcinoma [15, 16], prolonged wound healing, and skin aging [17]. In particular, human fibroblasts isolated from aged skin express secreted proteins related to inflammation and apoptosis, such as tumor necrosis factor alpha (TNF-α) [18], in addition to the usual SASP, called skin aging-associated secreted proteins (SAASP) [19]. Furthermore, it has been previously shown that SASP-induced fibroblasts can spread to neighboring non-aging fibroblasts [20]. This has attracted attention to the development of anti-aging therapies that suppress the SASP and selectively eliminate senescent cells. It has been reported that p16ink4a functions as a specific marker of senescent cells [9], and in vivo depletion of p16ink4a-positive senescent cells significantly improves organ function in aging organisms by suppressing the SASP, extending both healthy and overall life span [21, 22]. However, it is unclear whether SASP suppression of senescent cells in the dermis delays aging. Previous studies have shown through RNA sequencing that secreted frizzled-related protein 4 (SFRP4) mRNA and protein expression levels are significantly upregulated with age in human fibroblasts and that external supplementation of SFRP4 promotes fibroblast senescence [23]. SFRP4 is an extracellular Wnt antagonist that fine-tunes its signaling activity by binding directly to Wnt [24]. Although there are few descriptions of the association between SFRP4 and aging, it has been noted that SFRP4 expression is increased in scleroderma, correlates with skin and lung fibrosis, and may serve as a biomarker for epithelial mesenchymal transition [25]. We investigated the classical model of skin fibroblasts based on Hayflick’s mitotic limit [26], the observation of SFRP4 expression in replicating senescent cells, and the effect of regulating this on the suppression of SASP and aging skin. These results may contribute to the development of new therapies to ameliorate skin aging.
We used previously reported methods to confirm that aged human skin fibroblasts that had undergone several replications met the characteristics of senescent cells. These cells were simultaneously examined to assess whether SFRP4 expression was upregulated. Replication-aged fibroblasts showed a characteristic flattened and expanded morphology and increased SA-β-gal activity, as previously shown [9] (Figure 1A). Furthermore, analysis of BrdU uptake showed that proliferative activity was significantly lower in senescent cells than in proliferating cells (young cells) (P = 0.000005) (Figure 1B). Furthermore, real-time polymerase chain reaction (PCR) showed that the expression levels of characteristic fibroblast SASP factors, such as IL1A (P = 0.03), IL6 (P = 0.018), IL8 (P = 0.031), MMP3 (P = 0.025), and TNF-α (P = 0.022) were elevated in replication-aged fibroblasts compared to proliferating cells, and SFRP4 was also markedly elevated (P = 0.0018) (Figure 1C). ELISA showed that IL6 (P = 0.028) and IL8 (P = 0.021) protein expression was significantly enhanced in senescent cells (Figure 1D). Western blotting results showed that the protein expression level of SFRP4 was higher in senescent cells than in proliferating cells. Senescent cells also had increased expression of p21, an established senescence marker that functions as a cell cycle inhibitor by blocking G1/S-mediated progression when associated with cyclin-dependent kinase 2. In addition, senescent cells showed decreased expression of SIRT1, which protects cells from replicative senescence by promoting telomerase reverse transcriptase transcription (Figure 1E). Cell immunostaining showed that SFRP4, which is rarely expressed in proliferating cells, was significantly expressed in p16ink4a-positive senescent cells (Figure 1F).
To investigate the effect of SFRP4 on the SASP, we added recombinant SFRP4 to the culture medium. We verified cellular protein uptake from the medium by measuring SFRP4 levels in fibroblasts maintained in a medium containing recombinant SFRP4. Cellular protein uptake was determined by western blot protein bands or real-time PCR. Treatment of proliferating human skin fibroblasts for 10 days with 15 μg/ml of human rSFRP4 revealed an increase in SA-βGal-positive cells in some cells, but not all (P = 0.012) (Figure 2A). RT-PCR analysis also showed that the expression of the SASP factors IL1A (P = 0.041), IL6 (P = 0.025), IL8 (P = 0.021), MMP3 (P = 0.037), and TNF-α (P = 0.033) was significantly increased by rSFRP4 treatment (Figure 2B). ELISA showed that IL6 (P = 0.029) and IL8 (P = 0.021) protein expression was significantly enhanced by rSFRP treatment (Figure 2C). p21 protein expression levels were also increased by rSFRP4 treatment, and SFRP4 uptake was also observed (Figure 2D). We further tested whether SFRP4 contributes to the induction of senescence by silencing SFRP4 in aging skin fibroblasts and by evaluating senescence markers. Following strong silencing of SFRP4 at 72 h after transfection with SFRP4 siRNA, RT-qPCR analysis revealed that gene expression levels of the SASP factors IL1A (P = 0.0086), IL6 (P = 0.0028), IL8 (P = 0.006), MMP3 (P = 0.0084), TNF-α (P = 0.0078) and SFRP4 (P = 0.0000034) were significantly reduced in the SFRP4 siRNA group (Figure 3A). ELISA showed that IL6 (P = 0.011) and IL8 (P = 0.017) protein expression was significantly suppressed by SFRP knockdown treatment (Figure 3B). Western blot analysis revealed a marked reduction in the levels of p21 (Figure 3C). In addition, SFRP4 silencing promoted significant BrdU uptake (P = 0.0025) (Figure 3D), further indicating that SFRP4 contributes to the growth inhibition characteristic of senescence. In summary, our results indicate that SFRP4 promotes fibroblast senescence.
We investigated the possibility that the in vivo inhibition of SFRP4 might inhibit SASP-induced skin aging in mice (Figure 4A). siRNA treatment with invivofectamine in mice resulted in a strong knockdown of SFRP4 in the skin (control vs. SFRP4 siRNA; P = 0.00036, negative siRNA vs. SFRP4 siRNA; P= 0.0063). Simultaneously, the gene expression of IL1A (control vs SFRP4 siRNA; P = 0.00026, negative siRNA vs SFRP4 siRNA; P = 0.0064), IL6 (control vs SFRP4 siRNA; P = 0.0018, negative siRNA vs SFRP4 siRNA; P = 0.0029), MMP3 (control vs SFRP4 siRNA; P = 0.0019, negative siRNA vs SFRP4 siRNA; P = 0.002), and TNF-α (control vs SFRP4 siRNA; P = 0.0021, negative siRNA vs SFRP4 siRNA; P = 0.012) was significantly reduced in the skin (Figure 4B). At the protein level, SFRP4 siRNA-treated skin also showed decreased SFRP4 expression, whereas the expression of collagen III, which is involved in dermal aging, was increased (Figure 4C). Histologically, the skin from mice in which SFRP4 was knocked down by siRNA showed increased subcutaneous adipose tissue and panniculus carnosus muscle fibers (Figure 4D). Masson’s trichrome staining also revealed that the collagen fibers in the dermis of negative siRNA and control mice were thin and loosely assembled, whereas those in SFRP4 siRNA mice were thick and dense, similar to those in young mice (Figure 4E). This indicates that the knockdown of SFRP4 in the skin of aging organisms improves the skin aging phenotype.
This study shows that SFRP4, which is specifically expressed in aged p16ink4a-positive skin fibroblasts, contributes to SASP, and that treatment with SFRP4 causes worsening of this phenotype. To the best of our knowledge, the present study is the first to report that the suppression of SFRP4 expression in vivo ameliorates skin aging-related phenotypes, that is, adipose tissue atrophy and collagen fiber thinning, via SASP suppression. With age, more senescent cells are observed in mouse and human tissues owing to an increase in senescent cells themselves and/or failure of clearance of such cells by apoptosis or immunity [23–27]. Long-term senescent fibroblasts enforce tissue decline and skin aging owing to their non-proliferative state with increased proteolytic activity and suppressed ability to deposit ECM components. In particular, skin fibroblasts with high p16ink4a expression cause downregulated proliferation, likely due to the increased inhibitory effects on the CDK4/6 and retinoblastoma pathways, which leads to cell cycle arrest [28]. This SASP-promoted induction of inflammation by immortalized senescent cells clearly contributes to aging [13, 29]. In particular, an increase in p16ink4a-positive fibroblasts in the dermis has been reported to correlate with the typical morphological features of wrinkle formation and elastic fiber aging in human skin [30]. In this regard, clearance of p16ink4a cells leads to a significant improvement in lifespan and organ function in vivo [21] and has been reported to improve the skin phenotype associated with aging [31]. In other words, the removal of senescent cells in aging skin or suppression of SASP may be an effective means to help develop therapies to antagonize skin aging. In our study, we first observed SASP development and increased expression of SFRP4 in p16ink4a-positive fibroblasts. Furthermore, we found enhancement of SASP through SFRP4 ingestion and suppression of SASP through SFRP4 downregulation. However, there are few reports on the association between SFRP4 expression and aging. SFRP has garnered attention because it is highly expressed in fibroblasts and melanocytes near the basal layer of the epidermis and is involved in the pathogenesis of scleroderma, a type of immune fibrosis [25]. Its mechanism of binding to the Wnt ligand, which generally acts in negative regulation, not only has a complex effect on the formation of the Wnt gradient, but also extends the signaling range of the ligand [32, 33]. In particular, single-cell RNAseq revealed increased SFRP4 in different subgroups of fibroblasts isolated from systemic sclerosis interstitial lung disease (ILD) biosamples, which were hypothesized to be progenitor cells of myofibroblasts [34]. Even in normal skin, abnormal fibroblast activation and inadequate extracellular matrix deposition have been implicated in the aging skin phenotype [35], suggesting that SFRP4 expression in dermal fibroblasts may influence the skin phenotype associated with aging. However, the mechanisms by which SFRP4 is upregulated in dermal fibroblasts with aging are not fully understood, and should be investigated through comprehensive genetic and cascade analyses. For example, TGF-β, another mediator involved in fibrosis, has been shown to activate the canonical Wnt pathway and to potently stimulate fibroblast activation and tissue fibrosis [36], which may be in cross-talk with SFRP4. Another mediator that affects dermal fibroblast damage in relation to aging is TNF-α. In this study, TNF-α, as one of the SASP characteristics of dermal fibroblasts, has been demonstrated to have catabolic activity in human skin through degradation of collagen I and upregulation of MMP [37]. Previous studies have shown that TNF-α-induced senescence is mediated by p38 MAPK and ROS, and the accumulation of senescent cells in aging skin and non-healing chronic wounds may play an important role in tissue remodeling, repair, and homeostasis regulation [38, 39]. The suppression of TNF-α expression and improvement of the aging skin phenotype by suppressing SFRP4 expression observed in this study may provide some clues to pursue the role of aging dermal fibroblasts in skin. Since the skin is essentially atrophic in aging mice and this phenotype of skin atrophy has been repeatedly reported as comparable in humans [40, 41], we focused on the effects of SFRP4 knockdown on aging mouse skin. In particular, both collagen deposition and primary fiber thickness in aging mouse skin have been shown to be greatly reduced in the dermis [42], and suppression of SFRP4 expression achieved an improvement in this measure. Decreases in collagen III with aging have been also reported previously [42] suggesting that knockdown of SFRP4 has a protective effect on skin aging via collagen III increase and SASP suppression. A limitation of this study is that other effects of SFRP4 knockdown on skin have not been fully examined; some parts of the SASP perform essential biological functions in wound healing and in response to short-term tissue damage [43, 44]. To avoid interference with the beneficial functions of the aging phenomenon, interventions targeting senescent cells in the context of aging and aging-related diseases may need to be temporally and locally particular to specific conditions. This can be accomplished through further studies of the specific mechanisms of suppression of senescent cell function using SFRP4 knockdown mice. These studies will ultimately lead to the development of new therapeutic strategies for age-related medical conditions. In addition, this study used only male mice to avoid any effects attributable to fluctuating hormones, but future experiments on female mice are needed to consider applications in humans. Overall, this research could potentially aid in the development of anti-aging treatments for skin by preventing age-related changes through the regulation of SFRP4 expression in aging cells.
The study protocol was reviewed and approved by the Institutional Animal Care and Use Committee of our institution, the Keio University School of Medicine (approval number: 13072-(2)). All experiments were performed in accordance with the institutional guidelines on animal experimentation.
Normal human dermal fibroblasts (C-12300) were obtained from PromoCell GmbH (Heidelberg, Germany). The cells were grown in low-glucose Dulbecco’s Modified Eagle Medium (DMEM; Wako Pure Chemical Industries, Osaka, Japan) supplemented with 10% fetal bovine serum (Thermo Fisher Scientific, Waltham, MA, USA) and 1% penicillin/streptomycin (Thermo Fisher Scientific). Fibroblasts with proliferative senescence or more than 2 weeks were defined as the absence of cell proliferation. Intracellular SA-β-gal activity was assessed using the Senescence β-Galactosidase Staining Kit from Cell Signaling Technology (Danvers, MA, USA).
The cells were incubated with BrdU for 24 h at 37° C, then collected, and incubated with BrdU-FITC antibody (BrdUFlowEx FITC Kit; EXBIO Praha, a. s., Vestec, Czech Republic) for 30 min. A flow cytometer (BD Biosciences, Franklin Lakes, NJ, USA) was used for the analysis, and FlowJo (Ver. 10.2) was used to count the number of positive cells.
The cells were placed on glass slides, fixed in acetone for 5 min at room temperature (15–25° C), and dried completely before staining. Cells were incubated overnight at 4° C with an anti-SFRP4 antibody (Thermo Fisher Scientific) and anti-p16ink4a antibody (Abcam, Cambridge, UK) diluted at 1:100 in phosphate-buffered saline (PBS). After washing thrice with PBS, the slides were incubated with Alexa Fluor 488 conjugated goat anti-rabbit antibody and AlexaFluor555 conjugated donkey anti-goat antibody (Thermo Fisher Scientific) diluted at 1:2000 in PBS for 1 h at room temperature. After incubation, the slides were washed thrice with PBS and counterstained for nuclear visualization using ProLong Gold Anti-fade Mountant (Thermo Fisher Scientific) containing 4ʹ,6-diamidino-2-phenylindole.
Aged human skin fibroblasts (PD55-60) were maintained in acclimated DMEM containing 15 μg/ml recombinant SFRP4 (rSFRP4; ab245787, Abcam). The rSFRP4-containing medium was supplemented every 2 days. Samples were analyzed for the induction of senescence after 10 days using the SA-β-galactosidase assay (SA-βGal) and typical senescence markers in a real-time PCR.
Lipofectamine 2000 (11668-019; Life Technologies, Invitrogen, Germany) was used to inject SFRP4 siRNA (121422, 121423, and 121424; Silencer™ siRNA, Thermo Fisher Scientific). RNA was collected from the cells after 72 h, and specific gene knockdown was assessed using real-time PCR.
Total protein was extracted from the cells and tissues. Tissues were pre-shredded and treated with collagenase. Samples were extracted in lysate buffer: 50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 0.5% Nonidet P40, 0.5% sodium deoxycholate, and phenylmethylsulfonyl fluoride (FUJIFILM Wako Pure Chemical Co., Osaka, Japan). Western blotting was performed as previously indicated [45]. Briefly, each sample (40 μg) was electrophoresed on 10% polyacrylamide gels Mini-PROTEAN® TGX™ Precast Gels (Bio-Rad Laboratories, Inc., CA, USA) and transferred to a Trans-Blot Turbo Transfer System (Bio-Rad). After blocking with 3% nonfat milk for 2 h at room temperature, primary antibodies against SFRP4 (ab154167, Abcam. 1:200), p21 (ab220206, Abcam, 1:100), SIRT1 (ab32441, Abcam, 1:100), collagen III (PA5-27828, Thermo Fisher Scientific, 1:200), and GAPDH (1:2000 dilution; Santa Cruz Biotechnology, Santa Cruz, CA, USA) diluted in blocking solution were incubated overnight at 4° C. The next day, the sections were incubated with the following secondary antibodies: donkey anti-goat IgG H&L (HRP) (ab6885; Abcam), goat anti-rabbit IgG H&L (HRP) (ab205718; Abcam), and goat anti-mouse IgG H&L (HRP) (ab205719; Abcam) at 1:1000 dilution for 2 h at 37° C. After washing, the immunoreactive protein bands were visualized using an electrochemiluminescence detection kit (Pierce Biotechnology, Rockford, IL, USA). Images of the bands were obtained using a chemiluminescence imager (ImageQuant LAS4000 mini; GE Healthcare, Chicago, IL, USA). Image analysis was performed using ImageJ software (version 1.53p). Each experiment was repeated thrice.
Replicative senescent cell models were prepared as described above, and the medium was changed to serum-free medium containing antibiotics. After 24 h, the conditioned medium was collected and IL-6 and IL-8 expression was quantified using ELISA (Human IL-6 Quantikine ELISA Kit (D6050), Human IL-8 Quantikine ELISA Kit (D8000C), (R&D Systems, Inc., Minneapolis, MS, USA)), according to the manufacturer’s protocol.
Male C57BL/6 mice were purchased from the Sankyo Labo Service Corporation, Inc. (Tokyo, Japan). Young (15 weeks old) and old (90 weeks old) mice were both used in this study. The complexes were prepared by combining invivofectamine 2.0 (Life Technologies, Carlsbad, CA, USA) and SFRP4 siRNA (152089, 152090, and 152091; Silencer™ siRNA, Thermo Fisher Scientific), according to the manufacturer’s instructions. The SFRP4 siRNA complex, negative siRNA (Silencer™ Negative Control No. 1 siRNA, Thermo Fisher Scientific) complex, or control (PBS) were injected at 4 mg/kg into old mice by intravenous injection weekly (1.5 mg/kg in week 1 and 1 mg/kg in weeks 2 and 3). Experiments were performed with n = 5 for each group. One week later, the skin was collected in whole layers, and tissue specimens were fixed by immersion in 4% paraformaldehyde, embedded in paraffin, sectioned, and stained with hematoxylin-eosin and Masson’s trichrome staining. Tissues for RNA recovery were later immersed in RNA (Qiagen, Hilden, Germany) and stored at −20° C until use.
Total RNA was extracted from cells or skin tissue using a monophasic solution of phenol and guanidine isothiocyanate (ISOGEN; Nippon Gene, Tokyo, Japan), according to the manufacturer’s instructions. Total RNA was mixed with random primers, reverse transcriptase, and dNTP mixture (Takara Bio Inc., Shiga, Japan). The mixture was incubated in a T100TM thermal cycler (Bio-Rad Laboratories, Inc., Hercules, CA, USA) at 25° C for 5 min, 55° C for 10 min, and 80° C for 10 min for heat inactivation of reverse transcriptase to generate cDNA.
RT-qPCR was performed using the Applied Biosystems 7500 Fast Real-Time PCR System (Thermo Fisher Scientific). A total of 40 cycles were performed and the fluorescence of each sample was measured at the end of each cycle. The PCR reaction was performed in two major steps: holding the reagents at 95° C for 3 s (denaturation) and at 60° C for 30 s (annealing and extension). In the subsequent melting curve analysis stage, the temperature was increased from 60° C to 95° C, and fluorescence was measured continuously. Gene expression was analyzed for SFRP4 (Assay ID: Hs00180066_m1, Mm00840104_m1), Il-6 (Hs00985639_m1, Mm00446190_m1), Il-1a (Hs00174092_m1, Mm00515166_m1), Il-8 (Hs00174103_m1, Mm00441263_m1), MMP3 (Hs00968305_m1, Mm00440295_m1) and TNF-α (Hs00174128_ml, Mm00443258_m1) (Thermo Fisher Scientific). PCR master mix (Cat. 4352042; Applied Biosystems, Foster City, CA, USA) was used, according to the manufacturer’s instructions; GAPDH (Hs02786624_g1) and ACTB (Mm02619580_g1) were used as control genes for normalization, according to the manufacturer’s instructions. Gene expression levels in the proliferating cell population were used as the baseline, and fold-change values were determined using the 2−ΔΔCt method [46].
Statistical analyses were performed using GraphPad Prism (version 5.0; San Diego, CA, USA) or SPSS 22.0 (Chicago, IL, USA). Mann–Whitney U test was used to analyze differences between two groups. One-way ANOVA and Tukey’s post hoc test were used to compare differences between three or more groups. Statistical significance was set at P < 0.05.
The data that support the findings of this study are available from the corresponding author, K.T., upon reasonable request. |
PMC9648807 | 36279395 | Ning Bao,Jiping Liu,Zhe Peng,Rong Zhang,Rufei Ni,Runzuan Li,Jian Wu,Zhenhua Liu,Botao Pan | Identification of circRNA-miRNA-mRNA networks to explore the molecular mechanism and immune regulation of postoperative neurocognitive disorder | 21-10-2022 | postoperative neurocognitive disorder (PND),bioinformatics,transcriptome,competitive endogenous RNA network,immune cell infiltration | Postoperative neurocognitive disorder (PND) is a common complication in older patients. However, its pathogenesis has still remained elusive. Recent studies have shown that circular RNA (circRNA) plays an important role in the development of neurodegenerative diseases, such as PND after surgery. CircRNA, as a competitive endogenous RNA (ceRNA), mainly acts as a molecular sponge for miRNA to “adsorb” microRNA (miRNA) and to reduce the inhibitory effects of miRNAs on target mRNA. The sequencing data of circRNA were obtained from the Gene Expression Omnibus (GEO) database. By bioinformatic methods, circAtlas, miRDB, miRTarBase and miRwalk databases were applied to construct circRNA-miRNA-mRNA networks and screen differentially expressed mRNAs. To improve the accuracy of the data, we randomly divided aging mice into control (non-PND group) and PND groups, and used high-throughput sequencing to analyze their brain hippocampal tissue for analysis. Three key genes were cross-detected in the data of both groups, which were Unc13c, Tbx20 and St8sia2 (as hub genes), providing new targets for PND treatment. According to the results of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, immune cell infiltration analysis, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, quantitative real-time polymerase chain reaction (qRT-PCR), the genes that were not related to the central nervous system were removed, and finally, mmu_circ_0000331/miR-1224-3p/Unc13c and mmu_circ_0000406/miR-24-3p/St8sia2 ceRNA networks were identified. In addition, the CMap method was used to select the top 4 active compounds with the largest negative correlation absolute values, including cimaterol, Rucaparib, FG-7142, and Hydrocortisone. | Identification of circRNA-miRNA-mRNA networks to explore the molecular mechanism and immune regulation of postoperative neurocognitive disorder
Postoperative neurocognitive disorder (PND) is a common complication in older patients. However, its pathogenesis has still remained elusive. Recent studies have shown that circular RNA (circRNA) plays an important role in the development of neurodegenerative diseases, such as PND after surgery. CircRNA, as a competitive endogenous RNA (ceRNA), mainly acts as a molecular sponge for miRNA to “adsorb” microRNA (miRNA) and to reduce the inhibitory effects of miRNAs on target mRNA. The sequencing data of circRNA were obtained from the Gene Expression Omnibus (GEO) database. By bioinformatic methods, circAtlas, miRDB, miRTarBase and miRwalk databases were applied to construct circRNA-miRNA-mRNA networks and screen differentially expressed mRNAs. To improve the accuracy of the data, we randomly divided aging mice into control (non-PND group) and PND groups, and used high-throughput sequencing to analyze their brain hippocampal tissue for analysis. Three key genes were cross-detected in the data of both groups, which were Unc13c, Tbx20 and St8sia2 (as hub genes), providing new targets for PND treatment. According to the results of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, immune cell infiltration analysis, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, quantitative real-time polymerase chain reaction (qRT-PCR), the genes that were not related to the central nervous system were removed, and finally, mmu_circ_0000331/miR-1224-3p/Unc13c and mmu_circ_0000406/miR-24-3p/St8sia2 ceRNA networks were identified. In addition, the CMap method was used to select the top 4 active compounds with the largest negative correlation absolute values, including cimaterol, Rucaparib, FG-7142, and Hydrocortisone.
Postoperative neurocognitive disorder (PND) is a series of clinical manifestations characterized by inattention, decreased language comprehension, cognitive decline, and difficulty in returning to preoperative life after surgery [1]. PND includes acute delirium and more lasting postoperative neurocognitive impairment [2], mainly occurring in old patients [3], and it has become a major public health concern. International studies on postoperative cognitive function estimated that the one-year mortality rate of PND patients within three months after surgery was almost twice that of patients without PND [4]. The risk of PND in old patients was reported to be 25–40% [5]. The global prevalence of dementia was 46.8 million in 2015, and it is expected to increase to 131.5 million by 2050. The global cost of dementia was estimated to be $818 billion in 2015 [6]. PND leads to delayed recovery, prolonged hospitalization, increased medical costs, enhanced complications, and even loss of self-care ability, resulting in a series of medical, social, and economic problems [7]. Several studies have explored the possible mechanism of PND, and neuroinflammation [8], neuronal apoptosis [9], autophagy disorders [10], and synaptic plasticity [11] have been reported. At present, the pathogenesis of PND is not clear, and there is no effective treatment and specific marker for PND. With the rapid development of molecular biology and transcriptology, the biological functions and regulatory mechanisms of non-coding RNA can explain the occurrence and development of several complex diseases. Circular RNA (circRNA) is a newly discovered class of non-coding RNA with a length of about 100 nucleotides. The circRNA molecule has no PolyA tail with 5′ and 3′ ends, and it has a closed ring structure with a high stability. The roles of circRNAs in various diseases have been widely studied due to their unique molecular mechanism and molecular function, and have become a research hotspot. Their main function is to act as a molecular sponge for microRNAs (miRNAs) to “adsorb” miRNAs and to reduce the inhibition of miRNAs on target mRNAs. The mechanism of competitive endogenous RNAs (ceRNAs) in the occurrence and development of diverse diseases has been most thoroughly studied. Studies have reported that ceRNA network could have a great potential in the treatment of the central nervous system (CNS) diseases [12]. Recent studies have shown that circRNA_22058 and circRNA_44122/EGFR ceRNA network and circRNA_22673/Prkacb ceRNA could be the mechanism of PND [13]. However, the role of circRNA in elderly patients with PND has still remained elusive. Bioinformatics research has revealed the molecular targets of PND mechanism in a systematic, accurate, and effective manner, and clarified the theoretical basis of PND occurrence. In the present study, prefrontal cortex data of PND in aged mice obtained from GEO database were screened, and a circRNA-miRNA-mRNA network was constructed by bioinformatics method to identify differential mRNAs. To further improve the accuracy of data, the hippocampus of splenectomized aged mice were sent to Novogene Co., Ltd. (Beijing, China) for high-throughput sequencing of mRNA. The two sets of data were intersected to detect hub genes. Surgery and trauma can trigger inflammatory responses characterized by both pro-inflammatory and anti-inflammatory cytokines released by the immune system [14]. A large number of studies have shown that immune cells trigger neuroinflammatory responses, leading to postoperative cognitive dysfunction [15]. Immune regulation has become a hotspot in the study of PND mechanism. The present research aimed to identify immune-related genes. The flowchart of this study is shown in Figure 1.
Healthy male C57BL/6 mice (age, 18-month-old; body weight, 25–30 g) were purchased from Beijing Weitong Lihua Co., Ltd. (Beijing, China). All animals were acclimated for one week before the experiment. All animal experiments were performed in accordance with the Guide for Care and Use of Laboratory Animals, and approved by the Animal Care and Use Committee of the Southern Medical University (Guangzhou, China). The aging mice were divided into two groups: control group (n = 11) and surgery group (n = 11). Mice in the surgery group received splenectomy under general anesthesia. Among them, 16 mice underwent Morris water maze (MWM) test to determine the cognitive dysfunction in aging mice after surgery, and the hippocampus of the remaining 6 mice were taken out for transcriptome detection.
Splenectomy was performed under inhalation anesthesia (maintained by a mixture of 2.0–2.5% isoflurane and oxygen). The anal temperature was maintained between 38.8–39.8°C. After undergoing anesthesia, the left subcostal incision was made. The spleen was separated and exposed, the spleen arteries and veins in the splenic pedicle region were firmly ligated, and the spleen was removed. After confirming no active bleeding, the abdomen was closed layer-by-layer with silk thread. Mice in the control group were not anesthetized or underwent surgery. Splenectomy leads to reversible postoperative learning and memory dysfunction and subsequently induces Postoperative cognitive dysfunction (POCD) [16], and is often used as a model for establishing POCD [17].
MWM test was employed to assess abilities of mice in learning, memory, and spatial cognition. A pool with a diameter of 120 cm and a depth of 50 cm was divided into four quadrants, and a platform with a diameter of 12 cm was set 1 cm underwater in the target quadrant. Experimental mice first received positioning navigation training for 5 days. Mice were placed on the platform for 30 s, and were put into the pool from four different quadrants (N, W, S, and E). Observe and record the distance, time and speed of mice reaching the platform for the first time. If mice could not find the platform within 60 s, they were guided to the platform for 15 s. The interval between the two training sessions was at least 30 s. The time of reaching the platform for the first time, the number of times that the distance and speed could cross the platform quadrant, and the number of times that they crossed the platform quadrant were recorded. The experiment was lasted for at least 30 s. The time to reach the platform for the first time, the distance and speed to cross the platform, and the number of times to cross the platform quadrant were recorded. On day 6 and postoperative days 3, 7, and 14, the platform was removed and the MWM assay was performed to record the distance, speed, and time spent across the platform for the first time and the percentage of time spent across the target quadrant. Finally, the time point of the most severe PND in the aging mice was selected as the time point for taking the materials. The test performers and data analyzers were blinded to the mice groups.
Under isoflurane deep anesthesia, 6 mice were killed by decapital method, including 3 mice in control group and 3 mice in surgery group. The skull was separated by non-sharp method, the hippocampus were placed on ice bags, and then, the hippocampus were frozen in liquid nitrogen and stored in a −80°C refrigerator. The hippocampus were randomly selected for subsequent sequencing analysis. Total RNA was extracted from mouse hippocampus using TRIzol® reagent (Magen, Guangzhou, China). Nanodrop ND-2000 (Thermo Fisher Scientific, Waltham, MA, USA) was used to detect the A260/A280 absorbance ratio of RNA samples. An Agilent 4150 Bioanalyzer (Agilent Technologies, CA, USA) was used to measure RIN values of RNA. A PE library was prepared according to ABclonal mrna-SEQ Lib Prep Kit (ABclonal, China) specification. Sequencing was performed using the Illumina Novaseq 6000/MGISEQ-T7 sequencing platform (Illumina Inc., Chicago, IL, USA).
In the present study, GSE174410 dataset was downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo), as well as annotation platforms for GPL21273, including 3 normal tissue samples and 3 post-operative neurocognitive impairment tissue samples, accounting for a total of 6 samples. In the present experiment, high-throughput sequencing was performed on the mRNA of hippocampus of aging mice, including 6 samples from control group (n = 3) and PND group (n = 3). The “Limma” package was used to standardize the data of the two groups, and the circRNA/mRNA was significantly differentially expressed in the control group and the PND group. A gene with |LogFC|>1 and P < 0.05 was considered to be significantly differentially expressed, and it was displayed through a volcano plot.
In order to obtain the biological functions and signaling pathways involved in the occurrence and development of PND, the Metascape database (http://www.metascape.org) was used to enrich and analyze 530 mRNAs with differences in the experimental data for annotation and visualization. Specific genes were analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Min overlap ≥3 and P ≤ 0.05 were considered statistically significant.
We used circAtlas database to predict circRNA-miRNA interaction pairs. In addition, the interaction between miRNA and mRNA was predicted by combining data collected from miRDB, miRTarBase, and miRwalk databases. Targeted mRNAs identified by more than two databases were selected for further analysis. Then, the circRNA-miRNA-mRNA network was established by combining circRNA-miRNA interaction and mRNA-miRNA interaction, and Cytoscape software was used for visualization of biological networks.
The CIBERSORT algorithm was used to analyze RNA-seq data of mice in the control and PND groups to infer the relative proportions of 25 immune infiltrating cells. The “Pheatmap” package was used to draw the heatmap of immune cell infiltration to explore the distribution of immune cells. The “Corrplot” package was utilized to analyze the interaction between immune cells and to further analyze the influence of the interaction between immune cells. The “Vioplot” package was employed to plot the relative content of immune cells. P < 0.05 was considered statistically significant. Then, the Spearman correlation analysis was conducted between HUB gene expression level and immune cell content, and “GGploT2” package was used for visualization. Chemokines refer to small cytokines or signal proteins that have the function of making immune cells targeted to chemotaxis, which can control the migration and residence of all immune cells. Autophagy, as a defense mechanism of the collective, in the body’s immunity, inflammation, neural degeneration disease, aging, etc., has shown a very important role in the pathogenesis, and it is closely associated with the immune cells. We, in the present study, further discussed the hub genes and chemotactic factors, as well as the relationship between the autophagy using the Pearson correlation analysis.
Unc13c, Tbx20, and St8sia2 mRNA were analyzed by the GSEA. GSEA analyzes the expressions of a group of functionally-related genes based on gene expression profile data. The principle of GSEA is to search for sets of genes that are significantly over-represented in a given list of genes. According to the number of genes contained in the gene set and the expressions of genes, the normalized enrichment score (NES), false discovery rate (FDR), and adjusted P value of the signaling pathway were calculated. Genes with P < 0.05 were considered significantly enriched after 1000 permutations.
The PPI networks of Unc13c, Tbx20, and St8sia2 were constructed and visualized separately based on STRING database. The correlation analysis was performed between hub genes and their co-expressed gene expression levels.
RNA was extracted and validated from the cerebral cortex of 5 control and 5 PND aging mice using qRT-PCR. Total RNA was extracted using TRIzol reagent (Cat. No. 1596–026; Thermo Fisher Scientific). The reverse transcription reaction system was prepared according to the instructions of Bestar qPCR RT Kit. The total system was 20 μL, and the first strand of cDNA was synthesized. Follow the instructions presented for DBI Bestar SybrGreen qPCR masterMix reaction kit, annealing was carried out at 94°C for 2 min, at 94°C for 15 s, and at 60°C for 15 s, at 72°C for 15 s, with 40 cycles. GAPDH was used as an internal reference gene. A QuantStudio3 fluorimeter (Thermo Fisher Scientific) was used for fluorescence quantitative analysis. Relative gene expression was calculated by the 2−ΔΔCT method. The primers are shown in Table 1.
CMap is a database of expression profiles based on the expression of intervening genes developed by the Broad Institute. Lamp proposed that it could be used to discover the association between drugs, genes and diseases, and constructed the CMap database when the gene changes were obtained after cell disturbance [18]. CMap was used to compare the data list of differentially expressed genes measured by our research group with the database reference data set. Finally, the database will obtain a correlation score (−100–100) according to the enrichment of differentially expressed genes in the reference gene expression spectrum. The positive value indicated that the up-regulated and down-regulated differential expressions were similar, while the negative value indicated that the up-regulated and down-regulated differential expressions were opposite, and the reference gene expression was sequenced according to the value.
All data were analyzed using Graphpad Prism 8 software, and the measures were expressed according to the mean ± SD. Data were compared between groups using analysis of variance (Two-way ANOVA) P-value < 0.05 was considered statistically significant with great importance.
Part of datasets generated during and/or analyzed during the current study are available in the Gene Expression Omnibus (GEO) datasets (http://www.ncbi.nlm.nih.gov/geo/) and the other part of the data came from the high-sequencing results of our experiment.
During the 6 days of preoperative training, all experimental mice showed a gradual downward luminal trend in the time and distance to find the platform, indicating that the experimental mice were able to improve their spatial learning memory ability by constantly repeating finding the platform. The aging mice showed a significantly impaired memory on postoperative day 3, as the time and distance to find the platform (Figure 2B, 2C) both significantly increased; the number of times to pass the platform within 60 s and the percentage of target quadrants (Figure 2D, 2E) both significantly decreased, while the average speed did not significantly differ (Figure 2F), and basically recovered on postoperative day 14. Comparing postoperative day 3 with preoperative training day 6, postoperative day 7, and postoperative day 14, postoperative day 3 was the day with the worst memory ability and the most obvious cognitive impairment, as evidenced by a significant increase in the time and distance to find the platform for the first time and a significant decrease in the number of times to pass the platform within 60 s and the percentage of target quadrants. Therefore, we selected the hippocampus of aging mice for transcriptomic assay on the 3rd postoperative day when memory impairment was most severe. Timeline diagram of the Morris Behavior Test is shown in Figure 2A.
A total of 18 differentially expressed circRNAs, including 17 up-regulated circRNAs and 1 down-regulated circRNA, were identified from the circRNA transcriptome data of GSE174410 downloaded from the GEO database (Figure 3A). Compared with the control group, the difference in circRNA expression in the surgical group was statistically significant. A total of 530 differentially expressed mRNAs, including 266 up-regulated mRNAs and 264 down-regulated mRNAs, were screened, which are represented as volcano plots (Figure 3B).
We further conducted the GO and KEGG pathway enrichment analyses of 530 differentially expressed mRNAs through the Metascape database, and the results showed that, the main gene enrichment pathways were pheromone receptor activity, DNA-binding transcription factor activity, RNA polymerase II-specific and B cell proliferation, female sex differentiation, spinal cord development, anion transport, positive regulation of transmembrane receptor serine/threonine kinase signaling pathway, detection of chemical stimulus, and endocrine system development (Figure 3C).
In order to find out the downstream genes of circRNA and to construct the ceRNA network, we first used the online circAtlas database (circatlas.biols.ac.cn) to predict miRNA targets for the 5 differentially up-regulated circRNAs with the largest change. The results showed that there were 96 circRNA-related targeted miRNAs, including 105 circRNA-miRNA pairs (Figure 4A). A total of 4,386 mRNAs and 6,725 pairs of miRNA-mRNAs were predicted (Figure 4B). Besides, miRwalk, miRDB, and miRTarBase databases were further used to predict miRNA-related targeted mRNAs, the list of miRNA-mRNA was shown in Appendix 1. Finally, the intersection of 4386 predicted mRNAs and 530 differentially measured mRNAs was obtained, and a total of 13 mRNAs were detected, a Venn diagram was shown in Figure 4C. Then, 19 ceRNA networks were successfully constructed for circRNA-miRNA-mRNA pairs and visualized by the Cytoscape software (Figure 4D). From the Figure 4E we can see that Unc13c, Tbx20, and St8sia2 are located in the largest yellow, pink, and purple regions respectively, so we selected these three genes with the most miRNA relevance as the core genes (mRNA relationship pairs ≥3). We conducted ROC curve analyses to indicate whether three hub genes could better predict PND. The results showed that AUC values of the three hub genes were 1.000 (Figure 4F), 0.889 (Figure 4G), and 1.000 (Figure 4H), respectively. These three hub genes had high diagnostic potential in differentiating aging mice with PND from healthy control mice.
Immune microenvironment is mainly composed of immune cells, extracellular matrix, various growth factors, inflammatory factors, and special physical and chemical characteristics, significantly influencing the diagnosis and clinical treatment of diverse diseases. After analyzing the relationship between differentially expressed genes and immune invasion in PND data, the potential molecular mechanism of differentially expressed genes influencing the progression of immune invasion was further discussed. The results of immune cell infiltration assay showed the contents of immune cells in each sample, of which M0 macrophage and M2 macrophage accounted for the highest proportions (Figure 5A), and the correlation between immune cells is shown in Figure 5B. The number of mast cells was significantly lower in patients with disease than that in normal patients (Figure 5C). We additionally explored the relationship between core genes and immune cells, and the results showed that UNC13C and St8sia2 were significantly positively correlated with Treg cells and mast cells, respectively, and significantly negatively correlated with natural-killer (NK) cells and naive CD4 T cells (Figure 5D, 5F). Tbx20 expression in surgery group significantly increased, it was significantly positively correlated with the number of naive CD4 T cells, and it was significantly negatively correlated with the number of mast cells (Figure 5E). These results confirmed that key genes were closely related to the level of immune cell infiltration and play key roles in the immune microenvironment.
Correlation analysis of hub genes and chemokines showed that Tbx20 expression was significantly positively correlated with Ccl1 expression (Pearson R = 0.98), while Tbx20 expression was significantly negatively correlated with CCL9 expression (Pearson R = −0.83) (Figure 6A). Correlation analysis of hub genes and autophagy-related genes showed that Tbx20 and Epg5 were significantly negatively correlated (Pearson R = −0.91), while Unc13c and Atg4a were significantly positively correlated (Pearson R = 0.84) (Figure 6B).
GSEA of the three hub genes showed that high expression of Unc13c was mainly enriched in TUBULIN_BINDING, n-acetyltransferase_activity, VESICLE, and other pathways. The high expression of Tbx20 was mainly concentrated in FK506_BINDING, Protein_Peptidyl-prolyl_isomerization, MIDDLE_EAR_MORPHOGENESIS, and other signaling pathways. The high expression of St8sia2 was mainly concentrated in gamma-tubulin_binding, VESICLR, and response_to_intracestar signaling pathways. Besides, Unc13c, Tbx20, and St8sia2 could regulate the development of PND and affect the course of disease and prognosis of elderly mice through these signaling pathways (Figure 7).
The co-expressed genes (St8sia2, Tbx20, and Unc13c) were retrieved from the String database, and the confidence score was set to 0.4 to establish the corresponding protein interaction network (Figure 8A–8C). In addition, the correlation analysis of the three hub genes and their corresponding co-expressed genes was performed separately, and the results showed that Unc13c and Rims2 were significantly positively correlated, and Tbx20 and Myh6 were significantly negatively correlated. The correlation coefficient between Unc13c and Rims2 was 0.965 (P = 0.002) (Figure 8D). The correlation coefficient between Tbx20 and Myh6 was 0.823 (P = 0.044) (Figure 8E).
The relative expression levels of 4 circRNAs and 3 mRNAs in PND group and control group were detected by qRT-PCR. Compared with the control group, the mRNA expression levels of Unc13c (Figure 9E) and St8sia2 (Figure 9G) in PND group were reduced (P < 0.05). The expression levels of CIRC0000331 (Figure 9A), CIRC0000400 (Figure 9B), CIRC0000406 (Figure 9C), CIRC0000798 (Figure 9D), and Tbx20 (Figure 9F) were up-regulated (#P < 0.05; ##P < 0.01). These 7 genes could serve as potential biomarkers for diagnosis and prognosis.
The CMap method was used to select the top 4 active compounds with the largest negative correlation absolute values, including cimaterol, Rucaparib, FG-7142, and Hydrocortisone (Figure 10A–10D), as shown in Appendix 2. These four active compounds could be therapeutic targets of PND.
The course of delirium is acute in PND, mainly appearing 1–3 days after surgery and anesthesia, which is coincident with establishing aging mouse models with PND to screen the maximum severity of PND. However, devastating consequences were obtained, such as the increased mortality in the first year after surgery [19], the decreased quality of life, and the increased long-term risk of Alzheimer’s disease (AD) [20]. In adult mice, circRNA is more abundant in the brain than that in other organs (e.g., heart, liver, and lung) [21]. Cerebral circRNA enrichment is associated with the neurotransmitter function, neuronal maturation, and synaptic activity [22]. CircRNAs have been reported to target aging-related mRNAs in the brain and to regulate the aging process by changing the expressions and availability of specific mRNAs [23]. Based on the important role of circRNAs in regulating the CNS diseases, the current experiment first extracted circRNAs from the GSE174410 dataset, which was combined with our experimental data. Four circRNAs were detected, including mmu_cirC_0000400, mmu_cirC_0000331, mmu_cirC_0000406, and mmu_cirC_0000798, as well as 3 hub genes (Unc13c, Tbx20, and St8sia2). We first reviewed the literature to understand the three hub genes. Unc13c was widely expressed in 11 tissues, including brain, cerebellum, heart, and liver [24]. Unc13c is a protective gene against AD, and it may be involved in synaptic plasticity and synaptic transmission [25]. This was consistent with the results of the present study, in which Unc13c was down-regulated in the PND group. Unc13c was significantly down-regulated in spinal cord tissues of patients with amyotrophic lateral sclerosis [26]. It was reported as one of the hub genes in a genome-wide association study of post-traumatic stress disorder in Iraq-Afghanistan veterans [27]. Unc13c has also been found to be associated with neurodegeneration in a genetic dementia in Finland [28]. These studies suggested that Unc13c could be closely associated with the CNS, and it could be a target gene for reducing PND. Tbx20 has been detected in unique neurons and epithelial cells of mouse and human embryonic eye tissues [29], associating with the CNS development [30]. Song studied Tbx20 as a gene associated with motor neurons. However, most studies on Tbx20 have concentrated on the cardiovascular system, and Tbx20 was found to play a variety of basic roles in cardiovascular development and homeostasis in response to pathophysiological stress, as well as cardiac remodeling [31, 32]. However, the relationship between Tbx20 and PND needs to be studied by more experiments. St8sia2 syntheses polysialic acid (PSA), which is crucial for cerebral development and is closely related to synaptic plasticity [33]. Lukasz et al. suggested that St8sia2 is a candidate gene that is associated with brain development and plasticity in schizophrenia. Sebastian et al. demonstrated that St8sia2 promotes oligodendrocyte differentiation and myelin repair [34]. Reported in a patient with behavioral disorders, epilepsy and autism spectrum disorders, St8sia2 was expressed in the developing brain, and it showed to play an important role in neuronal migration, axon guidance, and synaptic plasticity [35]. A study on the role of neurodevelopment and genes in psychiatric comorbidity and the regulation of inflammatory process in AD revealed that St8sia2 was only associated with the existence of clinical depression, rather than with AD, and St8sia2 could regulate inflammatory factors (IL-6, IL-1β, etc.) [36]. TANTRA [37] showed that St8sia2 could protect juvenile mice from delayed cognitive impairment several years after cannabis exposure. Taken together, St8sia2 could regulate the cognitive function of the brain, while the specific regulatory mechanism needs more experimental studies. As a result, we found that Unc13c and St8sia2 were closely associated with the CNS and cognitive function, and they could regulate PND. The results of GO and KEGG pathway enrichment analyses revealed that, 530 differentially expressed genes were enriched in B cell proliferation and transmembrane receptor protein serine/threonine kinase signaling pathway functions. This suggested that PND could be related to immunity. Few studies have systematically screened biomarkers associated with PND immune infiltration. To further explore the role of immune cell infiltration in PND, CIBERSORT was used to comprehensively evaluate PND. The results showed that the number of mast cells in PND group was significantly reduced compared with that in the normal group. This is consistent with the results of a study on depression: isolated social stress caused depression in mice, significantly reducing the total number of mast cells in the brain by 90% on the first day [38]. There are also conflicting and controversial findings about the role of mast cells. On the one hand, mast cells seem to mediate neuroinflammation protection. Mast cells release specific lyase McP-4 to degrade inflammatory cytokines in a mouse model of traumatic brain injury (TBI) to modulate the CNS disease [39]. Some proteases released by mast cells play homeostasis, protection, and even anti-inflammatory roles [40]. On the other hand, palmitoethanolamide (PEA) induced attenuation of the number of mast cells, amylase and trypsin, and increased cerebral edema, infarct volume, and brain injury in experimental TBI mice [41]. Mast cells release mediators into the CNS, promoting neurogenesis, such as serotonin and IL-6, providing neuroprotection (e.g., IL-1β), and maintaining the integrity of the blood-brain barrier (BBB), such as histamine. However, excessive levels of these mediators have detrimental effects on the integrity of neurons and the BBB. These results seem to be contradictory, whereas the effect of inflammation on TBI may be beneficial or detrimental depending on the time after injury and the stage of TBI. Logically speaking, the role of mast cells in these different stages is two-fold [42]. The results of the correlation between Unc13c and St8sia2 and immune cells showed that the combination of Unc13c and St8sia2 significantly decreased in the surgery group compared with that in the control group, suggesting that Unc13c and St8sia2 could protect PND by regulating immunity. In recent years, the role of T cells in neurodegenerative diseases has noticeably attracted scholars’ attention [15]. In healthy cerebrospinal fluid without inflammation, 90% of cells were reported to be T cells, mainly CD4 cells [43]. In pathological conditions, T cells can penetrate the brain parenchyma, while CD4+ T cells may play a role in the pro-inflammatory process of postoperative cognitive dysfunction [44]. Regulatory CD4 cells (Treg cells) provide neuroprotection by attenuating microglial activation in the CNS diseases [45]. Zhu et al. [44] demonstrated that PND is related to the increased number of T cells and NK cells in the hippocampus of aging mice after surgery and anesthesia. NK cell involvement in neurodegenerative diseases, such as multiple sclerosis and AD, has been reported [46]. The reduction of NK cell activity was previously found in AD patients [47]. It is noteworthy that NK cell infiltration into the BBB may not be a defense response, while it could be the result of PND progression, leading to immune system activation. The results of qRT-PCR and Western blotting of Tbx20 combined with the correlation of Tbx20 immune cells suggested that Tbx20 might be a hub gene, promoting PND. Chemokines are a large family of small cytokines that control the migration and residence of all immune cells. The CXC family includes chemokines CXCL1–CXCL17, and the CC family includes CCL1-CCL28. CC chemokines not only stimulate monocytes, but also basophils, eosinophils, T lymphocytes, and NK cells [48]. Studies have shown that changes in chemokines and chemokine receptors play an important role in the inflammatory response to AD, in which CCL1 and CCR8 prevent bacterial infection from participating in the demyelination of AD. The results of the present study showed that Tbx20 was positively correlated with CCL1, suggesting that Tbx20 could regulate immunity and participate in the development of neurodegenerative diseases. Unc13c and St8sia2 were significantly correlated with CCL9, which could be closely related to PND [49, 50]. These evidences suggested that the three hub genes, which are closely related to levels of immune cell infiltration, play a critical role in the immune microenvironment. It is possible to influence the occurrence of PND by regulating the whole immune process. Autophagy is an important pathway to eliminate abnormal protein aggregation in mammalian cells, which is related to protein homeostasis and neuronal health. Several studies have recently shown a close relationship between autophagy and PND [10, 51, 52]. Based on the close relationship between the transmembrane receptor protein serine/threonine kinase pathway of GO function and mTOR and autophagy, we conducted correlation analysis of hub genes and autophagy-related genes, and found that Unc13c, Tbx20, and St8sia2 were significantly correlated with different autophagy-related factors. Therefore, we speculated that these hub genes might regulate PND in aging mice through autophagy. Finally, nine groups of ceRNA networks were identified, including mmu_circ_0000331/miR-1224-3p/Unc13c, mmu_circ_0000400/miR-5120/Unc13c, mmu_circ_0000331/miR-5134-5p/Unc13c, mmu_circ_0000331/miR-1224-3p/Tbx20, mmu_circ_0000400/miR-504-3p/Tbx20; mmu_circ_0000331/miR-5134-5p/Tbx20, mmu_circ_0000406/miR-24-3p/St8sia2, mmu_circ_0000400/miR-6396/St8sia2, and mmu_circ_0000798/miR-672-5p/St8sia2. Through literature review of each circRNA and miRNA, miR-24-3p was found to be closely related to AD [53–55]. Among them, miR-24-3p in Liu et al.’s study was noted to be negatively correlated with the score of mild mental state testing of AD, and down-regulation of miR-24-3p could promote cell proliferation and inhibit cell apoptosis [56]. Zhang et al.’s findings were different, in which mice with AD showed a decreasing trend of miR-24-3p with age [57]. These results suggest that miR-24-3p may be closely related to PND. In addition, miR-1224-3p was found to be highly expressed in the hippocampus, and the decrease of miR-1224-3p could promote the growth and metastasis of glioma cells [58, 59]. Other circRNAs and miRNAs have not been found in studies on the CNS diseases. Hence, we will concentrate on the regulatory roles of mmu_circ_0000331/miR-1224-3p/Unc13c and mmu_circ_0000406/mmu-miR-24-3p/St8sia2 in PND.
As of the time of our analysis of this project, only one circRNA dataset (GSE174410) on postoperative neurocognitive disorder was available in the GEO public database. Therefore, subsequent studies by our group will also be conducted based on this dataset. If more circRNA datasets can be selected in the GEO database, the bias of the experiment can be reduced. In addition, the four drugs we screened will be validated in the follow-up study; and the mechanism of action of screening PND-related ceRNAs will also be studied in more depth.
In summary, we detected three mRNAs and four circRNAs that were associated with immune regulation of PND using publicly available data and combined mRNA analysis of high-throughput sequencing of aging mouse hippocampus after splenectomy by our experimental data. The results of the qRT-PCR verified the differential expression of these seven genes in the control and surgical groups, and Western blotting re-verified the differential expression of the three mRNAs in the two groups. After reviewing the literature to remove the genes that were not associated with the CNS, two new ceRNA hub regulatory networks were finally constructed. The data of differentially expressed genes measured by our experiments were analyzed using CMap, and four active compounds (cimaterol, rucaparib, FG-7142, and hydrocortisone) were detected to interfere with gene expression. The results of the the present study may provide new insights into the immunomodulation-related pathogenesis and potential therapeutic targets of PND. |
PMC9648809 | 36170022 | Lars Erichsen,James Adjaye | Crosstalk between age accumulated DNA-damage and the SIRT1-AKT-GSK3ß axis in urine derived renal progenitor cells | 24-09-2022 | renal differentiation,aging,SIRT1,DNA-damage | The aging process is manifested by a multitude of inter-linked biological processes. These processes contribute to genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, de-regulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. The mammalian ortholog of the yeast silent information regulator (Sir2) SIRT1 is a NAD+-dependent class III histone deacetylase and has been recognized to be involved in many of the forementioned processes. Furthermore, the physiological activity of several Sirtuin family members has been connected to the regulation of life span of lower organisms (Caenorhabditis elegans and Drosophila melanogaster) as well as mammals. In the present study, we provide evidence that SIX2-positive urine derived renal progenitor cells-UdRPCs isolated directly from human urine show typical hallmarks of aging. This includes the subsequent transcriptional downregulation of SIRT1 and its downstream targets AKT and GSK3ß with increased donor age. This transcriptional downregulation is accompanied by an increase in DNA damage and transcriptional levels of several cell cycle inhibitors such as P16. We provide evidence that the renal progenitor transcription factor SIX2 binds to the coding sequence of SIRT1. Furthermore, we show that the SIRT1 promoter region is methylation sensitive and becomes methylated during aging, dividing them into SIRT1-high and -low expressing UdRPCs. Our results highlight the importance of SIRT1 in DNA damage repair recognition in UdRPCs and the control of differentiation by regulating the activation of GSK3β through AKT. | Crosstalk between age accumulated DNA-damage and the SIRT1-AKT-GSK3ß axis in urine derived renal progenitor cells
The aging process is manifested by a multitude of inter-linked biological processes. These processes contribute to genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, de-regulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication. The mammalian ortholog of the yeast silent information regulator (Sir2) SIRT1 is a NAD+-dependent class III histone deacetylase and has been recognized to be involved in many of the forementioned processes. Furthermore, the physiological activity of several Sirtuin family members has been connected to the regulation of life span of lower organisms (Caenorhabditis elegans and Drosophila melanogaster) as well as mammals. In the present study, we provide evidence that SIX2-positive urine derived renal progenitor cells-UdRPCs isolated directly from human urine show typical hallmarks of aging. This includes the subsequent transcriptional downregulation of SIRT1 and its downstream targets AKT and GSK3ß with increased donor age. This transcriptional downregulation is accompanied by an increase in DNA damage and transcriptional levels of several cell cycle inhibitors such as P16. We provide evidence that the renal progenitor transcription factor SIX2 binds to the coding sequence of SIRT1. Furthermore, we show that the SIRT1 promoter region is methylation sensitive and becomes methylated during aging, dividing them into SIRT1-high and -low expressing UdRPCs. Our results highlight the importance of SIRT1 in DNA damage repair recognition in UdRPCs and the control of differentiation by regulating the activation of GSK3β through AKT.
Recent demographic studies suggest a considerable increase in the number of elderly people within the next decades [1]. The aging process has been recognized as one of the main risk factors of the world’s most prevalent diseases, including neurodegenerative disorders, cancer, cardiovascular disease and metabolic disease [2]. Aged tissues are characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. On molecular level, Otin et al., proposed nine candidate hallmarks of aging, which are considered to contribute to the aging process in general and collectively contribute to the aging phenotype [3]. In detail these hallmarks are: genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient-sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, and altered intercellular communication [3]. As mammals age their cells accumulate somatic mutations and other forms of DNA damage, such as chromosomal abnormalities and changes in chromosome copy number [4]. When these alterations arise the cell cycle is arrested in G1 phase, mainly triggered by the activity of TP53 and/or TP16 [5–7]. Depending on the cell type, an active DNA damage response (DDR) has four potential outcomes, namely, transient cell cycle arrest coupled with DNA repair, apoptosis, senescence, or cell differentiation [8]. For example, it is well established that ataxia telangiectasia mutated (ATM) [9] and TP53 [10, 11] are capable of regulating hemopoietic stem cell quiescence or senescence and self-renewal. Furthermore, both show a biphasic response to DNA-damage in a dose dependent manner [11, 12]. A major pathway that becomes activated by the DDR is the phosphatidylinositol-3-kinase/protein kinase B pathway (PI3K/AKT) [13]. It has been recognized that ATM and DNA-dependent protein kinase (DNA-PKs) are involved in AKT activation at the site of double strand breaks and inhibition of AKT activity impairs the repair of DNA double-strand breaks (DBS) [14–16]. On the other hand, AKT is also activated by growth factors and promotes cell cycle progression at G1/S and G2/M transition [13, 17, 18]. SIRT1 is the mammalian ortholog of the yeast silent information regulator (Sir2) and as a NAD+-dependent class III histone deacetylase involved in many processes connected to aging, like apoptosis, cell differentiation, development, stress response, metabolism, and tumorigenesis [19–21]. The high number of cellular features that can be regulated by SIRT1 is based on its variety of target molecules. Beside its specificity for the histone proteins H1, H3 and H4 and thereby promoting the formation of heterochromatin and transcriptional repression, SIRT1 has been reported to also deacetylate several transcription factors [22–24], and apoptosis and cell-cycle regulating proteins, including TP53 [25, 26]. The physiological activity of several sirtuin family members has been connected to the regulation of life span of lower organisms such as Caenorhabditis elegans [27], and Drosophila melanogaster [28] as well as mammals [29]. While SIRT1 is recruited to DBS by ATM and is required for DNA damage repair [30], it has also been noticed that SIRT1 activity is negatively regulated by genotoxic stress via ATM interaction with deleted in breast cancer 1 (DBC1) [31, 32]. In senescent mesenchymal stem cells (MSCs) SIRT1 expression is reduced, while its over-expression delays the onset of senescence and the loss of differentiation capacity [33]. We recently reported human urine as a non-invasive source of renal stem cells with regenerative potential [34]. The urine derived renal progenitor cells (UdRPCs) express renal stem cell markers such as SIX2, CITED1 WT1, CD133, CD24 and CD106. Here we provide evidence that SIX2-positive urine derived renal progenitor cells-UdRPCs isolated from human urine show typical hallmarks of aging when obtained from elderly donors. This includes the transcriptional downregulation of SIRT1 and its downstream targets AKT and GSK3ß. This transcriptional downregulation is accompanied by an increase in DNA damage and transcriptional levels several cell cycle inhibitors such as P16. We provide evidence that the renal progenitor transcription factor SIX2 binds to the coding sequence of SIRT1 and both factors mutually influence the transcription of each other. Furthermore, we show that the SIRT1 promoter region is methylation sensitive and becomes subsequently methylated in UdRPCs derived from aged donors, dividing them into SIRT1 high and low expressing UdRPCs. We propose the SIRT1-AKT-GSK3ß axis to regulate and monitor self-renewal capacity of urine derived renal progenitor cells.
We recently reported MSCs isolated directly from urine samples. These cells express the renal stem cell markers SIX2, CITED1 WT1, CD133, CD24 and CD106, we referred to these cells as urine derived renal progenitor cells (UdRPCs) [34]. In this study, the progenitor cells were isolated from distinct individuals of mixed ethnicity with ages ranging from 21 to 77 years. It is well documented in the literature, that MSCs show decline of self-renewal capacity and of immunosuppressive properties with increased donor age and in vitro expansion [35–38]. As previously reported UdRPCs can be kept in culture for up to 12 passages, whereas cells from aged donors show a decline of proliferation capacity after 9-10 passages. Therefore, all experiments were carried out with UdRPCs after 7-8 passages. Hierarchical clustering analysis comparing the transcriptomes of UdRPCs revealed a distinct expression pattern of cells derived from donors aged between 21 to 51 years (young, green box) and 54 to 61 years (aged, red box) (Figure 1A). Microarray analysis revealed a common expressed gene-set of 11917 between UdRPCs derived from young- and elderly donors, while 750 genes were exclusively expressed in cells derived from young- and 155 genes in cells derived from elderly individuals by comparing the expressed gene set (det-p < 0.05) (Figure 1B). The most over-represented GO BP-terms common expressed in UdRPCs derived from young- and elderly donors are associated with metabolic processes such as, organic acid transport and regulation of ion transport as well as cell junction organization and cell morphogenesis involved in differentiation. The most over-represented GO BP-terms exclusive to the young UdRPCs are associated with DNA-replication, mesodermal cell differentiation, renal system development and PI3K-AKT signalling pathway. In comparison the most over-represented GO BP-terms exclusive to aged UdRPCs are associated with assembly of collagen fibrils and other multimeric structures and regulation of calcium ion transport (Figure 1C). The full gene list can be found in Supplementary Table 3. Additionally, we analysed our data-set for genes associated with the hallmarks of aging as proposed by Otin et al., (Figure 1D) [4]. The heatmap reveals a negative correlation between the donor age and the expression of genes involved in genomic instability (ATM, MSH6, TFEB and XRCC1), epigenetic alterations (SETDB1, KDM6A, EZH1, SIRT1, SETDB2, HDAC6, HDAC4, KDM4B and SIRT3) and genes involved in de-regulation of nutrient sensing of the one carbon-, cysteine- and methionine-metabolic pathways (SHMT1, SHMT2 and MAT2B). Furthermore, our transcriptome data reveals a positive correlation between donor age and the expression of genes involved in cellular senescence (CDKN1A, CDKN2A and CDKN2D) and stem cell exhaustion (CXCL1, IL6 and IL8). In conclusion, the transcriptome data revealed typical hallmarks of aging in UdRPCs derived from elderly donors.
Since our microarray data revealed genes encoding for mesodermal cell differentiation and renal system development to be exclusively expressed in UdRPCs derived from young donors, we assumed that this is caused by an age-associated decline of self-renewal capacity. To test this hypothesis, we applied immunofluorescent staining for the renal stem cell marker SIX2 and qRT-PCR analysis for the stem cell markers SIX2 and CD133. Surprisingly, the immunofluorescent staining revealed that almost 100% of the isolated cells from donors aged between 21 and 51 years are positive for SIX2 (Figure 2B). In contrast, qRT-PCR analysis revealed a significant downregulation/ 0.98-fold (p < 0.01) of SIX2 mRNA expression between cells derived from the 21-year-old donor compared to cells derived from all other donors. For the stem cell marker CD133 qRT-PCR analysis revealed a significant downregulation/ 1.16-fold (p ≤ 0.01) of mRNA expression between cells derived from donors aged between 21 and 48 compared to individuals aged between 51 and 77 years (Figure 2B). As a further marker of premature terminal differentiation and/or senescence [39] we assessed the truncated form of the Lamin A transcript Progerin by semi-quantitative PCR. Strikingly, we found an increase of truncated Lamin A transcript Progerin within UdRPCs derived donors aged between 21 and 48 compared to individuals aged between 51 and 77 years) (Figure 2C). Accumulation of Progerin has been described to lead to DNA-damage and chromosomal aberrations [40, 41], by inhibiting inter alia the SIRT6 mediated DNA-damage repair mechanism [42]. To test our hypothesis that the identified increase of Progerin mRNA in the aged UdRPCs is accompanied by increased DNA-damage, we analysed phosphorylation levels of Histone 2A (pH2A.X), an established biomarker of DNA-damage at DBS [43], by immunofluorescent based detection (Figure 2D). UdRPCs derived from individuals aged between 21 and 45 years showed a positive pH2A.X staining only in a small percentage of cells, 2% and 6% respectively. In contrast we detected a significant (p = 0.03) increase of DBS in cells derived from donors aged 51, 63 and 77 years, with 28%, 50% and 55% of cells being positive for the pH2A.X staining (Figure 2D and Supplementary Figure 1A). Furthermore, we applied qRT-PCR analysis for ATM, P16 and TP53 (Figure 2E and Supplementary Figure 1B). mRNA expression of CDKN2A and TP53 were found to be not significantly altered between UdRPCs derived the different donors (p-value P16: p = 0.91 and TP53: p = 0.41), with a trend for P16 being up- and TP53 being downregulated with increased donor age but could also reflect heterogeneity in-between individuals. In contrast, qRT-PCR analysis revealed a significant downregulation/ 0.85-fold (p < 0.05) of ATM mRNA expression between cells derived from the 21-years-old donor compared to UdRPCs derived from individuals aged between 27 and 77 years. Finally, we evaluated the expression of Methionine Adenosyltransferase 2B (MAT2b) by qRT-PCR. This enzyme catalyzes the final step of one carbon metabolism by forming S-Adenosyl-L-methionine from methionine and adenosine triphosphate. For MAT2b a significant downregulation/ 1.15-fold of mRNA levels were found in the UdRPCS derived from donors aged between 21 and 63 compared to donors aged 69 to 77 (p < 0.01) (Supplementary Figure 1B). In summary, UdRPCs show an increase in DNA damage with increased donor age, while MSC markers CD133 and the renal progenitor marker SIX2 were found to be downregulated.
SIRT1 [30] and the AKT pathway [14] are involved in DNA-damage-repair and our results indicate a downregulation of SIRT1 and members of the AKT pathway in UdRPCs derived from individuals aged 48 years and above. Therefore, we evaluated the expression of SIRT1 as well as the phosphorylation levels of H2A.X, AKT and its downstream target GSK3ß by Western blot detection (Figure 3A). Consistently, we found SIRT1 expression exclusively in the UdRPCs derived from individuals aged ≤ 48 years. Normalized to ß-actin expression we found a significant reduction of SIRT1 protein expression (p = 0.01) in UdRPCs derived from elderly donors by 81.3% and 64.7% (48 and 51 years). Our Western blot results revealed a similar observation for the phosphorylation levels of AKT and GSK3β, while total levels of AKT and GSK3ß expression were found to be unchanged. The ratio of the phosphorylated version to the total protein levels revealed a significant reduction of AKT (p = 0.05) and GSK3β (p = 0.04) protein phosphorylation in UdRPCs derived from donors aged 48 and 51 years, by 97.36% and 97.62% for AKT and 55.69% und 55.95% for GSK3β. Furthermore, we also evaluated H2A.X phosphorylation and observed an increase of DBS only in the sample derived from the 51-year-old individual. Normalized to ß-actin the increase was found to be not significant (p = 0.47) but became highly significant when H2A.X phosphorylation was normalized to the detected SIRT1 protein expression (p ≤ 0.01). To further confirm that SIRT1 becomes downregulated in UdRPCs derived from aged donors, we applied qRT-PCR analysis. According to the genecards database the SIRT1 mRNA has six major splicing variants. Almost all splicing variants consist of exon 4 to exon 6 and can be distinguished into two groups by the existence of exon 1-3 or exon 7-10 (Supplementary Figure 2). To evaluate which is the major splicing variant that changes in UdRPCs obtained from elderly donors, we designed primers that anneal in exon 1 and 2 as well as exon 7 and 8 (Supplementary Table 1). Surprisingly, we found both variants to be expressed and significantly altered (Exon 1-2: p = 0.03 and Exon 7-8: p = 0.01) between cells derived from the 21- and the 27-year-old donor compared to the UdRPCs derived from donors aged between 45 and 77 years, with fold changes of 0.54 and 0.75 respectively (Figure 3B). It has been recognized, that during the aging process “de novo” DNA methylation occurs within the promoter region of transcriptional downregulated genes [44, 45]. By applying genomic bisulfite sequencing we analysed the methylation status of the SIRT1 gene in UdRPCs derived from the 27-year- and the 51-year-old individual. In total a 341bp fragment of the SIRT1 promoter containing 45 CpG-dinucleotides were analysed. Stinkingly, we found 15.2% of CpGs to be methylated in the UdRPCs derived from the 27-year-old individual, while UdRPCs derived from the 51-year-old individual showed 61.3% of CpGs to be methylated. We concluded that UdRPCs could be distinguished in high and low SIRT1 expressing cells, depending on the donor age with a threshold of 48 years from our cohort. Finally, we wanted to investigate whether the age associated downregulation of SIX2, which is needed to maintain renal progenitor cells during kidney organogenesis [34], affects the level of SIRT1 mRNA expression rate. Since the UdRPCs derived from the 27-year-old individual showed the highest SIRT1 protein expression in Western blot analysis, we choose this cell line for Immunoprecipitation analysis. SIX2 has been reported to regulate the expression of Odd-skipped related 1 (Osr1) [46] and Glial Cell Derived Neurotrophic Factor (GDNF) [47] so we chose these as positive controls, while RPL0 was chosen as negative control. PCR analysis was performed with DNA derived from the whole cell extract (Input), after Immunoprecipitation with (IP) and without (negative control) antibodies against SIX2. Hereby, we could confirm a direct interaction between the SIX2 transcription factor and the genomic DNA of GDNF, OSR1 and SIRT1 (Figure 3D). We hypothesize that for the aging process of UdRPCs, the renal progenitor transcription factor SIX2 and SIRT1 mutually influence their transcriptional levels positively in cells derived from donors aged ≤ 48 years. In cells aged >48 and above we found the transcriptional downregulation of SIX2 and SIRT1, for which DNA-Methylation seems causative in the latter case. This downregulation of SIRT1 is associated with reduced phosphorylation levels of AKT and GSK3ß and renders the cells more vulnerable to double strand breaks.
Resveratrol has been recognized as a potent activator of SIRT1 [48, 49]. Therefore, we evaluated by qRT-PCR the effect of varying concentrations of resveratrol on low SIRT1 expressing UdRPCs with and without age-associated DNA damage. We prepared final concentrations of resveratrol directly in the cell culture medium ranging from 0.1μM to 250μM and treated the UdRPCs derived from the 48- and 51-year- old individual for 24h. Strikingly we detected a significant increase of SIRT1 Exon 7-8 mRNA (48: p = 0.05; 9-fold and 51: p = 0.04; 0.44-fold) and SIRT1 Exon 1-2 only in the UdRPCs derived from the 51-year-old-individual (p < 0.01; 0.4-fold) (Figure 4B, 4F and Supplementary Figure 3A). In contrast, the resveratrol treatment caused a significant downregulation of SIRT1 Exon 1-2 mRNA in the UdRPCs derived from the 48-year-old-individual (p = 0.02; 0.52-fold) (Supplementary Figure 3A). Immunofluorescence-based detection and Western blot analysis revealed a significant upregulation of SIRT1 protein by 50% and 120% respectively (48: p = 0.04 and 51: p = 0.03) for both cell lines treated with 1μM resveratrol (Figure 3A, 3D, 3H and Supplementary Figure 3A). In contrast a significant downregulation of SIRT1 mRNA (48: p < 0.01 and 51: p < 0.01) and protein (48: p < 0.01) was detected within the cells treated with the resveratrol solutions containing 2.5μM and higher concentrations by 100 and 50% respectively (Figure 4D, 4H and Supplementary Figure 3A). To test if this effect is due to the resveratrol, we also measured the expression level of MAT2B. The promoter region of this gene has been identified to harbors two resveratrol binding pockets and gets activated by resveratrol in a time- and dose-dependent manner [50]. As expected MAT2B mRNA expression levels were significantly upregulated (48: p < 0.01 / 1.2-fold change and 51: p < 0.01 / 1.24-fold change) in the UdRPCs treated with low- concentrations and significantly downregulated (48: p < 0.01 / 0.97-fold change and 51: p < 0.01 / 0.27-fold change) in the cells treated with the high concentrations of resveratrol. These results correlate with the observed changes in SIRT1 mRNA expression (Supplementary Figure 3B). Furthermore, immunofluorescence- based and Western blot detection of pH2A.X revealed no DBS in UdRPCs derived from the 48-year-old individual under control conditions and in the cells treated with the 1μM resveratrol solution, but a significant increase of DBS (p < 0.01; 86% of cells were found to be pH2A.X positive) was detected when cells were treated with the 2.5μM resveratrol solution (Figure 4A, 4D). Accordingly, 54% of UdRPCs obtained from the 51-year-old individual revealed a positive pH2A.X staining under control conditions, which became significantly elevated (51: p = 0.05; 71% of cells were found to be pH2A.X positive) by the 2.5μM resveratrol solution (Figure 4A, 4H). Strikingly only 34% of cells treated with 1μM resveratrol were positive for pH2A.X expression (51: p = 0.05). To test our hypothesis if resveratrol induced activation of SIRT1 prevents cellular senescence by increasing DNA damage repair mechanisms, we evaluated mRNA expression levels of ATM and the cell cycle regulator P16 by qRT-PCR (Figure 4B, 4F and Supplementary Figure 3B). We found a significant upregulation / 26-fold of mRNA expression in the UdRPCs derived from the 48-year-old donor treated with low concentrations and a significant downregulation/ 0.99-fold and 0.5-fold in cells derived from both donors 48 and 51 treated with high concentrations of resveratrol (48: p < 0.01, 51: p < 0.01). In contrast P16 expression levels became significantly downregulated/ 0.62-fold in UdRPCs derived from the 48-year-old donor with the low concentration and significantly upregulated 8.8-fold when cells were treated with the high concentrations of resveratrol (48: p < 0.01). Next, we assessed the effects of resveratrol on the stem cell characteristics by measuring the expression level of the renal progenitor marker SIX2 and CD133 (Figure 4A, 4F and Supplementary Figure 3B) as well as the occurrence of the aberrant Lamin A splicing variant Progerin (Figure 4E, 4I). In both cell cultures we found a non-significant upregulation/ 0.35-fold and 0.16-fold of SIX2 mRNA (48: p < 0.31 and 51: p < 0.27) when cells were treated with the low concentrations of resveratrol, while high concentrations of resveratrol significantly downregulated/ 0.99-fold and 0.45-fold (48: p < 0.01 and 51: p < 0.01) SIX2 mRNA expression levels. In contrast for CD133 a significant upregulation/ 5.3-fold was only found in the UdRPCs derived from the 48-year-old individual (p = 0.02) when treated with the low and a significant downregulation/ 0.83-fold (p < 0.01) when treated with the high concentrations of resveratrol. UdRPCs derived from the 51-year-old individual showed no difference in CD133 mRNA expression when treated with the low and a slightly but not significant (p = 0.2) downregulation/ 0.31-fold of CD133 when treated with the high concentration of resveratrol. The aberrant Lamin A splicing variant Progerin could not be detected in the UdRPCs derived from the 48-year-old individual treated with 0μM, 0.1μM or 1μM of resveratrol, but alternate splicing of Lamin A occurred when cells were treated with the 2.5μM resveratrol solution. In contrast, in the UdRPCs derived from the 51-year-old individual the two aberrant splicing variants were detected in all samples, but in cells treated with low concentrations of resveratrol the intensity of one of the detected bands became much weaker. Next, we performed Western blot analysis of the downstream targets of SIRT1, namely AKT and GSK3ß (Figure 4D, 4H and Supplementary Figure 3A). For both cell lines we did not observe a change in total AKT nor GSK3ß protein expression in either treatment. We found that in the low DNA damage UdRPCs derived from the 48-year-old individual GSK3β-phosphorylation (p = 0.04) was significantly increased by 32% when the cells were treated with the 1μM solution of resveratrol. Furthermore, within cells from the 48-year-old individual treated with the 2.5μM solution of resveratrol GSK3β phosphorylation was found to be significantly decreased by 56% (p < 0.01). In contrast, AKT phosphorylation was found to be significantly decreased when cells were treated with either of the resveratrol solutions by 41.19% and 67.86% respectively (p < 0.01). In the high DNA damage UdRPCs derived from the 51-year-old individual no significant changes in AKT or GSK3ß phosphorylation were observed. Finally, we evaluated the effects of the resveratrol treatment on the proliferative capacity of UdRPCs (Figure 4C, 4G). After 24h cells from both donors treated with the 2.5 μM solution of resveratrol showed a significant decrease in cell number (p < 0.01) (Figure 4G). In contrast only the UdRPCs derived from the 48-year-old individual showed a significant increase in cell number after 24h of 0.1 and 1μM resveratrol treatment (Figure 4C). In summary, resveratrol can activate SIRT1 in a dose dependent manner in UdRPCs. A high dose >2.5μM enhances age- associated phenotypes whereas low doses <1μM induce the opposite effect.
DNA damage can trigger four potential outcomes namely, transient cell cycle arrest coupled with DNA repair, apoptosis, senescence or cell differentiation [9]. SIRT1 has been reported to participate in all of the mentioned biological processes, so we tested the effect of endogenous induced DNA damage on SIRT1 expression in UdRPCs, expressing high levels of SIRT1 [20–22]. Bleomycin has been recognized as potent inducer of DBS for many years [51, 52], so we prepared final concentrations of 1μM resveratrol, 30μg/ml Bleomycin and a combination of both substances directly in the cell culture medium and treated the UdRPCs derived from the 27-year- old individual for 24h. Interestingly while resveratrol treated UdRPCs showed a significant increase/ 0.41-fold in SIRT1 mRNA expression (p < 0.01), upon resveratrol treatment (Figure 5B), we found SIRT1 protein expression to be unchanged between resveratrol treated and control cells (Figure 5C and Supplementary Figure 4B). In contrast, cells treated with the combination of resveratrol and bleomycin showed no changes in mRNA expression, while bleomycin alone showed a significant decrease/ 0.21-fold in SIRT1 mRNA as well as protein expression (p < 0.01) (Figure 5C and Supplementary Figure 4B). Immunofluorescence- based detection of H2A.X phosphorylation revealed no beneficial effect of the resveratrol treatment alone, with 34% (control) and 33% (1μM resveratrol) of cells being positive for DBS. In contrast UdRPCs treated with either Bleomycin alone or the combination of 1μM resveratrol and 30μg/ml Bleomycin showed a significant increase (p < 0.01), with 78% and 59% of cells being positive for pH2A.X expression (Figure 5A). Furthermore, Western blot analysis of H2A.X phosphorylation normalized either to ß-actin or SIRT1 expression revealed a significant increase (p < 0.01) of DBS in cells treated with Bleomycin (Figure 5C). In contrast H2A.X phosphorylation could not be detected in either the control, the resveratrol, and the resveratrol + Bleomycin treated samples (Figure 5C). Strikingly the increased H2A.X phosphorylation was accompanied by a significant increase/ 9.23-fold of P16 mRNA expression, while for all other conditions expression levels were found to be unchanged (p < 0.01). Surprisingly ATM mRNA expression levels were also found to be unchanged, with a slight downregulation in the resveratrol treated samples. Next, we assessed the effects of the resveratrol and/or Bleomycin on the stem cell characteristics of UdRPCs by measuring the expression level of the renal progenitor marker SIX2 and CD133 (Figure 5B). In accordance with our previous data, UdRPCs treated with resveratrol alone and in combination with Bleomycin showed an upregulation of SIX2 (1.38-fold) and CD133 (4-fold) mRNA levels. While this upregulation was found to be not significant when cells were treated with the combination of Resveratrol and Bleomycin (p = 0.25), the upregulation became highly significant by resveratrol treatment alone (p = 0.01). In contrast bleomycin treatment alone slightly downregulated mRNA expression levels of SIX2 and CD133 (Figure 5B and Supplementary Figure 4A). Finally, we performed western blot analysis of the downstream targets of SIRT1, namely AKT and GSK3ß (Figure 5C and Supplementary Figure 4B). Total levels of AKT were found to be significantly downregulated by 53.14% in the bleomycin treated cells, while total levels of GSK3ß were found to be significantly upregulated by 114% in the resveratrol treated cells. While AKT phosphorylation was found to be significantly upregulated in resveratrol only treated cells by 108% (p=0.05). UdRPCs treated with the combination of resveratrol and bleomycin and the bleomycin alone treated cells did not show a significant change. Interestingly GSK3β phosphorylation was found to be significantly downregulated in UdRPCs derived from the 27-year-old individual under all culture conditions (by 71.78% with Resveratrol, by 77.8% with Resveratrol and Bleomycin and 94.36% with Bleomycin) (Figure 5B and Supplementary Figure 4A). In conclusion, DNA-damage can induce an aging phenotype by downregulation of SIRT1.
MSCs show a decline of self-renewal capacity and immunosuppressive properties with increased donor age and in vitro expansion [36, 38, 53]. In the present manuscript we provide evidence that UdRPCs directly isolated from human urine show typical hallmarks of aging when obtained from elderly donors. Our transcriptome data reveals the upregulation of genes involved in cellular senescence (CDKN1A, CDKN2A and CDKN2D) and inflammation (CXCL1, IL6 and IL8) with increased donor age. In particular, the cell cycle regulator P16 (or CDKN2A) is believed to play a crucial role in mediating cellular senescence and preventing tumour growth [54, 55]. Furthermore, P16 expression has been linked to the extension of normal cellular lifespan [56]. In contrast our transcriptome data revealed a negative correlation between the donor age and the expression of genes involved in genomic instability (ATM, MSH6, TFEB and XRCC1), epigenetic alterations (SETDB1, KDM6A, EZH1, SIRT1, SETDB2, HDAC6, HDAC4, KDM4B and SIRT3) and genes involved in deregulation of nutrient sensing of the one carbon-, cysteine- and methionine-metabolic pathways (SHMT1, SHMT2 and MAT2B). Somatic cells acquire mutations and other forms of DNA damage as mammals age with four potential outcomes for the affected cell namely, transient cell cycle arrest coupled with DNA repair, apoptosis, senescence or cell differentiation [9]. UdRPCs derived from aged donors show increased phosphorylation levels of Histone 2A (pH2A.X), which is an established biomarker of DNA-damage at double strand breaks [44]. This increased amount of double strand breaks is accompanied by a downregulation of ATM and reduced phosphorylation levels of AKT and GSK3β. AKT signalling has been recognized to be positively affected by ATM [13] and needed for double strand break repair [57]. Furthermore, a “stemness checkpoint” controlled by ATM has been suggested. Hereby, double strand break initiated ATM signalling maintains MSCs and blocks differentiation [9]. This proposed “stemness checkpoint” is also reflected in our data. UdRPCs derived from young donors show low level of DNA damage accompanied with high expression levels of ATM and stem cell markers CD133 and SIX2. Furthermore, these cells also show high phosphorylation levels of AKT and GSK3β. In contrast UdRPCs derived from elderly donors show the direct opposite expression patterns for all the mentioned factors. Of note, GSK3β inhibition has already been linked to kidney progenitor differentiation [34]. This hypothesis that UdRPCs derived from aged donors might be more prone to differentiation is strengthened by the increased amount of the aberrant splicing form of Lamin A (Progerin). Since Progerin has been recognized as a marker of premature terminal differentiation and/or senescence [39]. The physiological activity of several sirtuin family members has been connected to the regulation of life span of lower organisms such as Caenorhabditis elegans [27], and Drosophila melanogaster [28] as well as mammals [29]. SIRT1 is the mammalian ortholog of the yeast silent information regulator (Sir2) and as a NAD+-dependent class III histone deacetylase with a wide variety of target molecules. Therefore its deacetylase activity has been linked to many biological processes connected to aging, examples- apoptosis, cell differentiation, development, stress response, metabolism, and tumorigenesis [20–22]. Interestingly, SIRT1 is recruited to double strand breaks by ATM and is required for DNA damage repair [30]. Our transcriptome data reveals a downregulation of SIRT1 in UdRPCs derived from elderly donors. By applying genomic bisulfite sequencing we show that the SIRT1 promoter is methylation sensitive and found to be hypermethylated in UdRPCs derived from an elderly donor. It is well known that for the preservation of an unmethylated promoter DNA-methyltransferases must be excluded from the 5’-regulatory regions, which is strongly promoted by the binding of transcription factors. If a gene becomes transcriptional inactive this can lead to the progressive methylation within the 5’-regulatory region [58]. Elevated levels of genotoxic substances have been linked to increased DNA adducts, higher amounts of DNA damage and increased levels of DNMT1 expression [59]. Therefore, it is tempting to speculate that the DNA methylation changes found within the SIRT1 promoter might be a direct consequence of the increased levels of DBS in UdRPCs derived from elderly donors. Furthermore, it has been recognized that DNA damage in vitro results in decreased SIRT1 activity [31] and that SIRT1 expression is reduced in senescent mesenchymal stem cells (MSCs), while its overexpression delays the onset of senescence and the loss of differentiation capacity [33]. When UdRPCs are treated with genotoxic substances (e.g., high doses of resveratrol or bleomycin), we observed a complete down-regulation of SIRT1 mRNA and protein expression. Furthermore, high doses of genotoxic substances caused upregulation of CDKN2A accompanied with increased phosphorylation levels of H2A.X. Of note also several studies reported high doses of Resveratrol being causative for the induction of replicative stress [60], DNA-damage [61] and even premature senescence [62]. Furthermore, the expression of stem cell markers SIX2 and CD133, as well as the phosphorylation levels of AKT and GSK3ß were found to be exclusively down-, while Progerin expression was up-regulated. Strikingly, when UdRPCs are treated with low concentrations of resveratrol, which has been recognized as a potent activator of SIRT1 [48, 49], the mentioned changes within the UdRPCs could be partially reversed. Consistent in all treated UdRPCs, resveratrol caused an upregulation of SIRT1 mRNA and protein, which was accompanied by the transcriptional upregulation of the stem cell markers CD133 and SIX2, while P16 expression was consistently downregulated. Renal progenitor surface marker CD24 and stem cell self-renewal marker CD133 are required for primordial nephrogenesis [63, 64]. SIRT1 is known to co- localize with CD133 and Sirt1 deficiency has been recognized to decrease the percentage of CD133 positive cells [65], while resveratrol treatment is associated with an increase in CD133 expression in human bone marrow mesenchymal stem cells [66]. Furthermore, dependent on the accumulated DNA damage in the UdRPCs, resveratrol treatment induced an upregulation of GSK3ß phosphorylation, which we conclude might enhance the self-renewal and proliferation capacity of the treated cells. This causative correlation between increased SIRT1 expression and cellular differentiation has been shown in mesenchymal stem cell models during neuronal differentiation [66]. Additionally, increased SIRT1 protein expression was found to be protective against DBS, even when cells were treated with bleomycin. Our results highlight the importance of SIRT1 in DNA damage repair recognition in UdRPCs and ultimately the control of differentiation by regulating the activation of GSK3ß. Furthermore, UdRPCs can be distinguished into SIRT1 high and low expressing UdRPCs, rendering the cells with low expression levels more vulnerable to endogenous noxae. This might accelerate the accumulation of DNA damage and ultimately the accumulation of aging associated hallmarks. To our knowledge this is the first study that reports a physical interaction of the renal progenitor marker SIX2 with the SIRT1 promoter region. The transcription factor SIX2 is needed to maintain renal progenitor cells during kidney organogenesis [34]. In UdRPCs derived from elderly donors we found a decrease in SIX2 as well as SIRT1 mRNA, while cells derived from young donors showed high expression levels accompanied with the already discussed consequences for cellular differentiation. This makes it tempting to speculate that both SIX2 and SIRT1 are needed to maintain self-renewal in UdRPCs and that both factors positively regulate each other. This is further strengthened by the fact that the other factors (OSR1 and GNDF) which we used as positive controls for our pull down experiment have been reported to either maintain self-renewal of nephron progenitor cells (OSR1 [46]) or to participate in the developmental process of kidney organogenesis (GDNF [47]). Furthermore, in a mouse model of acute kidney injury exosomes derived from adipose tissue mesenchymal stem cells were found to mediate a renal protective effect by the activation of the SIRT1 pathway [67]. Acute kidney injury has a high and increasing incidence in the elderly population [68] and can be caused by increased amounts of DNA-damage [69]. DNA damage has been recognized as one of the primary hallmarks [4] of aging and we propose that upon genotoxic stress either SIX2 or SIRT1 might be aberrantly regulated. The consequence of this might be even elevated levels of DBS leading to cellular senescence or differentiation and ultimately carcinogenic transformation. This mode of action has been proposed for the etiology of bladder cancer formation in the PrimeEpiHit hypothesis [70]. Of note, our transcriptome data revealed a downregulation of genes associated with the carbon-, cysteine- and methionine-metabolic pathways, including MAT2B. The promoter region of this gene has been identified to contain two resveratrol binding pockets and gets activated by resveratrol in a time- and dose-dependent manner [50]. A comparison with the cancer genome atlas TCGA reveals a downregulation of SIRT1 and MAT2B for several urogenital cancer entities like, Bladder Urothelial Carcinoma, Kidney renal papillary cell carcinoma, Kidney Chromophobe, Pan-kidney cohort (KICH+KIRC+KIRP) (Supplementary Figure 5). In summary we provide evidence for a direct interaction between the renal progenitor transcription factor SIX2 and the NAD+-dependent class III histone deacetylase SIRT1. Both factors are needed to maintain self-renewal of CD133-positive UdRPCs. Hereby, SIRT1 is involved in deacetylation and thereby activation of protein kinase B (AKT) [23] as well as deacetylation and thereby inactivation of ß-Catenin [25]. Furthermore, AKT needs to be activated by ATM [71], even though the phosphorylation is indirect [14]. Activated AKT dephosphorylates and thereby inactivates GSK3β [71]. GSK3β phosphorylates ß-Catenin, which then become disassembled by the proteasome. Unphosphorylated and acetylated ß-Catenin is transferred to the nucleus, where it binds to TCF4 and induces nephrogenesis via activation of WNT signaling [72, 73] (Figure 6).
UdRPCs were isolated as described in Rahman et al. [34] and were cultured in Proliferation Medium (PM) composed of 50% DMEM high Glucose and 50% Keratinocyte medium supplemented with 5% FCS, 0.5% NEAA, 0.25% Gtx and 0.5% Penicillin and Streptomycin at 37° C (Gibco, Carlsbad, CA, USA) under hypoxic conditions. For all experiments cells were collected after 7-8 passages and seeded in 6- or 12-well plates coated with 0.2% Gelatin (Thermo Fisher Scientific, Waltham, MA, USA). Resveratrol (Sigma Aldrich, St. Louis, MO, USA) and Bleomycin (Sigma Aldrich) were added to the the culture medium to a final concentration of 30 μg/ml. Cells were incubated with Resveratrol and Bleomycin containing culture medium for 24h.
Total RNA was extracted from UdRPCs using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. First-strand cDNA synthesis was performed from 1 μg RNA by reverse transcription using oligo(dT) (Promega, Madison, WI, USA) and Moloney murine leukemia virus reverse transcriptase (Promega) in a volume of 50 μL at 42° C for 1 h. Real time PCR of aging associated gene expression was performed as follows: Real time measurements were carried out on the Step One Plus Real Time PCR Systems using MicroAmp Fast optical 384 Well Reaction Plate and Power Sybr Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA). The amplification conditions were denaturation at 95° C for 13 min. followed by 37 cycles of 95° C for 50s, 60° C for 45s and 72° C for 30s. Primer sequences are listed in Supplementary Table 1.
Cells were fixed with 4% paraformaldehyde (PFA) (Polysciences, Warrington, PA, USA). Unspecific binding sites of the fixed cells were blocked by incubation with blocking buffer containing 10% normal goat or donkey serum, 1% BSA, 0.5% Triton, and 0.05% Tween, for 2h at room temperature. The primary antibody was diluted 1:1 in blocking buffer with PBS and incubated at 4° C overnight (or at least 16h). After incubation the cells were washed three times with PBS/0.05% Tween and the secondary antibodies were diluted the same way as the primary antibodies with a 1:500 dilution. After 1h of secondary antibody incubation the cells were washed again three times with PBS/0.05% Tween and nuclei were stained with Hoechst 1:5000 (Thermo Fisher Scientific) and cytoskeleton was stained with Alexa Flour 488 phalloidin (Thermo Fisher Scientific) (1:400). Images were captured using a fluorescence microscope (LSM700; Zeiss, Oberkochen, Germany) with Zenblue software (Zeiss). Individual channel images were processed with Fiji. Detailed Information of the used antibodies are given in Supplementary Table 2.
Total RNA (1 μg) preparations were hybridized on the PrimeView Human Gene Expression Array (Affymetrix, Thermo Fisher Scientific, USA) at the core facility Biomedizinisches Forschungszentrum (BMFZ) of the Heinrich Heine University Düsseldorf. The raw data was imported into the R/Bioconductor environment and further processed with the package affy using background-correction, logarithmic (base 2) transformation and normalization with the Robust Multi-array Average (RMA) method. The heatmap.2 function from the gplots package was applied for cluster analysis and to generate heatmaps using Pearson correlation as similarity measure. Gene expression was detected using a detection-p-value threshold of 0.05. Differential gene expression was determined via the p-value from the limma package which was adjusted for false discovery rate using the q value package. Thresholds of 1.33 and 0.75 were used for up-/down-regulation of ratios and 0.05 for p-values. Venn diagrams were generated with the Venn Diagram package. Subsets from the venn diagrams were used for follow-up GO and pathway analyses as described by Zhou et al. [74]. Gene expression data will be available online at the National Centre of Biotechnology Information (NCBI) Gene Expression Omnibus.
UdRPCs were lysed in lysis buffer composed of 5M NaCl, 1% NP-40, 0.5% DOC, 0.1% SDS, 1 mM EDTA, 50mM Tris, pH 8.0, and freshly added 10μL/mL protease- and phosphatase inhibitor (Sigma Aldrich). 20μg of the obtained protein lysate was resolved in a 10% sodium dodecyl sulfate-PAGE gel and transferred onto Immobilon-P membrane (Merck Millipore, Burlington, MA, USA). Membranes were probed with primary antibody at 4° C overnight, washed three times with 0.1% Tween-20 in Tris-buffered saline, and incubated with secondary antibody for 1h at room temperature. The signals were visualized with enhanced luminescence Western Bright Quantum (Advansta, San Jose, CA, USA). Detailed Information of the used antibodies are given in Supplementary Table 2.
Total RNA was extracted from UdRPCs using the RNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. First-strand cDNA synthesis was performed from 1μg RNA by reverse transcription using oligo(dT) (Promega, Madison, WI, USA) and Moloney murine leukemia virus reverse transcriptase (Promega) in a volume of 50μL at 42° C for 1 h. Lamin A and Progerin were detected as described by McClintock et al., [75]. Primer sequences are listed in Supplementary Table 1.
Cells were chemically crosslinked with 11% formaldehyde solution for 15min at room temperature. Cells were washed twice with 1× PBS and harvested using a silicon scraper in a lysis buffer, and genomic DNA was sonicated at 4° C in TPX® polymethylpentene tubes using a Bioruptor® sonicator (Diagenode, Liege, Belgium). Twenty sonication pulses of each 15sec were applied. The resulting wholecell extract (WCE) was incubated overnight at 4° C with 100μl of Dynal Protein A magnetic beads (Diagenode) previously pre-incubated with (input) and without (negative control) 10μg of SIX2 antibody. Beads were washed five times with RIPA buffer and once with TE containing 50mM NaCl. Bound complexes were eluted from the beads by heating at 65° C with occasional vortexing, and crosslinking was reversed by overnight incubation at 65° C. Input and negative control were also treated for crosslink reversal. Immunoprecipitated DNA and whole-cell extract DNA were then purified by treatment with RNase A, proteinase K, multiple phenol: chloroform:isoamyl alcohol extractions and precipitation with ethanol. Purified DNA was amplified using the PCR protocol.
Bisulfite sequencing was performed following bisulfite conversion with the EpiTec Kit (Qiagen, Hilden, Germany) as described in Erichsen et al. [70]. PCR primer sequences are given in Supplementary Table 1 and refer to +1 transcription start of the following sequences: Homo sapiens sirtuin 1 (SIRT1), RefSeqGene on chromosome 10 NCBI Reference Sequence: NG_050664.1 Obtained sequences were analysed using Quma (http://quma.cdb.riken.jp/) as described in [76].
Data is presented as arithmetic means + standard error of mean. At least three experiments were used for the calculation of mean values. To address the statistical significance, we applied the two-samples Student’s t-test with a significance threshold 0.05. The level of significance was set to p < 0.05. |
PMC9648810 | 36287172 | Prameet Kaur,Agimaa Otgonbaatar,Anupriya Ramamoorthy,Ellora Hui Zhen Chua,Nathan Harmston,Jan Gruber,Nicholas S. Tolwinski | Combining stem cell rejuvenation and senescence targeting to synergistically extend lifespan | 25-10-2022 | aging,stem cells,senescence | Why biological age is a major risk factor for many of the most important human diseases remains mysterious. We know that as organisms age, stem cell pools are exhausted while senescent cells progressively accumulate. Independently, induction of pluripotency via expression of Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) and clearance of senescent cells have each been shown to ameliorate cellular and physiological aspects of aging, suggesting that both processes are drivers of organismal aging. But stem cell exhaustion and cellular senescence likely interact in the etiology and progression of age-dependent diseases because both undermine tissue and organ homeostasis in different if not complementary ways. Here, we combine transient cellular reprogramming (stem cell rejuvenation) with targeted removal of senescent cells to test the hypothesis that simultaneously targeting both cell-fate based aging mechanisms will maximize life and health span benefits. We find that OKSM extends lifespan and show that both interventions protect the intestinal stem cell pool, lower inflammation, activate pro-stem cell signaling pathways, and synergistically improve health and lifespan. Our findings suggest that a combination therapy, simultaneously replacing lost stem cells and removing senescent cells, shows synergistic potential for anti-aging treatments. Our finding that transient expression of both is the most effective suggests that drug-based treatments in non-genetically tractable organisms will likely be the most translatable. | Combining stem cell rejuvenation and senescence targeting to synergistically extend lifespan
Why biological age is a major risk factor for many of the most important human diseases remains mysterious. We know that as organisms age, stem cell pools are exhausted while senescent cells progressively accumulate. Independently, induction of pluripotency via expression of Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) and clearance of senescent cells have each been shown to ameliorate cellular and physiological aspects of aging, suggesting that both processes are drivers of organismal aging. But stem cell exhaustion and cellular senescence likely interact in the etiology and progression of age-dependent diseases because both undermine tissue and organ homeostasis in different if not complementary ways. Here, we combine transient cellular reprogramming (stem cell rejuvenation) with targeted removal of senescent cells to test the hypothesis that simultaneously targeting both cell-fate based aging mechanisms will maximize life and health span benefits. We find that OKSM extends lifespan and show that both interventions protect the intestinal stem cell pool, lower inflammation, activate pro-stem cell signaling pathways, and synergistically improve health and lifespan. Our findings suggest that a combination therapy, simultaneously replacing lost stem cells and removing senescent cells, shows synergistic potential for anti-aging treatments. Our finding that transient expression of both is the most effective suggests that drug-based treatments in non-genetically tractable organisms will likely be the most translatable.
Life is a constant struggle. This is true at cellular and molecular levels where tissue homeostasis requires constant surveillance, repair and replacement of cells damaged or lost due to intrinsic and extrinsic insults [1]. Stem cells play a pivotal role in this tissue homeostasis by providing a reservoir of pluripotent precursor cells, needed to replace fully differentiated cells that are lost or damaged [2]. At the opposite end of the cell-fate spectrum are senescent cells, or cells that have permanently withdrawn from the cell cycle [3]. Cellular senescence can be replicative, where it is triggered by telomere shortening or mediated by stochastic damage, such as oxidative damage to DNA. Senescent cells can also arise as a response to oncogene activation to oppose transformation and cancerous growth [4]. By entering permanent replicative arrest, senescent cells prevent mutations from expanding, thereby providing a sink for genotoxic damage. This senescent state does not simply result in passive replicative arrest but instead leads to transcriptional changes causing resistance to apoptosis and increased secretion of pro-inflammatory signaling molecules, a process known as Senescence Associated Secretory Phenotype (SASP). Senescent cell induced SASP in turn promotes inflammation and contributes to age-dependent dysfunction and to the development of age-related diseases [5]. While the number of stem cells decreases in aging animals, senescent cells accumulate with age [6]. Manipulating cell fates by cellular reprogramming (to rejuvenate somatic cells) and by senolytic interventions (to remove senescent cells) are two promising approaches to restore homeostasis in aged individuals and to prevent age-dependent diseases. Cellular reprogramming allows differentiated cells to regain plasticity and to take on more stem cell-like qualities. A major step towards this goal was the demonstration of cellular reprogramming of terminally differentiated cells into pluripotent embryonic-like stem cell states [7]. Such reprogramming reverses epigenetic aging marks, demonstrating that even mature, terminally differentiated cells can be returned to a younger state [8]. While continuous expression of the Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) in mice led to the formation of teratomas and decreased lifespan [9, 10], repeated short term expression in adult mice succeeded in ameliorating cellular and physiological signs of aging [11–13]. Subsequently, several studies have suggested that this approach can be applied to human aging and age-related disease [14–18], and cycling expression can rejuvenate stem cells in vitro [19]. Ablation of senescent cells has been shown to reverse tissue dysfunction and extend healthspan in mice [20, 21]. A recent study using a senolytic construct (FOXO4-DRI peptide) that induced apoptosis in senescent cells, by interfering with the binding of p53 to FOXO4 thereby freeing p53 to activate apoptosis, showed that the clearing of senescent cells both counteracted senescent cell induced chemotoxicity and restored age-dependent declines in physical performance, fur density, and renal function in aging mice [22]. Several studies have further explored applications of different senolytic strategies to ameliorate age-related decline and disease [6, 23–26]. Accumulation of senescent cells and loss of stem cells are not independent processes. Through SASP, senescent cells release pro-inflammatory cytokines which contribute to chronic inflammation and mTOR activation, ultimately leading to stem cell exhaustion [27]. This interaction suggests that senolytic therapies might interact with cellular reprogramming strategies in delaying age-dependent decline and disease. We have previously explored drug-drug interactions as synergistic aging interventions [28], and here we ask whether a combinatorial treatment of OKSM and senolytic (Sen) expression could mitigate or reverse the effects of aging more efficiently than either intervention alone. To test this hypothesis, we induced expression of OKSM, Sen and an OKSM-Sen combination in adult flies and compared their effects on health and lifespan. We find that each treatment alone had limited benefits, with OKSM alone benefiting maximum lifespan while Sen expression alone increased mean lifespan but had no effect on maximum lifespan. In contrast, animals subjected to the combined intervention experienced substantially longer mean and maximum lifespan. Our data is consistent with a synergistic interaction between the two interventions, simultaneously rejuvenating stem cells and removing senescent cells.
To test the interaction between senolytic removal of senescent cells and cellular reprograming, we designed a model combining these two interventions in an inducible overexpression system in Drosophila. First, we used the four Yamanaka factor based OKSM approach as this had been previously shown to induce pluripotent stem cells in mice [7], humans [29–31] and non-mammalian vertebrate and invertebrate species [32]. To make a senolytic factor for Drosophila, we took advantage of the mouse sequence (FOXO4-DRI [22]) to design an orthologous peptide based on the Drosophila foxo (forkhead box, sub-group O) gene [33]. We then characterized effects of these two interventions independently as well as in combination. We began by looking at the effect of OKSM and Sen on stem cells in an intestinal stem cell (ISC) model [34, 35]. We chose to investigate phenotypic effects specifically in the digestive system of Drosophila (Supplementary Figure 1). As in mammals, the Drosophila gastric lining has a high turnover of cells which is enabled by stem cell pools that replenish the epithelia [34]. Age-dependent loss of stem cells and degradation of barrier function has been shown to contribute to age-dependent functional decline and mortality in Drosophila [36]. The Drosophila gut is composed of four cell types: enterocytes (ECs or absorptive cells), enteroendocrine (EEs or secretory cells), enteroblasts (EBs or transit amplifying cells) and intestinal stem cells (ISCs). ISCs rest on the external surface of the gut epithelium away from the gut lumen, and divide symmetrically to generate more ISCs, or asymmetrically to form EBs [37]. The small, bright green cells or ISCs, can be observed either by expression of the stem cell determinant escargot (esgGal4>UAS-GFP), or by using a marker of Wnt activation, β-catenin (armadillo or arm in Drosophila), observable by GFP construct inserted into the endogenous locus [38]. We looked at the effect of constant expression of the two factors separately or together over a time course of 28 days. We observed a marked increase in ISC numbers starting at day 7 and continuing into day 28 in all three experimental conditions (Figure 1). We observed an increase in ISCs and transit amplifying cells in OKSM expressing epithelia (Figure 1B, 1F, 1J, 1N), an effect likely explained by the presence of Myc and suggesting that stem cell exhaustion may occur [39]. We saw a similar increase in ISCs and transit amplifying cells in Sen expressing epithelia possibly due to the effect of p53 on stem cells [40]. The increase in ISC numbers in animals expressing OKSM was expected, but surprisingly we observed a large increase in ISCs when Sen was expressed (Figure 1C). Overall, the treatments showed higher numbers of stem cells over time as compared to wildtype flies. We next looked at lifespan effects. Continuous expression of OKSM is detrimental in mice while repeated short-term expression was beneficial [13]. We expressed OKSM in ISCs only (Supplementary Figure 2A) or ubiquitously (Figure 4C), both of which led to a significant detriment in lifespan. Recent studies have identified factors involved in senescence and SASP in Drosophila [41–43]. Based on these studies, we used an assay for senescence-associated β-galactosidase (SA-β-gal) to examine whether Sen expression affected the number of senescent cells. We observed a marked absence of SA-β-gal in fly midguts constantly expressing Sen, OKSM and the Sen-OKSM combination at day 40 as compared to both younger and older control flies (Figure 2A–2E and quantified in 2F). The surprising finding was that all three treatments led to the lowering of senescent cells. To look more closely at the effect of Sen and OKSM, we determined the transcriptional profiles of the three conditions as compared to wildtype. We dissected the midguts of flies constantly expressing OKSM, Sen, OKSM and Sen (OKSM-Sen), and control flies (WT) expressing only a fluorescent protein, either in the ubiquitous expression (i.e., under the control an armadillo driver) or the ISC-restricted expression model (i.e., under control of an escargot driver) and performed RNA-seq (Figure 3, Supplementary Figure 3). In the ubiquitous expression model, 1282 genes (FDR < 10%) were identified as significantly differentially expressed (Supplementary Table 1). Clustering of these genes identified seven distinct clusters, each representing groups of genes with similar expression profiles across the four conditions (Figure 3A). Each of the seven clusters was enriched for distinct pathways and processes (Figure 3B, Supplementary Table 1). Cluster I (N = 300) contained genes that were downregulated by Sen but were unaffected in the other conditions. Genes in this cluster were associated with cytokinesis, cell cycle and DNA replication. These would be expected results as p53 release from FoxO should lead to apoptosis rather than cell proliferation. Genes in Cluster II (N = 201) were upregulated in all conditions compared to WT. This cluster was enriched for processes associated with Glutathione and sugar metabolism and lysosomal activity, which are related to tissue building and repair. Cluster III (N = 157) consisted of genes that were upregulated only in the OKSM condition and was enriched for lipid and amino acid metabolism. Cluster IV (N = 173) consisted of genes that were downregulated by all conditions relative to WT and was enriched for genes associated with secretion and phagocytosis. Cluster V (N = 147) represented genes that were downregulated in both OKSM and in the combined OKSM-Sen conditions and contained genes related to translation. Cluster VI (N = 204) contained genes that were upregulated specifically by Sen alone and consisted mainly of genes involved in homeostasis. Cluster VII (N = 100) contained genes upregulated by the OKSM-Sen combination which were involved in Arginine and Proline amino acid metabolism. Overall, our results suggested that genes affected by expression of Sen correlated with lower cell division and FoxO signaling, and a higher level of amino acid metabolism. Expression of OKSM upregulated amino acid and lipid metabolism and proteolysis while downregulating translation and sugar metabolism. In the ISC-restricted expression model, we identified 3791 genes (FDR < 10%) as significantly differentially expressed across conditions (Supplementary Table 2), which were subsequently clustered into seven distinct groups (Figure 3C, 3D, Supplementary Figure 2). Cluster I (N = 991) contained genes that were upregulated in all three conditions relative to WT. Genes in this cluster were associated with cytokinesis, cell cycle, cell migration and DNA replication, further supporting that in ISCs expression of these factors, in any combination, results in increased proliferation and migration of ISCs. Genes in Cluster II (N = 271) were downregulated by the expression of OKSM or Sen. This cluster contained transcription regulators and genes involved in ISC homeostasis. Cluster III (N = 233) consisted of genes that were specifically upregulated by OKSM, and downregulated in the other conditions, and was enriched for fatty acid degradation and peroxisome function. Cluster IV (N = 410) consisted of genes that were upregulated in all conditions, although to a lower extent in Sen, and was enriched for genes involved in vesicle transport and Toll signaling. Cluster V (N = 825) contained genes that were downregulated in all conditions, and contained genes involved in protein translation and signaling pathways. Genes in Cluster VI (N = 642) were downregulated by all of the constructs investigated, albeit not as much by expression of OKSM, and contained genes involved in peroxisome function. Cluster VII (N = 419) contained genes that were upregulated in either Sen or Sen-OKSM and was enriched for genes involved in key signaling pathways and apoptosis. Overall, our transcriptional analysis suggested that gene expression was affected by the expression of Sen or OKSM individually, but that there was not a distinct group that was differentially expressed specifically in response to the combination (Sen-OKSM). Overall, OKSM expression upregulated misfolded protein response and Toll signaling, while downregulating Insulin secretion and protein translation, whereas Sen expression activated Wnt and Hedgehog signaling and downregulated Toll and mTOR pathways. Next, we compared the sets of differentially expressed genes identified in both models to determine if there was also a shared core transcriptional program was altered in both systems. Overall, 812 genes were differentially expressed in both models (Figure 3E, p < 1 × 10−6), with these genes being enriched for DNA replication, regulation of epithelial cell migration, mitosis, inflammation, various metabolic processes and specific developmental signaling pathways (Figure 3F). The majority of these genes have similar responses to transgene expression in either model, while some exhibit differences in their response between models. To evaluate lifespan effects under optimized conditions, we designed two approaches for cycling expression to overcome the continuous expression detriment. We first used a drug induced expression model where the polycistronic OKSM transgene, under the transcriptional control of UAS regulatory sequences, was driven by the Actin-Switch-GAL4 driver activated by RU486 [44]. Flies were placed on fresh food supplemented with the drug weekly leading to periodic, ubiquitous expression. We found that OKSM expression alone resulted in mean lifespan extension in both male and female flies, with female flies showing an increase maximum lifespan as well (Supplementary Figure 2B). The advantage of this system was that flies could be cultured at higher temperatures reducing the overall length of lifespan studies and allowing rapid confirmation of lifespan benefits, however, this system does not allow for more precise control of expression due to drug half-life, consumption, and distribution to all tissues. For this we turned to a temperature sensitive expression system where a ubiquitous GAL4 driver was combined with a ubiquitous, temperature sensitive GAL80 inhibitor. This system allowed us to generate adults with no embryonic expression, and by modulating the temperature of culture, we were able to precisely induce expression in all tissues for defined periods ranging from constant to once per week. For each of these experiments the control cohort expressing a fluorescent protein alone was subjected to the same temperature cycling profile as the experimental strains. Using this approach, we found that continuous expression of OKSM was detrimental (Figure 4C), expression for 24 hours twice per week was mildly beneficial (Figure 4B), and for 12 hours twice per week showed lifespan extension (Figure 4A). We carried out similar optimization experiments for the senolytic peptide (Sen) alone and found that, in terms of median lifespan, continuous expression was also detrimental while expression for either 24 or 12 hours twice per week resulted in significant lifespan extension (Figure 4). Having established conditions under which each individual intervention was beneficial, we then tested whether simultaneous removal of senescent cells (Sen) and cellular reprogramming (OKSM) would result in additive or synergistic benefits in aging flies. The combined intervention again was detrimental when expression of Sen and OKSM was induced continuously, but extended both maximum lifespan and median lifespan when expressed for 24 hours twice per week (Figure 4). Most striking was the significant mean and maximum lifespan extension noted in flies with OKSM and Sen expressed together for 12 hours twice per week (Figure 4A). We reasoned that longer-lived flies should maintain stem cell pools for longer due to stem cell rejuvenation through OKSM expression. To test this hypothesis, we examined the midguts from flies expressing the various transgenes over a time course of 28 days using cycling expression of 12 hours twice per week. As these flies were expressing the factors ubiquitously, we could not use the esg>GFP marker for ISCs and instead used an endogenously GFP-tagged allele of the Wnt responsive β-catenin gene [38]. ISCs show higher levels of cytoplasmic β-catenin protein making them readily identifiable, but in addition other cell types in the epithelium show junctional β-catenin [45]. We visualized ISCs in gastric epithelia of flies with cycling expression of OKSM, Sen or both at four weeks (Figure 5A–5D), eight weeks (Figure 5G–5J), and twelve weeks (Figure 5M–5P). We observed and quantified a higher number of ISCs in flies when OKSM was expressed (Figure 5E, 5F, 5K, 5L, 5Q, 5R). These findings were not consistent with a loss of stem cells in aging organisms, but rather may reflect the loss of stem cell functionality that comes with accumulated damage [46]. Our observations for periodic expression of Sen were consistent with data from mice subjected to senolytic interventions. Sen flies experience a substantial increase in mean but not in maximum lifespan, indicating compression of mortality with excess late deaths compensating for protective effects earlier in life. The same is not true for OKSM flies which experience a statistically significant maximum lifespan extension with both 24 h and 12 h induction. Strikingly, simultaneous application of Sen and OKSM, especially for 12 h induction, result in a mortality trajectory that combines beneficial features of both individual interventions and result in mean and maximum lifespan extension benefit that exceed either. To further investigate this interaction, we performed a quantitative analysis of age-dependent mortality. Biological aging is defined by an exponential increase in mortality rate over time. Mathematically, this is expressed by the Gompertz–Makeham law of mortality [47]: Where A is the initial mortality rate in young animals, B is the age-independent mortality and MRDT is a characteristic time interval over which age-dependent mortality doubles. To quantitatively compare the impact of each single intervention and of the combined intervention on age-dependent mortality, we followed an approach recently described by Axel Kowald and Tom Kirkwood, fitting Gompertz–Makeham survival functions to our experimental survival data [48]. This fit yielded estimates for the initial mortality A and the MRDT parameters of flies (Supplementary Table 3). All fits resulted in a good agreement between experimental data and the Gompertz curve, with a mean residual standard error of 0.03 (3% survival) across all conditions (Figure 6B, 6D, 6F, Supplementary Table 3). Mortality trajectories were then visualized by plotting the logarithm of mortality against age (Figure 6A, 6C, 6E). In this graph the initial mortality A is the intercept of the mortality trajectory at time zero while the slope of the line is proportional to the inverse of the MRDT. Sen-driven lifespan extension showed a substantial decrease in early mortality A, relative to control. However, this decrease was associated with a significant penalty in the form of age-acceleration (decreased MRDT). For the 12 h induction, early mortality decreases almost 100-fold while MRDT decreases from 22.7 days to 7.9 days (p < 0.05). In other words, while initial mortality is substantially lower following Sen treatment, the treated flies age approximately 3 times faster than WT. This pattern was consistent with previously described mouse data and explains why Sen treatment results in mortality compression with increased mean but not maximum lifespan. By contrast, the impact of OKSM induction on initial mortality and MRDT was much smaller. OKSM induction for 24 h and 12 h significantly reduced initial mortality A by 46% and 30%, (p < 0.05), respectively (Supplementary Table 3). While OKSM was also associated with a slight age acceleration penalty in terms of MRDT, this effect was much smaller than for Sen. For 12 h OKSM induction, MRDT only decreased from 22.7 days to 19.6 days (p < 0.05); a 15.8% increase in aging rate. The result of this change can be seen when plotting log mortality as a function of age (Figure 6C, 6E). OKSM mortality was shifted downwards relative to control but ran nearly parallel to control mortality, meaning that age-dependent mortality for 12 h OKSM animals remained lower than for WT at later ages. This pattern explains why OKSM impacted maximum lifespan and was not associated with mortality compression. OKSM-Sen flies with 24 h of induction experienced a significant reduction in the age-acceleration penalty (p < 0.05) relative to 24 h Sen-only flies (Figure 6C, Supplementary Table 3). OKSM-Sen flies with 12 h induction also showed this trend, but the difference was not statistically significant (Figure 6E, Supplementary Table 3). In contrast, the initial mortality A was decreased by over 330-fold in 12 h OKSM-Sen compared to WT. This means that initial mortality rate was significantly lower in OKSM-Sen than even in Sen-only animals (p < 0.05), suggesting that adding OKSM to Sen partially rescued the age-acceleration penalty while further augmenting Sen benefits in terms of early mortality. The resulting survival trajectories consequently show both mean and maximum lifespan extension, with mortality compression occurring only late in life, after approximately day 150 when the mortality of OKSM-Sen crosses that of controls. Mortality compression therefore only becomes apparent after most WT animals have already died. When investigating the interaction between Sen and OKSM treatments, it is useful to compare observed effects to a hypothetical survival and mortality trajectory constructed by assuming that the two interventions act independently (see materials and methods for details). Comparing the OKSM-Sen group to this hypothetical cohort (purple dashed lines in Figure 6) revealed that the actual, observed effects cannot be explained without a direct interaction between Sen and OKSM in terms of aging rate. On their own, both interventions accelerate aging rate (decrease MRDT) but lower early mortality. However, when combined, these interventions result in a reduction, rather than further increase, in the age-acceleration penalty while further augmenting early mortality benefits. This synergistic interaction between OKSM and Sen is the reason why the combined treatment improves both maximum and median lifespan more significantly than either of the two interventions alone. Mechanistically, these data imply a direct interaction between partial reprogramming via OKSM and the Sen-driven senescent cell apoptosis. Indeed, this is what we observed on the cellular level, as expression of Sen impacted the number of stem cells directly (Figure 1) even without OKSM expression.
Here we show that it is possible to extend both the mean and maximum lifespans by combining strategies targeting two different ageing mechanisms related to cell fate. Pulsed expression of the four Yamanaka transcription factors to rejuvenate cells combined with a Senolytic factor kept flies healthier and extended their lives. To the best of our knowledge, this is the first study to show lifespan extension in an otherwise normal animal through the expression of Yamanaka factors. Although not tested in our study, reprogramming leads to a change in DNA methylation and other epigenetic markers leading to a more youthful gene expression signature [16]. The periodic removal of senescent cells leads to fewer chemotoxic molecules being produced and in rejuvenation of organs [22]. Both interventions are rejuvenating in the sense that they reverse cellular tissue composition towards a more youthful state (fewer senescent cells, preserved stem cell pools). The substantial reduction in initial mortality following both interventions is consistent with this mechanism. Here we report that these two interventions are more closely related than previously appreciated. It is important to note that in this study we used the mammalian versions of OKSM and based the Sen fragment on the published mammalian interaction domain. We did not use the Drosophila homologs of OKSM, but rather showed that at least some functionality is conserved from flies to mammals. The same is true for the Sen peptide, which may function through a different mechanism in Drosophila than that proposed for mammals due to the differences in p53 function and FoxO/p53 interactions between the species [49]. Here we focused on the conserved aspects affecting lifespan, rather than the possible differences in mechanism. Senescent cells show persistent activation of the mTOR pathway, a state that promotes secretion of a wide range of signaling molecules, including proinflammatory cytokines [50–52]. As these molecules are secreted, they have the potential to impact neighboring or distant cells increasing the number of senescent cells, impairing tissue homeostasis [53, 54]. In the ubiquitous expression model, we found that expression of the Senolytic peptide led to a decrease in Tgfβ (−0.51 log2 Fold decrease, FDR = 7.51 × 10−2) and the cytokine upd3 (−1.0 log2 Fold decrease, FDR = 2.91 × 10−4) compared to Control (Supplementary Figure 4). Upd3 activates Jak/Stat signaling often related to stem cell activation upon injury leading to asymmetric divisions and lower numbers of stem cells [55, 56]. Tgfβ is upstream of upd3 and involved in promoting senescence [57–59]. SASP-mediated activation of cytokines and mTOR therefore directly link age-dependent accumulation of senescent cells to accelerated loss of stem cells and declining capacity for repair and tissue regeneration. This mechanism suggests a model by which removal of senescent cells would promote increased resilience and improved maintenance of stem cell pools, a phenotype we observe in the fly. The pathways and processes perturbed by expression of OKSM, Sen or Sen-OKSM are associated with those previously identified as been involved in the hallmarks of aging [46]. The genes affected by expression of Sen reflect those that would be expected to be altered by disrupting FOXO: p53 interactions, i.e. apoptosis, whereas those altered by expression of OKSM include genes involved in ISC function and homeostasis. RNA-seq analysis indicates that the extension of lifespan is the result of two largely distinct transcriptional programs, and is not the result of Sen-OKSM specifically activating or repressing a shared transcriptional pathway. A major class of genes affected were in various metabolic pathways. Although we have not investigated this here, the metabolic changes resulting from rejuvenation and senolytic treatments will be interesting to consider in future work. Although OKSM must function through partial reprogramming of cells, the exact mechanism of how this works in adult tissues is not entirely clear [13, 16–18]. We observed changes in Hedgehog signaling recently proposed as a neuroprotective and life extending pathway [60] along with genes associated with cytokinesis and DNA replication. Importantly, we observe that OKSM has limited effect on maximum lifespan unless senescent cells are removed suggesting that SASP counteracts the benefits of rejuvenation. Previous studies have shown either OKSM or Sen to be anti-aging, but in both cases the effects did not affect maximum lifespan. In our combinatorial approach, we can now extend both mean and maximum. We have further established that both approaches can be studied in the easily, genetically manipulatable Drosophila model. We suggest that reprogramming accomplished through gene therapy, or another method combined with senolytic peptides or drugs could promote both tissue repair and reverse age-related decline.
The Oct4-2A-KLF4-2A-Sox2-IRES-Myc DNA fragment containing the human iPS factors was obtained from the OKSIM plasmid (OKSIM was a gift from Jose Cibelli, Addgene plasmid # 24603; http://n2t.net/addgene:24603; RRID: Addgene_24603) [61]. Oct4-2A-KLF4-2A-Sox2 were amplified as one fragment with attB1 and att5r-flanked sites and recombined with the pDONR P1-P5r entry vector (Thermo Fisher Scientific). IRES-Myc was amplified with attB5 and attB2-flanked sites and recombined with the pDONR P5-P2 entry vector. MultiSite Gateway® Pro 2.0 recombination (Thermo Fisher Scientific) was used to recombine the 2 donor plasmids into the pUASg.attB.3XHA (A kind gift from J. Bischof and K. Basler, Zurich) [62] vector to obtain the OKSM gene cassette for expression in Drosophila [63]. The Senolytic (Sen) construct corresponded to amino acid 86 to 131 of the Drosophila Fork Head protein. The Sen construct was synthesized and transferred by Gateway cloning (Thermo Fisher Scientific) into pUASg.attB with C-terminal 3XHA tag (A kind gift from J. Bischof and K. Basler, Zurich) [63].
For Drosophila, the transgenes were injected into attP2 (Strain#8622) P[CaryP]attP2 68A4 by BestGene Inc. (CA, USA) [64]. Expression was driven by Actin-Switch-Gal4 [44], escargot-GAL4 [34, 65] and tubulin-GAL80ts [66]. All additional stocks were obtained from the Bloomington Drosophila Stock Center (NIH P40OD018537).
Actin5C(-FRT)SwitchGAL4: BDSC 9431 [44]; UAS-Td-Tomato: BDSC 36328 (Joost Schulte and Katharine Sepp); esg-Gal4, UAS-GFP; tub-Gal80ts, UAS-dCas9.VPR: BDSC 67069 [34]; armGFSTF MI08675-GFSTF: BDSC 60651 [38, 67, 68]; armGal4; tub-GAL80ts: BDSC 86327 [66]; UAS-OKSM; UAS-Sen (This study). Fly crosses performed were: Act5CGAL4-Switch x w; UAS-OKSM ActGAL4-Switch x w; UAS-TdTomato esg-Gal4, UAS-GFP; tubGal80ts x w; UAS-OKSM esg-Gal4, UAS-GFP; tubGal80ts x w; UAS-Sen esg-Gal4, UAS-GFP; tubGal80ts x w; UAS-Sen; UAS-OKSM esg-Gal4, UAS-GFP; tubGal80ts x w; UAS-TdTomato arm-Gal4, UAS-GFP; tubGal80ts x w; UAS-OKSM arm-Gal4, UAS-GFP; tubGal80ts x w; UAS-Sen arm-Gal4, UAS-GFP; tubGal80ts x w; UAS-Sen; UAS-OKSM arm-Gal4, UAS-GFP; tubGal80ts x w; UAS-TdTomato armGSFTF; arm-Gal4; tubGal80ts x w; UAS-OKSM armGSFTF; arm-Gal4; tubGal80ts x w; UAS-Sen armGSFTF; arm-Gal4; tubGal80ts x w; UAS-Sen; UAS-OKSM armGSFTF; arm-Gal4; tubGal80ts x w; UAS-TdTomato
Drosophila were maintained at standard humidity and temperature (25°C) with food containing 6 g Bacto agar, 114 g glucose, 56 g cornmeal, 25 g Brewer’s yeast and 20 ml of 10% Nipagin in 1L final volume as previously described [69].
Adult fly midguts were dissected in 200 μl of 1× PBS in a PYREX™ Spot Plates concave glass dish (FisherScientific). The midguts were rinsed with PBS and stained with 10 mg/ml Hoechst 33342 diluted 200 times in 1X PBS for 1min. Subsequently, the guts were carefully transferred onto a small droplet of 1× PBS on a 35 mm glass bottom dish. Using fine forceps, the gut was repositioned to resemble its natural orientation. PBS was then removed from the area surrounding the gut, leaving a small amount of excess PBS to hold the gut in place and prevent desiccation. The 3 mm glass bottom dish was then mounted onto the Zeiss LSM800 (Carl Zeiss AG, Germany) for imaging. For each construct, midguts from at least 3 flies were dissected and imaged at the 25% percentile from the anterior midgut [45]. The SA-β-gal activity was visualized using a Senescent Cells Staining Kit (CellEvent™ Senescence Green Detection Kit, C10850, Invitrogen).
Images were acquired on the Zeiss LSM 800 (Carl Zeiss, Germany) using the Plan-Apochromat 63X/1.4 Oil DIC M27 objective, 3% laser power for 488nm and 3% laser power for 405 nm. Images were processed using the ZEN 2014 SP1 software (Carl Zeiss, Germany). Figures were made with Adobe Photoshop and Illustrator. Models were created with https://biorender.com.
For each experiment, more than 50 F1 flies were cultured at 25°C or 18°C. Flies were counted daily noting the number of dead and censored subjects. Lifespans were scored every day. Flies that failed to respond to taps were scored as dead, and those that were stuck to the food were censored. Lifespan curves and statistical analysis of lifespan studies were performed using OASIS 2 (Online Application for Survival Analysis 2 [70]). For studies using Actin-Switch, flies were moved to fresh vials with food supplemented by 200 μM RU486 (mifepristone) weekly. For studies using the temperature sensitive expression inhibitor tubGal80ts flies were raised in a Torrey Pines IN35 programable incubator where the temperature was automatically cycled from 18°C to 25°C twice per week for either 12 or 24 hours.
RNA-seq was aligned against BDGP6.22 (Ensembl version 97) using STAR v2.7.1a [71], and quantified using RSEM v1.3.1 [72]. Reads mapping to genes annotated as rRNA, snoRNA, or snRNA were removed. Genes which had less than 10 reads mapping on average across all samples were also removed. A differential expression analysis was performed using DESeq2 [73]. The likelihood ratio test (LRT) was used to identify any genes that show change in expression across the different conditions. Pairwise comparisons were performed using a Wald test, with independent filtering. To control for false positives due to multiple comparisons in the genome-wide differential expression analysis, the false discovery rate (FDR) was computed using the Benjamini–Hochberg procedure. The gene level counts were transformed using a regularized log transformation, converted to z-scores, and clustered using partitioning around medoids (PAM), using correlation distance as the distance metric. Gene ontology (GO) and KEGG pathway enrichments for each cluster were performed using EnrichR [74–76]. Terms with an FDR < 10% were defined as significantly enriched.
All analysis of lifespan data and curve fitting was performed using the nls non-linear least square tools in the R programming language. Survival data was imported into R and a survival curve derived from Gompertz–Makeham mortality law was fitted according to [47, 48]. Briefly, survival curves are the integral of (Eq. 1), that is: Survival at a given age can be expressed as exp(A*MRDT/ln(2)*(1-exp(ln(2)*age/MRDT)-B*age). The B term captures death of flies due to age-independent causes such as sticking to food or transfer injury. B was fixed empirically to a low estimate of 0.001 or 0.1% of the total cohort per day. The MRDT and A parameters were then fitted to the empirical survival data using the nls library functions in R. Confidence intervals for the A and MRDT and residual standard errors were generated as part of the non-linear fit. For statistical testing, two parameters were considered statistically significantly different if their 95% confidence intervals did not overlap. The hypothetical mortality and survival statistic for the combination treatments were generated by applying fold changes of both individual interventions to MRDT and A parameters for each separate intervention sequentially.
RNA-seq data from this study has been deposited to GEO (GSE201338).
All code necessary to recreate the results from the analysis presented is available from: https://github.com/harmstonlab/OKSM_Senolytic. |
PMC9648812 | 36227739 | Paul Dent,Laurence Booth,Jane L. Roberts,Andrew Poklepovic,Jennifer Martinez,Derek Cridebring,Eric M. Reiman | AR12 increases BAG3 expression which is essential for Tau and APP degradation via LC3-associated phagocytosis and macroautophagy | 13-10-2022 | Alzheimer’s disease,macroautophagy,LAP / LANDO,GRP78,AR12 | We defined the mechanisms by which the chaperone ATPase inhibitor AR12 and the multi-kinase inhibitor neratinib interacted to reduce expression of Tau and amyloid-precursor protein (APP) in microglia and neuronal cells. AR12 and neratinib interacted to increase the phosphorylation of eIF2A S51 and the expression of BAG3, Beclin1 and ATG5, and in parallel, enhanced autophagosome formation and autophagic flux. Knock down of BAG3, Beclin1 or ATG5 abolished autophagosome formation and significantly reduced degradation of p62, LAMP2, Tau, APP, and GRP78 (total and plasma membrane). Knock down of Rubicon, a key component of LC3-associated phagocytosis (LAP), significantly reduced autophagosome formation but not autophagic flux and prevented degradation of Tau, APP, and cell surface GRP78, but not ER-localized GRP78. Knock down of Beclin1, ATG5 or Rubicon or over-expression of GRP78 prevented the significant increase in eIF2A phosphorylation. Knock down of eIF2A prevented the increase in BAG3 expression and significantly reduced autophagosome formation, autophagic flux, and it prevented Tau and APP degradation. We conclude that AR12 has the potential to reduce Tau and APP levels in neurons and microglia via the actions of LAP, endoplasmic reticulum stress signaling and macroautophagy. We hypothesize that the initial inactivation of GRP78 catalytic function by AR12 facilitates an initial increase in eIF2A phosphorylation which in turn is essential for greater levels of eIF2A phosphorylation, greater levels of BAG3 and macroautophagy and eventually leading to significant amounts of APP/Tau degradation. | AR12 increases BAG3 expression which is essential for Tau and APP degradation via LC3-associated phagocytosis and macroautophagy
We defined the mechanisms by which the chaperone ATPase inhibitor AR12 and the multi-kinase inhibitor neratinib interacted to reduce expression of Tau and amyloid-precursor protein (APP) in microglia and neuronal cells. AR12 and neratinib interacted to increase the phosphorylation of eIF2A S51 and the expression of BAG3, Beclin1 and ATG5, and in parallel, enhanced autophagosome formation and autophagic flux. Knock down of BAG3, Beclin1 or ATG5 abolished autophagosome formation and significantly reduced degradation of p62, LAMP2, Tau, APP, and GRP78 (total and plasma membrane). Knock down of Rubicon, a key component of LC3-associated phagocytosis (LAP), significantly reduced autophagosome formation but not autophagic flux and prevented degradation of Tau, APP, and cell surface GRP78, but not ER-localized GRP78. Knock down of Beclin1, ATG5 or Rubicon or over-expression of GRP78 prevented the significant increase in eIF2A phosphorylation. Knock down of eIF2A prevented the increase in BAG3 expression and significantly reduced autophagosome formation, autophagic flux, and it prevented Tau and APP degradation. We conclude that AR12 has the potential to reduce Tau and APP levels in neurons and microglia via the actions of LAP, endoplasmic reticulum stress signaling and macroautophagy. We hypothesize that the initial inactivation of GRP78 catalytic function by AR12 facilitates an initial increase in eIF2A phosphorylation which in turn is essential for greater levels of eIF2A phosphorylation, greater levels of BAG3 and macroautophagy and eventually leading to significant amounts of APP/Tau degradation.
AR12 (OSU-03012) is a derivative of the anti-inflammatory agent celecoxib; unlike the parent compound AR12 lacks COX2 inhibitory activity. Our group demonstrated that AR12 reduced the expression of many chaperone proteins and caused endoplasmic reticulum stress signaling with repression of protein translation, i.e., increased eIF2α S51 phosphorylation, and rapidly caused autophagosome formation, followed later by autolysosome formation, i.e., autophagic flux [1–10]. We demonstrated that the key cellular target for AR12 was the ER stress-regulatory chaperone GRP78 (aka BiP, HSPA5), followed by other ATP-dependent chaperones in the HSP90/HSP70 families. AR12 was shown to directly inhibit chaperone ATPase activities. In vivo studies using AR12 in mice, rats and rabbits has shown drug efficacy without damage to normal tissues [8, 11]. In our recent studies in Alzheimer’s Disease, using wild type and genetically modified HCT116 colon cancer cells as a model system expressing either ATG16L1 T300 or ATG16L1 A300, we determined whether drugs that directly inhibit the chaperone ATPase activity or cause chaperone degradation and endoplasmic reticulum stress signaling leading to macroautophagy could reduce the levels of proteins which play a pathogenic role in neurodegenerative diseases [10]. AR12 and the breast cancer drug neratinib (NER) rapidly reduced expression of Tau, amyloid precursor protein (APP), superoxide dismutase 1 (SOD1) and TAR DNA-binding protein 43 (TDP-43) [10, 12–14]. GRP78 is expressed in the ER and on the outer leaflet of the plasma membrane [15–20]. In the ER it prevents ER stress signaling by PERK, IRE1 and ATF6 and acts as a molecular chaperone renaturing misfolded proteins. GRP78 can chaperone Tau and APP, and can prevent, in an ATP-independent fashion, the processing of APP to insoluble amyloid-β [21]. Cell surface GRP78 plays a key role in permitting neurons and microglia to uptake Tau and APP, i.e., GRP78 is a key player in facilitating the ‘prion’-like bystander behavior of Tau [21]. In that regard, GRP78 has already been shown to modulate prion propagation [19]. At first glance, approaches that would enhance GRP78 expression, rather than inhibiting its ATPase, could be considered as an AD therapeutic approach. This would prevent protein denaturation and processing of Tau and APP into tangles. However, over-expression of GRP78 also blocks both ER stress signaling and the ability of cells to perform macroautophagy and autophagic flux. i.e., over-expression of GRP78 can stabilize Tau and APP, but it does so at the cost of preventing their degradation. Over-expression of GRP78 will also increase the amount of Tau and APP being taken up by bystander neurons and microglia. We know that AR12 and neratinib have overlapping and separate biologies which facilitate autophagosome formation and autophagic flux resulting in the degradation of proteins. We hypothesize that AR12, by inhibiting GRP78, leads to increased ER stress signaling and a reduced capacity to signal into the PI3K pathway, resulting in the inactivation of mTOR, with subsequent autophagosome formation and degradation of Tau and APP. Neratinib through reactive oxygen species, activates ataxia telangiectasia mutated (ATM) which phosphorylates and activates the AMP-dependent protein kinase (AMPK). AMPK, by acting to reduce mTOR activity and by directly activating ULK1, also results in autophagosome formation and degradation of Tau and APP. Furthermore, because neratinib can cause degradation of HER2, which is over-expressed in the brains of AD patients, PI3K/mTOR signaling will be further reduced [12–14, 22, 23]. The present studies were performed to define the biology of AR12 and neratinib in macrophages, microglia, and neuronal cells and whether AR12, alone or combined with neratinib, was competent to cause degradation of APP and Tau proteins in these cell types.
HCT116 colon cancer cells and HCN2 neuronal cells were purchased from the ATCC (Bethesda, MD). BV2 rodent microglial cells and RAW macrophages were supplied by Dr. Martinez. AR12 was purchased from Selleckchem (Houston, TX). Neratinib was supplied by Puma Biotechnology Inc. (Los Angeles, CA). Plasmids to express HSP70, HSP90, LC3-GFP-RFP, Tau-GFP, and amyloid precursor protein (APP)-FLAG were purchased from Addgene (Watertown, MA). The plasmid to express GRP78 was kindly provided by Dr. Amy Lee (University of Southern California, Los Angeles). Trypsin-EDTA, RPMI, penicillin-streptomycin were purchased from GIBCOBRL (GIBCOBRL Life Technologies, Grand Island, NY). Other reagents and performance of experimental procedures were as described [1–14]. Cell Signalling antibodies: ATM (D2E2) Rabbit mAb #2873; Phospho-ATM (Ser1981) (D25E5) Rabbit mAb #13050; AMPKα #2532; Phospho-AMPKα (Thr172) (D4D6D) Rabbit mAb #50081; mTOR #2972; Phospho-mTOR (Ser2448) #2971; Phospho-mTOR (Ser2481) #2974; ULK1 (R600) #4773; Phospho-ULK1 (Ser317) #37762; Phospho-ULK1 (Ser757) #6888; eIF2α #9722; Phospho-eIF2α (Ser51) #9721; PERK (D11A8) Rabbit mAb #5683; Phospho-PERK (Thr980) (16F8) Rabbit mAb #3179; AKT Antibody #9172; Phospho-AKT (Thr308) (244F9) Rabbit mAb #4056; STAT3 (124H6) Mouse mAb #9139; Phospho-STAT3 (Tyr705) Antibody #9131; STAT5 (D2O6Y) Rabbit mAb #94205; Phospho-STAT5 (Tyr694) #9351; Beclin-1 #3738; ATG5 (D5F5U) Rabbit mAb #12994; ATG13 (D4P1K) Rabbit mAb #13273; Phospho-ATG13 (Ser355) (E4D3T) Rabbit mAb #46329; GRP78/BiP #3183; CHOP (L63F7) Mouse mAb #2895 PP1α Antibody #2582; NFκB p65 (L8F6) Mouse mAb #6956; Phospho-NFκB p65 (Ser536) (93H1) Rabbit mAb #3033; Src (36D10) Rabbit mAb #2109; Phospho-Src Family (Tyr416) (E6G4R) Rabbit mAb #59548; Phospho-Src (Tyr527) Antibody #2015; c-MET (25H2) Mouse mAb # 3127; Phospho-MET (Tyr1234/1235) Antibody #3126; FAS (4C3) Mouse mAb #8023; FAS-L (D1N5E) Rabbit mAb #68405; JAK1/2 (6G4) Rabbit mAb #3344; Phospho-Jak1 (Tyr1034/1035)/Jak2 (Tyr1007/1008) (E9Y7V) Mouse mAb #66245; c-KIT (D13A2) XP® Rabbit mAb #3074; Phospho-c-KIT (Tyr719) Antibody #3391; HER/ErbB Family Antibody Sampler Kit #8339; p70 S6 Kinase #9202; Phospho-p70 S6 Kinase (Thr389) #2904; PDGF Receptor beta #3164; Phospho-PDGF Receptor beta (Tyr754) (23B2) Rabbit mAb #2992; Phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) (20G11) Rabbit mAb #4376; Histone Deacetylase (HDAC) Antibody Sampler Kit #9928; HDAC7 (D4E1L) Rabbit mAb #33418; HDAC8 (E7F5K) Rabbit mAb #66042; HDAC11 (D5I8E) Rabbit mAb #58442; MHC Class II (LGII-612.14) Mouse mAb #68258; p38 MAPK #9212; Phospho-p38 MAPK (Thr180/Tyr182) (3D7) Rabbit mAb #9215; LATS1 (C66B5) Rabbit mAb #3477; Phospho-LATS1/2 (Ser909) #9157; Phospho-LATS1/2 (Thr1079) (D57D3) Rabbit mAb #8654; YAP (1A12) Mouse mAb #12395; Phospho-YAP (Ser127) (D9W2I) Rabbit mAb #13008; Phospho-YAP (Ser109) (E5I9G) Rabbit mAb #53749; Phospho-YAP (Ser397) (D1E7Y) Rabbit mAb #13619; TAZ (E8E9G) Rabbit mAb #83669 Phospho-TAZ (Ser89) (E1X9C) Rabbit mAb #59971; NEDD4 Antibody #2740; PTEN Antibody #9552; Estrogen Receptor α (D6R2W) Rabbit mAb #13258; Cyclin Antibody Sampler Kit #9869; BCL-XL #2762; MCL-1 (D35A5) Rabbit mAb #5453; BAX #2772; BAK #2814; BIM #2819; JNK1/2 #9252; Phospho-JNK (Thr183/Tyr185) (81E11) Rabbit mAb #4668; p44/42 MAPK (ERK1/2) (L34F12) Mouse mAb #4696). Santa Cruz Biotechnology antibodies: Histone Deacetylase 9 (HDAC9) (B-1) #sc398003; Histone Deacetylase 10 (HDAC10) (E-2) #393417. ABCAM antibodies: Anti-PD-L1 [28-8] (ab205921); Anti-PD-L2 [EPR25200-50] (ab288298); Anti-Ornithine Decarboxylase/ODC [ODC1 / 2878R] (ab270268); BAG3 ab92309; HSP90 (#2928); HSP90 (ab195575); HSP90 3G3 (13495); GRP78 (ab191023); GRP78 (ab103336); HSP27 [EP1724Y] (ab62339). Specific multiple independent siRNAs to knock down expression were purchased from Qiagen (Hilden, Germany). Human: HSP90 GeneGlobe ID SI03028606; HSP70 GeneGlobe ID SI04324481; GRP78 GeneGlobe ID SI00443114; Beclin-1 GeneGlobe ID SI00055573; ATG5 GeneGlobe ID SI00069251; Rubicon GeneGlobe ID SI00452592; BAG3 GeneGlobe ID SI02632812; AMPKα GeneGlobe ID SI00086387; eIF2α GeneGlobe ID SI00105784; ULK1 GeneGlobe ID SI00053060; perk GeneGlobe ID SI00069048. Mouse: Beclin-1 GeneGlobe ID SI00214165; ATG5 GeneGlobe ID SI00230664; BAG3 GeneGlobe ID SI00208425; AMPKα GeneGlobe ID SI01388247; eIF2α GeneGlobe ID SI00969675; ULK1 GeneGlobe ID SI01461999; PERK GeneGlobe ID SI00991319. Thermo Fisher mouse: HSP70 si RNA ID: s201487 Cat #4390771; GRP78 si RNA ID: s67084 Cat #4390771; Rubicon si RNA ID: s104761 Cat #4390771; HSP90 si RNA ID: s67897 Cat #4390771. Multiple control studies have been previously presented showing on-target specificity of our siRNAs, primary antibodies, and our phospho-specific antibodies to detect both total protein levels and phosphorylated levels of proteins and we present data in HCN2 (human) and BV2 (mouse) cells (Figure 1) [1–14].
Cells were grown at 37° C (5% (v/v CO2) using RPMI supplemented 5% (v/v) fetal calf serum and 1% (v/v) Non-essential amino acids. All therapeutics were dissolved in DMSO making a 10 mM stock solution, stored in multiple 100 μl vials. AR12 and neratinib are diluted in DMSO until the final dilution into growth media (VEH: vehicle control; NER: neratinib). The final concentration of DMSO is never more than 0.1% (v/v). Cells were not cultured in reduced serum media.
Multi-channel fluorescence HCS microscopes perform true in-cell western blotting. Three independent cultures derived from three thawed vials of cells of a tumor were sub-cultured into individual 96-well plates (~5,000 cells per well). Twenty-four hours after plating, the cells are transfected with a control plasmid or a control siRNA, or with an empty vector plasmid or with plasmids to express various proteins. After another 24 hours, the cells are ready for drug exposure(s). At various time-points after the initiation of drug exposure, cells are fixed in place using paraformaldehyde and using Triton X100 for permeabilization. Standard immunofluorescent blocking procedures are employed, followed by incubation of different wells with a variety of validated primary antibodies and subsequently validated fluorescent-tagged secondary antibodies are added to each well. The microscope determines the background fluorescence in the well and in parallel randomly determines the mean fluorescent intensity of 100 cells per well. The counting is independent of cell density. Of note for scientific rigor is that the operator does not personally manipulate the microscope to examine specific cells; the entire fluorescent accrual method is independent of the operator.
Cells were plated and 24h after plating, transfected. Plasmids expressing a specific mRNA or appropriate empty vector control plasmid (CMV) DNA was diluted in 50 μl serum-free and antibiotic-free medium (1 portion for each sample). Concurrently, 2 μl Lipofectamine 2000 (Invitrogen), was diluted into 50 μl of serum-free and antibiotic-free medium (1 portion for each sample). Diluted DNA was added to the diluted Lipofectamine 2000 for each sample and incubated at room temperature for 30 min. This mixture was added to each well / dish of cells containing 100 μl serum-free and antibiotic-free medium for a total volume of 300 μl, and the cells were incubated for 4 h at 37° C. An equal volume of 2x serum containing medium was then added to each well. Cells were incubated for 24h, then treated with drugs.
Cells from a fresh culture growing in log phase as described above, and 24h after plating transfected. Prior to transfection, the medium was aspirated, and serum-free medium was added to each plate. For transfection, 10 nM of the annealed siRNA or the negative control (a “scrambled” sequence with no significant homology to any known gene sequences from mouse, rat or human cell lines) were used. Ten nM siRNA (scrambled or experimental) was diluted in serum-free media. Four μl Hiperfect (Qiagen) was added to this mixture and the solution was mixed by pipetting up and down several times. This solution was incubated at room temp for 10 min, then added dropwise to each dish. The medium in each dish was swirled gently to mix, then incubated at 37° C for 2h. Serum-containing medium was added to each plate, and cells were incubated at 37° C for 24h before then treated with drugs (0-24h).
Autophagy studies made use of a plasmid which produces an LC3-GFP-RFP fusion protein. In autophagosomes, both GFP and RFP fluoresce whereas in the acidic autolysosome only RFP fluoresces. Transfected cells expressing LC3-GFP-RFP, were, as indicated, also transfected with siRNA molecules. After an additional 24h, cells were treated with vehicle control or with the test agents as shown in each graph. Cells were visualized at 60X magnification after 4 h and 8 h of drug exposure. At least fifty randomly selected cells are examined and the mean number of GFP+ RFP+ and RFP+ only punctae per cell determined. Three independent triplicates from separate wells used to calculate the mean number of punctae per cell.
Comparison of the effects of various treatments was using one-way ANOVA for normalcy followed by a two tailed Student’s t-test with multiple comparisons. Differences with a p-value of < 0.05 were considered statistically significant. Experiments are the means of multiple individual data points per experiment from 3 independent experiments (± SD). Data in each Figure has statistical annotation with the actual standard deviation value removed for clarity.
Upon appropriate request, data will be shared with others.
The chaperone GRP78 acts both as a chaperone to renature proteins but also plays a pivotal role in the abilities of cells to sense endoplasmic reticulum (ER) stress [15]. GRP78 is located both in the ER and on the outer leaflet of the plasma membrane. In the ER, GRP78 inhibits PKR-like endoplasmic reticulum kinase (PERK), which phosphorylates and inactivates eIF2α on S51. On the cell surface GRP78 plays roles in stabilizing plasma membrane receptors and more recently was shown to play an essential role as a co-receptor for the virus SARS-CoV-2 [9, 16]. We have previously shown that neratinib, via Rubicon-dependent LC3-associated phagocytosis (LAP), caused the internalization and subsequent macroautophagic degradation of growth factor receptors and RAS proteins [12–14]. In microglia, the uptake and degradation of APP has also been linked to LC3-associated endocytosis (LANDO) [24, 25]. Our present studies were designed to determine whether LAP / LANDO played a mechanistic role in the abilities of AR12 and neratinib to cause Tau, APP, and chaperone degradation in neurons and microglia. In HCN2 human neuronal cells and BV2 murine microglial cells, knock down of the essential LAP regulatory protein Rubicon suppressed autophagosome formation though did not significantly alter autophagic flux, i.e., vesicles that were initially GFP+ RFP+ became over time only RFP+ (Figure 2). Knock down of the macroautophagy proteins Beclin1 or ATG5 also abolished autophagosome formation and autophagic flux (Figure 3) [10]. Based on the data in Figures 2, 3, HCN2 neuronal and BV2 microglial cells were transfected with plasmids to express Tau or APP, and co-transfected with siRNA molecules to knock down the expression of Rubicon, Beclin1 or ATG5. AR12 and neratinib reduced the expression of Tau and APP in HCN2 cells, that was blocked by knock down of Rubicon, Beclin1 or ATG5 (Figure 4A). Knock down of Rubicon, Beclin1 or ATG5 did not significantly alter the basal expression levels of Tau or APP (not shown). Knock down of Rubicon, Beclin1 or ATG5 blocked the degradation of Tau and APP in BV2 microglia (Figure 4B). ompared to the amount of APP expressed from a transfected plasmid (100%), endogenous APP expression in the HCN2 cells was only 5%. For Tau expression, it was 6%. AR12 and neratinib, to a significantly greater extent than observed when expressing Tau or APP from plasmids, reduced endogenous Tau and APP levels in HCN2 cells (Figure 4C). To confirm our Rubicon siRNA data, we made use of RAW macrophages that had been genetically deleted for Rubicon. In wild type RAW macrophages, AR12 and neratinib reduced the expression of chaperones, Tau, and APP, and increased eIF2α S51 phosphorylation (Figure 4D). Deletion of Rubicon in the macrophages abolished the degradation of all tested proteins and the increase in eIF2α S51 phosphorylation (Supplementary Figure 1). We next determined in HCN2 neuronal cells and in BV2 microglia the abilities of AR12 and neratinib to reduce the expression of mutant forms of APP and Tau [26–29]. AR12 and the drug combination reduced the expression of Tau 301L which trended to be less than the reduction of wild type Tau (Figure 5). AR12 and the drug combination was equipotent at reducing the expression of APP, APP715 and APP 692. These findings are important for future in vivo studies as, for example, the Tau P301L mutant is used in transgenic models of Alzheimer’s Disease. We hypothesized that expression of Tau or APP may alter the behavior of intracellular signaling pathways when cells are treated with AR12 and neratinib. HCN2 cells were treated with AR12 and neratinib for 6h, after which alterations in protein expression and protein phosphorylation were determined. As we have observed in other cell types, neratinib reduced both the expression and the phosphorylation of the plasma membrane receptors ERBB1/2/3 (Figure 6). Neither expression of Tau nor expression of APP significantly altered the levels of drug-induced protein degradation or protein phosphorylation when compared to empty vector transfected cells. Notably, compared to tumor cell types we have previously treated with neratinib as a single agent, we observed a profound increase in the phosphorylation of ULK1 S317 and profound reductions in the phosphorylation of ULK1 S757, mTORC1 S2448, mTORC2 S2481 and p70 S6K T389. Increased ULK1 S317 phosphorylation concomitant with lower ULK1 S757 phosphorylation results in a very high level of ULK1 catalytic activity which drives autophagosome formation. Knock down of AMPKα prevented the alterations in protein phosphorylation observed in mTOR and ULK1 (Figure 7A). Expression of an activated mTOR protein suppressed autophagosome formation and the degradation of APP and Tau (not shown). The most surprising data was that AR12 and neratinib combined to not only reduce p70 S6K T389 phosphorylation but also to reduce p70 S6K protein levels. Knock down of the macroautophagy regulatory proteins Beclin1 or ATG5 prevented p70 S6K degradation (Figure 7B). As p70 S6K signaling has been linked to enhanced Tau phosphorylation, we hypothesize that the portions of p70 S6K complexed with Tau were being degraded by macroautophagy in our cells. A key AR12 target are the ATP binding domains of HSP90 and HSP70 family chaperone proteins [3–10]. Both AR12 and neratinib can also reduce the protein levels of chaperone proteins by stimulating autophagy. In HCN2 neuronal and BV2 microglial cells, AR12 reduced the expression of GRP78 (cell surface and total), HSP70 and HSP90 (Figures 8, 9). AR12 and neratinib interacted to further reduce the expression of HSP90 and to inactivate eIF2α. Chaperones are complexed with other proteins including BAG3 (associated with HSP70) and AHA1 and CDC37 (associated with HSP90). BAG3, AHA1 and CDC37 have all been linked to AD pathology [30–36]. BAG3 has been shown to enhance Tau degradation by autophagy [24, 25, 30]. HSP90 and AHA1 promote Tau pathogenesis [31]. And CDC37 with HSP90 also acts to maintain Tau stability [34–36]. AR12 alone as well as the drug combination increased BAG3 expression (Figures 8, 9). The drug combination reduced AHA1 levels but did not alter the expression of CDC37. The histone deacetylase HDAC6 regulates HSP90 activity; increased HSP90 acetylation reduces chaperone function [37]. AR12 and the drug combination reduced HDAC6 expression, which will result in increased HSP90 acetylation concomitant with a further reduction in overall HSP90 chaperoning activity (Figures 8, 9). In neuronal cells and microglia, knock down of Rubicon, Beclin1 or ATG5 prevented the drugs alone or in combination from reducing the expression of chaperone proteins (Figures 10A, 10B). Knock down of Beclin1 or ATG5 prevented AR12 alone or in combination from enhancing eIF2α S51 phosphorylation in microglia whereas knock down of Rubicon did not (Figure 10B) [38–41]. We discovered, however, that knock down of eIF2α significantly reduced autophagosome formation and prevented the degradation of Tau and APP by AR12 and neratinib (Figure 11). This implies eIF2α regulates autophagy, but that autophagy also regulates serine 51 phosphorylation of eIF2α. In agreement with the autophagy data in Figure 11, AR12 and the drug combination reduced expression of p62 and LAMP2 (Figure 12). Thus, we hypothesize that the initial inactivation of GRP78 catalytic function by AR12 facilitates an initial increase in eIF2α phosphorylation which in turn is essential for greater levels of eIF2α phosphorylation, greater levels of macroautophagy and eventually leading to significant amounts of Tau / APP / chaperone protein degradation. Based on our data showing reduced chaperone expression following drug exposure, we next defined which chaperones played the most important roles in regulating APP and Tau stability; in HCN2 and BV2 cells (Figure 13); in RAW macrophages (Supplementary Figure 2). Over-expression of GRP78, HSP70 or HSP90, or knock down of GRP78, HSP70 or HSP90 surprisingly did not significantly alter the basal expression levels of APP and Tau (not shown). This data demonstrates that over-expression of either GRP78, HSP70 or HSP90 prevented the drug-induced degradation of APP. Over-expression of GRP78, but not of HSP70 or HSP90, prevented the drug combination from reducing Tau expression. Knock down of GRP78, but not of HSP70 or HSP90, enhanced the ability of AR12 alone and the drug combination to reduce APP expression. Knock down of GRP78 also further enhanced the ability of AR12 as a single agent to reduce Tau levels. Our GRP78 data is congruent with our earlier findings when knocking down eIF2α expression. Collectively these findings strongly argue that the chaperone GRP78 and translation regulator eIF2α play key roles in regulating the ability of AR12 and neratinib to reduce Tau and APP protein levels. The co-chaperone BAG3 has been shown to facilitate the degradation of Tau and APP [30–32]. Over-expression of GRP78, HSP70 or HSP90, or knock down of GRP78, HSP70 or HSP90 did not alter the basal expression level of BAG3 (not shown). Knock down of GRP78, HSP70 or HSP90 significantly enhanced the ability of AR12 to enhance BAG3 expression (Figure 14A, 14B). Over-expression of GRP78, HSP70 or HSP90 significantly reduced the ability of AR12, alone or in combination with neratinib, from enhancing BAG3 levels (Figure 14A, 14B). Thus, reduced chaperone levels facilitate more drug-induced BAG3 expression. Knock down of BAG3 reduced drug-induced autophagosome formation and autophagic flux (Figure 14C). AR12 and neratinib, as previously observed, profoundly reduced the expression of Tau and APP, and knock down of BAG3 almost abolished the abilities of AR12 and neratinib to cause degradation of Tau (~92%) and APP (~91%) (Figure 14D). Knock down of BAG3 also significantly reduced the abilities of AR12 and neratinib to reduce the expression of GRP78 (total and cell surface) and of HDAC6 (Figure 15A). Notably and in contrast to Tau and APP, a trend of GRP78 degradation was observed even in drug-treated BAG3 knock down cells, with a reduction in degradation of only ~48%. This data suggests that the regulation of APP and Tau expression after AR12 / neratinib exposure, compared to GRP78, is exquisitely dependent upon BAG3 expression. Knock down of BAG3 prevented the degradation of HSP90, p62 and LAMP2 which is congruent with our autophagy data (Figure 15B). AR12 and neratinib activated PERK and significantly increased eIF2α S51 phosphorylation (Figure 15C). Knock down of BAG3 reduced the ability of the drugs to increase PERK and eIF2α phosphorylation. This data suggests that the initial catalytic inhibition of GRP78 by AR12 that facilitates ER stress signaling, which in turn leads to autophagy, and degradation of GRP78, acts in a feed-forward fashion to fully activated ER stress signaling. Finally, we attempted to link cause-and-effect for the actions of AR12 upon the expression of BAG3 and the role of autophagy and ER stress signaling. Knock down of Beclin1, ATG5, ULK1, eIF2α or PERK significantly reduced AR12-induced BAG3 expression (Figure 16). This data further supports the concept that the drugs cause a feed-forward signaling loop via ER stress signaling and autophagy to degrade Tau and APP.
The present studies were performed to determine whether AR12 and neratinib in microglia and neuronal cells caused the degradation of Tau and APP. Our data in a neuronal cell line, a microglial cell line and an established monocyte cell line, were near identical to our prior findings in HCT116 colon cancer cells. Previously we had shown that AR12 and neratinib reduced Tau and APP levels via macro-autophagy and that cells expressing the autophagy protein ATG16L1 T300 were more capable of autophagosome formation and APP / Tau degradation than cells expressing the ATG16L1 A300 isoform [10, 38–42]. In the present studies, knock down of a regulator of LC3-associated phagocytosis / endocytosis, Rubicon, significantly reduced the abilities of the drugs alone or in combination to reduce the expression of chaperone proteins, Tau and APP. LAP cooperated with macroautophagy in facilitating the degradation of Tau and APP (Figure 17) [43]. In our initial studies, we discovered that AR12 significantly increased BAG3 expression. BAG3 has been shown to play a role in the regulation of eIF2α phosphorylation and promote macroautophagy, HDAC6 function, and a transcriptional regulatory in its own right and with heat shock factor 1 (HSF1) [44–48]. The IRE1 ER stress pathway has been proposed to regulate the transcription of BAG3 in part by the regulation of HSF1 [48, 49]. Over-expression of GRP78 prevents both the phosphorylation of eIF2α and also activation of the IRE1 ER stress pathway and our data demonstrated that expression of GRP78 almost abolished the drug-induced expression of BAG3. This data argues we are inducing a coordinated series of cell signals which promote ER stress signaling to increase the expression of BAG3, Beclin1 and ATG5 which collectively facilitate the formation of autophagosomes which sequester chaperones, Tau, and APP, leading to their degradation. GRP78 is localized in the ER where it binds to and inactivates PERK and chaperones newly synthesized proteins and in the outer leaflet of the plasma membrane where it mains stability of receptors and itself can act as a docking protein. AR12 reduced the protein levels of ER- and plasma membrane-localized GRP78 however the mechanisms by which this occurred were overlapping but not identical. The degradation of ER-localized required macroautophagy whereas the reduction in membrane-localized GRP78 required LAP and macroautophagy. The ability of the AR12 and neratinib drug combination to increase eIF2α S51 phosphorylation also reflected this pattern where knock down of Rubicon did not prevent eIF2α inactivation. In AD, the ability of cells to mount an ER stress response and clear denatured proteins is impaired and our findings argue that one way to overcome this issue is the application of AR12 and neratinib which complement each other in promoting ER stress signals and protein degradation. Membrane localized GRP78 is capable of sensing the presence of denatured Tau and APP in the extracellular space liminal to the plasma membrane of neurons and microglia. Extracellular GRP78 chaperones both Tau and APP and extracellular GRP78 is essential for amyloid-β uptake by microglia [50–56]. Previously we noted that cells expressing ATG16L1 T300 expressed 25% higher cell surface levels of GRP78 than cells expressing ATG16L1 A300 [10]. Amyloid-β induces cells to over-express GRP78 and GRP78 is over-expressed in neurons from APP/PS1 mice [57, 58]. n.b. This is the well-described ER stress response that occurs after any perceived overload of denatured protein. The exogenous membrane-associated GRP78, once ingested with the amyloid-β / Tau proteins, was shown to translocate to the ER of the microglia where it acts to block ER stress signaling by PERK, and hence the macroautophagic digestion of denatured Tau and amyloid-β proteins. Thus, neurons and microglia from persons with more surface GRP78 are likely to have an enhanced capability to take up extracellular materials such as Tau and APP which, in contrast to the beneficial effect this has in Crohn’s Disease, in AD is deleterious. And, if Tau is considered to have ‘prion’-like properties, elevated plasma membrane GRP78 levels will result in a greater amount of Tau being propagated / seeded into bystander neurons and microglia. Signaling by ERBB2 and KRAS play important roles in the development and progression of Alzheimer’s Disease [22, 23, 59–64]. Neratinib as a single agent and trending more so when combined with AR12 reduced both ERBB2 and KRAS levels. The robust changes in protein phosphorylation after drug exposure demonstrated that a strong signal was being sent to the cell to form autophagosomes. This agrees with our data showing that AR12 and neratinib interacted to cause autophagosome formation. The degradation and dephosphorylation of p70 S6K has important consequences for a cell over-expressing Tau and APP. Phosphorylation of ribosomal S6 is mediated by p70 S6K, and this enhances ribosomal protein synthesis. Inactivation of mTORC1 and p70 S6K, combined with enhanced phosphorylation of eIF2α S51 will likely completely shut down further synthesis of Tau and APP. In parallel, with enhanced autophagosome formation and autophagic flux, insoluble aggregates of Tau and APP will be cleared, restoring protein homeostasis to the neuron. The co-chaperone BAG3 has been linked to the regulation of autophagy and cell viability, for example, Ji et al. demonstrated that BAG3 facilitates the autophagic degradation of Tau [30–32]. AR12 increased BAG3 expression and knock down of BAG3 reduced the ability of AR12 to cause autophagosome formation. Knock down of BAG3 profoundly reduced the ability of AR12 to reduce the expression of Tau and APP. BAG3 also facilitates the clearance of endogenous Tau in primary neurons and it plays a key role in sensing and regulating protein quality control [65]. Our data demonstrated that knock down of PERK / eIF2α significantly reduced the ability of AR12 to increase BAG3 expression. Furthermore, eIF2α is required to increase the expression of Beclin1 and ATG5 and knock down of eIF2α significantly reduces AR12-induced autophagosome formation. Knock down of Beclin1 / ATG5 / ULK1 also significantly reduced the AR12-induced expression of BAG3. These findings collectively support the hypothesis that AR12 via an initial catalytic inhibition of GRP78 results in an initial wave of ER stress signaling which leads to autophagosome formation. This results in a feed-forward positive loop where GRP78 and other chaperones are digested via autophagy resulting in greater levels of ER stress signaling, a significant increase in BAG3 expression, leading to greater autophagosome formation and the digestion of APP and Tau. Studies beyond the scope of the present manuscript will be required to fully define the relationship between the actions of AR12 and the biology of BAG3. Our initial hypothesis, stated in the Introduction, was oversimplistic. Regulation of GRP78 function by both agents influences the abilities of the drugs to degrade APP and especially Tau. The relevance of BAG3 to the processes of degrading chaperones, APP and Tau was not initially appreciated, and our data highlight the importance of this cochaperone protein. Our prior publication and the data presented in this manuscript strongly suggest that AR12 and neratinib can cause the degradation of Tau and APP in multiple cell types, including microglia and neurons. Future work, based on the availability of funding, will be required to perform in vivo studies in transgenic Tau and APP mice to define whether these drugs have therapeutic efficacy against Alzheimer’s Disease mouse models. Should such studies eventually take place, we hope that this therapeutic combination can be tested for safety and activity in AD patients. |
PMC9648814 | 36279394 | Ying-Hao Han,Xin-Mei He,Seung-Jae Lee,Ying-Ying Mao,Xuan-Chen Liu,Hu-Nan Sun,Mei-Hua Jin,Taeho Kwon | Network analysis for the identification of hub genes and related molecules as potential biomarkers associated with the differentiation of bone marrow-derived stem cells into hepatocytes | 20-10-2022 | hepatocytes,BMSCs,hub genes,miRNA,lncRNA | The incidence of liver diseases has been increasing steadily. However, it has some shortcomings, such as high cost and organ donor scarcity. The application of stem cell research has brought new ideas for the treatment of liver diseases. Therefore, it is particularly important to clarify the molecular and regulatory mechanisms of differentiation of bone marrow-derived stem cells (BMSCs) into liver cells. Herein, we screened differentially expressed genes between hepatocytes and untreated BMSCs to identify the genes responsible for the differentiation of BMSCs into hepatocytes. GSE30419 gene microarray data of BMSCs and GSE72088 gene microarray data of primary hepatocytes were obtained from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1896 genes were upregulated and 2506 were downregulated in hepatocytes as compared with BMSCs. Hub genes were analyzed using the STRING and Cytoscape v 3.8.2, revealing that twenty-four hub genes, play a pivotal role in the differentiation of BMSCs into hepatocytes. The expression of the hub genes in the BMSCs and hepatocytes was verified by reverse transcription-quantitative PCR (RT-qPCR). Next, the target miRNAs of hub genes were predicted, and then the lncRNAs regulating miRNAs was discovered, thus forming the lncRNA-miRNA-mRNA interaction chain. The results indicate that the lncRNA-miRNA-mRNA interaction chain may play an important role in the differentiation of BMSCs into hepatocytes, which provides a new therapeutic target for liver disease treatment. | Network analysis for the identification of hub genes and related molecules as potential biomarkers associated with the differentiation of bone marrow-derived stem cells into hepatocytes
The incidence of liver diseases has been increasing steadily. However, it has some shortcomings, such as high cost and organ donor scarcity. The application of stem cell research has brought new ideas for the treatment of liver diseases. Therefore, it is particularly important to clarify the molecular and regulatory mechanisms of differentiation of bone marrow-derived stem cells (BMSCs) into liver cells. Herein, we screened differentially expressed genes between hepatocytes and untreated BMSCs to identify the genes responsible for the differentiation of BMSCs into hepatocytes. GSE30419 gene microarray data of BMSCs and GSE72088 gene microarray data of primary hepatocytes were obtained from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1896 genes were upregulated and 2506 were downregulated in hepatocytes as compared with BMSCs. Hub genes were analyzed using the STRING and Cytoscape v 3.8.2, revealing that twenty-four hub genes, play a pivotal role in the differentiation of BMSCs into hepatocytes. The expression of the hub genes in the BMSCs and hepatocytes was verified by reverse transcription-quantitative PCR (RT-qPCR). Next, the target miRNAs of hub genes were predicted, and then the lncRNAs regulating miRNAs was discovered, thus forming the lncRNA-miRNA-mRNA interaction chain. The results indicate that the lncRNA-miRNA-mRNA interaction chain may play an important role in the differentiation of BMSCs into hepatocytes, which provides a new therapeutic target for liver disease treatment.
A liver transplant is a significant way to treat patients with severe liver damage, such as decompensated cirrhosis, liver failure, and advanced liver cancer [1]. However, there is a scarcity of liver donors, and transplantation is associated with immune rejection and other problems [2]. Over the past few decades, in addition to advances in biological treatment research, molecular biology, cell bioengineering, and the stem cell research, stem cell therapy has emerged as an economic and feasible liver disease treatment for the end-stage liver disease [3], particularly decompensated cirrhosis liver failure and advanced liver cancer, and offers an effective strategy with no limit of supply and demand [3]. Stem cell therapy has broad application prospects in liver disease [4]. Bone marrow-derived stem cells (BMSCs) are widely used as adult stem cells, which originate from the mesoderm [5, 6]. Several experiments have demonstrated that BMSCs can differentiate into the cells of the mesoderm lineage, such as osteoblasts [7], adipocytes [8], muscle cells [9], neurons and brain cells [10], cardiomyocytes [11], and hepatocytes [12]. Growing evidence suggests that BMSCs can differentiate into hepatocytes, presenting interesting possibilities for cellular therapy of liver diseases. Previous reports have shown that decreased Wnt signaling contributes to the differentiation of BMSCs into hepatocytes [13]. Another study demonstrated the ability of BMSCs to differentiate into liver cells [14]. In addition, Kang et al. reported that rat BMSCs differentiate into hepatocytes [15]. It has been reported that cytokines, such as HGF and bFGF, are key contributing factors in promoting cell differentiation. Under certain conditions, HGF and bFGF can promote BMSCs differentiation into liver cells for the treatment of advanced liver disease [16, 17]. Owing to the emergence and development of RNA-sequencing technology, a large number of miRNA, lncRNA, and circRNA have been discovered and utilized [18]. LncRNA and miRNA are the two most important types of ncRNA. MiRNAs exhibit post-transcriptional inhibitory effects in animals and plants by pairing with target mRNAs [19]. LncRNA is a class of ncRNAs that does not encode proteins and whose transcripts are longer than 200 nt [20]. LncRNAs are speculated to regulate protein-coding genes in several ways. Studies have found that lncRNA regulates miRNA in three ways: (1) as a precursor or host of miRNAs; (2) lncRNAs and miRNAs compete for mRNA binding; and (3) LncRNA acts as a molecular sponge by absorbing miRNA, thereby regulating gene transcription and expression. Furthermore, miRNA and lncRNA play a major role in various life activities, and in the occurrence and development of liver disease. Most importantly, these are potential therapeutic targets and diagnostic biomarkers. At present, the mechanism underlying the differentiation of BMSCs into hepatocytes is unclear. Further investigation of miRNA and lncRNA can advance the research of the differential genes. Therefore, we first analyzed the hub genes in the differentiation process of BMSCs into hepatocytes, and further identified miRNAs and lncRNAs, namely, miR-186, miR-703, miR-466k, miR-23a, miR-692, miR-466l, miR-137, miR-383, miR-466d-5p, miR-23b, miR-539, Zfp469, 1700020I14Rik, Gm42418, Zfas1, Dubr, and Peg13 as potential biomolecules. The chip data for this study was obtained from a comprehensive gene expression database, and the differential genes were analyzed using bioinformatics software. Different genes were screened to obtain the hub genes that control the differentiation of BMSCs into hepatocytes and liver development. These hub genes include Cat, Cyp2e1, Pah, Ugt2a3, Acss2, Aldh6a1, Hmgcs2, H6pd, Aldh1a7, Hmgcl, Ugt1a1, Arg1, Otc, Baat, Slco1b2, Onecut1, Hhex, Proc, Cdk4, Il6, Fn1, Erbb2, Ccnd1, and Bmp4. In summary, this study provides potential therapeutic targets for the treatment of liver diseases.
Figure 1A shows differences in the data, indicating unstandardized data, while Figure 1B shows roughly well-standardized data. A total of 4402 differential genes were detected in hepatocytes, with 1896 upregulated and 2506 downregulated genes compared with BMSCs.
The principal-component analysis (PCA) was utilized to obtain information about the overall composition of the analyzed complex microarray datasets. The three samples in the hepatocyte sample group were closely distributed, indicating their high mutual similarity. The BMSCs group exhibited high repeatability. However, the dispersion between the hepatocyte sample and BMSCs groups was very high and well distinguishable, with distinct differences between them, as shown in Figure 2.
The abscissa represents the sample data, which can be divided into two categories: BMSCs and hepatocytes. In addition, the expression of genes in the BMSCs and hepatocytes is shown in Figure 3A. The log2-fold change difference and the negative logarithm of p values between the volcano map samples of DEGs in the BMSC and hepatocyte samples are indicated on the X and Y axes, respectively, each point representing a single gene with detectable expression in both samples. The down-regulated and up-regulated genes were indicated by blue and red, respectively, and insignificant genes were indicated by gray dots. As compared to BMSCs, 1896 and 2506 genes were upregulated and downregulated, respectively, in hepatocytes (Figure 3B).
All DEGs were analyzed by DAVID software (https://david.ncifcrf.gov/tools.jsp). GO enrichment analysis showed that the following GO terms were included for upregulated genes: oxidation-reduction process (GO:0055114), lipid metabolic process (GO:0006629), metabolic process (GO:0008152), and liver development(GO:0001889) (Figure 4A), and the following for downregulated genes: regulation of cell cycle (GO:0007049), cell division (GO:0051301), multicellular organism development (GO:0007275), and positive regulation of cell proliferation (GO:0008284) (Figure 4B).
To screen out the hub genes during the differentiation of BMSCs into hepatocytes, up-regulated genes in hepatocyte oxidation-reduction process, hepatic metabolism process and liver development, and down-regulated genes in positive regulation of cell proliferation were selected because they are closely related to hepatocyte differentiation. The PPI network was constructed with four GO genes (Table 1) and STRING database (https://www.string-db.org/). The DEGs contained in the four GO terms were respectively uploaded to STRING to construct a PPI network. The data exported from the STRING was screened for the hub genes. Two genes were valuable in the oxidation-reduction process of hepatocytes: Cat and Cyp2e1. Seven genes were shown to be hub genes within the metabolic process: Pah, Ugt2a3, Acss2, Aldh6a1, Hmgcs2, H6pd, and Aldh1a7. Nine genes play a critical role in liver development: namely Hmgcl, Ugt1a1, Arg1, Otc, Baat, Slco1b2, Onecut1, Hhex, and Proc; and six genes in suppressing liver differentiation: Cdk4, Il6, Fn1, Erbb2, Ccnd1 and Bmp4 (Table 2). Cytoscape v.3.8.2 software act as a pathway for PPI network mapping and analysis. Degree and betweenness centrality (BC) value represents the numbers of interactions of a particular protein and represents nodes passing the node in the shortest distance, respectively. In the final network shown in Figure 5, genes with larger nodes and darker colors are more essential in the PPI network.
To investigate key molecular targets regulating differentiation of BMSCs into hepatocytes, four GO terms (Table 1) related to liver development and differentiation were selected. Twenty-four DEGs were identified as hub genes in the differentiation of BMSCs into hepatocytes. Then, twenty-four hub genes were verified by RT-qPCR. Twenty-three hub genes, namely Cat, Cyp2e1, Pah, Ugt2a3, Acss2, Aldh6a1, Hmgcs2, H6pd, Aldh1a7, Hmgcl, Ugt1a1, Arg1, Otc, Baat, Slco1b2, Onecut1, Hhex, Proc, Cdk4, Fn1, Erbb2, Ccnd1 and Bmp4 were consistent with RNA sequencing results, while IL6 gene was contrary to RNA sequencing results. All the 24 hub genes were differentially expressed. Together, these results suggested that twenty-four hub genes are important to regulate differentiation of BMSCs into hepatocytes (Figure 6).
We predicted the miRNAs corresponding to the twenty-four hub genes in miRWalk (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk/predictedmirnagene.html). The screening criteria included the miRNA binding site should be in the 3'-UTR, the minimum nucleotide length of miRNA should be 7-mer, and P <0.05. Five prediction programs, miRanda, miRDB, miRWalk, RNA22, and TargetScan, were selected, and the intersection of their predictions was the final prediction result [21]. The genes and MiRNAs interaction network are shown in Figure 7. MiRNAs targeting at least two genes were counted (Table 3).
In StarBase (https://starbase.sysu.edu.cn/), we predicted the lncRNAs corresponding to miR-692, miR-466l, miR-703, miR-137, miR-186, miR-383, miR-539, miR-23a, miR-466d-5p, miR-23b, and miR-466k. Very high stringency (≥3) was used as the prediction criterion. Only six miRNAs, namely miR-23a-3p, miR-23b-3p, miR-137-3p, miR-186-5p, miR-466l-3p, and miR-539-5p, met the criteria. Moreover, Zfp469 was observed to target two key miRNAs (Figure 8 and Table 4).
Liver disease is a serious hazard to human health. Ideal clinical intervention drugs and methods have not been identified yet in any part of the world. The liver transplantation is a conventional and effective intervention method. However, transplantation cannot meet the clinical needs due to the shortage of high-quality liver cells, allograft rejection, and other problems [22]. Therefore, there is an urgent need to develop new methods for liver disease intervention. Currently, stem cell-based cell replacement therapy has attracted worldwide attention. BMSC transplantation provides a new way to intervene liver disease. Several studies have shown that transplanted BMSCs can differentiate into hepatocytes to replace the function of damaged hepatocytes and tissues in liver due to their directional differentiation ability, promoting the recovery of liver injury. However, the mechanism of differentiation of BMSCs into hepatocytes remains unclear. The study of key genes and downstream regulatory mechanisms associated with the differentiation of BMSCs into liver cells has a profound clinical impact, which can provide potential targets for BMSC-based treatment of liver failure. We uploaded chip data of BMSCs and hepatocytes to Transcriptome Analysis Console software for DEGs Analysis. Total 4402 DEGs were detected in hepatocytes, with 1896 upregulated and 2506 downregulated genes compared with BMSCs. GO analysis was performed on all the DEGs, and the results were as follows: among the upregulated genes, 11.44% were related to oxidation-reduction process, 7.16% to liver metabolism process, and 1.92% were to liver development process. Among the downregulated genes, 5.04% were related to cell proliferation. Oxidation-reduction process, metabolism process, liver development process, and cell proliferation are closely related to differentiation of hepatocytes. The STRING search tool was used to build PPI network, and the hub genes were identified by analyzing the network. Among the hub genes, Cat and Cyp2e1 are related to the oxidation-reduction process of hepatocytes; Cat and FOXO3 are positively correlated with the differentiation of BMSCs into cells of the osteogenic lineage [23]. In the later stage of stem cells differentiation into hepatocytes, Cyp2e1 is expressed [24]. The hub genes Pah, Ugt2a3, Acss2, Aldh6a1, Hmgcs2, H6pd, and Aldh1a7 are related to liver metabolism. The mRNA level of Hmgcs2 increase during the differentiation process of embryonic stem cells to hepatocellular-like cells [25]. The role of Pah, Ugt2a3, Acss2, Aldh6a1, H6pd, or Aldh1a7 in hepatocyte cells is currently unclear. The hub genes Hmgcl, Ugt1a1, Arg1, Otc, Baat, Slco1b2, Onecut1, Hhex, and Proc are related to liver development. Ugt1a1 is a marker of hepatocytes, and its significant expression during the differentiation of human hematopoietic stem cells into hepatocytes indicates that HSCs have successfully differentiated into normal hepatocytes [26]. Arg1 expression is observed during hepatic-like phenotype differentiation of somatic stem cells in vitro [27]. In a previous study, increased Otc expression was reported in 201B7 cells after culture in hepatocyte differentiation initiation medium [28]. Onecut interacts with Lmx1a to promote the differentiation of ventral midbrain neural stem cells into dopamine neurons through the Wnt1-Lmx1a pathway [29]. Hhex regulates the hepatic differentiation of embryonic stem cells [30]. The role of Hmgcl, Baat, Slco1b2, or Proc in hepatocyte differentiation and development is unclear. The hub genes Cdk4, Il6, Fn1, Erbb2, Ccnd1, and Bmp4 regulate cell proliferation networks and are downregulated in hepatocytes compared with untreated BMSCs, suggesting inhibitory effects on liver differentiation. Studies have shown that expression of Cyclin B1 and Cdk4 during the hepatic differentiation of liver epithelial progenitor cells (LEPCs) induced by sodium butyrate may be related to the growth arrest of LEPCs shortly after treatment [31]. MSCs contribute to liver differentiation by activating the IL-6 / gp130-mediated STAT3 signaling pathway [32]. Ccnd1 has been reported to be associated with liver regeneration, and it is speculated that they play a key role in mouse hepatocytes [33]. Ccnd1 silencing suppresses liver cancer stem cells (LCSCs) differentiation [34]. Bmp4 is an important regulator of cell proliferation and differentiation. Studies have shown that Bmp4 is a key cytokine for the development of mouse embryonic stem cells into hepatocytes [35]. Fn1 and Erbb2 have not been reported in this respect. Furthermore, we verified twenty-four hub genes in the BMSCs and hepatocytes using RT-qPCR. All the twenty-four hub genes were differentially expressed. We used miRWalk to predict upstream target miRNAs that regulate gene expression. Eleven miRNAs were predicted to target at least two hub genes each. Previous studies have reported that lncRNAs, located upstream of miRNAs, are not negligible in BMSCs differentiation; therefore, we used StarBase 2.0 for prediction. However, only miRNA-23a, miRNA-23b, miR-137, miRNA-186, miRNA-466l, and miRNA-539 were predicted to obtain target lncRNAs. Six lncRNAs, Zfp469, 1700020I14Rik, Gm42418, Zfas1, Dubr, and Peg13 were predicted by StarBase v2.0. Therefore, we introduced the above six miRNAs and their predicted lncRNAs in the regulation of BMSC differentiation. Studies have shown that down-regulation of miR-23a can promote osteogenic differentiation of BMSCs [36]. Mir-23b-3p can affect the hepatic trans-differentiation of MSCs [37]. Another study showed that miR-23b-3p can impact the differentiation of BMSCs [38], as do Mir-137-3p [39]. Current research shows that the relationship between miRNA-186-5p, miRNA-466l-3p, miRNA-539-5p, and BMSCs differentiation is not very clear. Among the 6 lncRNAs, only Zfas1 was found to be associated with BMSC differentiation, while the relationship between Zfp469, 1700020I14Rik, Gm42418, Dubr, Peg13, and BMSC differentiation remains unclear. Zfas1 has been reported to affect BMSC differentiation by sponging miR-499. However, further research is needed to confirm these results [40]. Taken together, through bioinformatics analysis, we identified key genes that regulate the differentiation of BMSCs into hepatocytes and their upstream miRNAs and lncRNAs, providing potential targets for stem-cell replacement therapy for liver diseases. Hence, exploring the key genes and downstream regulation mechanism of the differentiation of BMSCs into hepatocytes is essential for treating hepatic diseases.
In this experiment, two-step perfusion method was used to isolate and acquire mouse primary hepatocytes. First, the liver was flushed with Ca2+-free HEPES, followed by HEPES perfusion containing collagenase and Ca2+, and then the cell suspension was cultured in DMEM/Ham’s F12 medium. The primary hepatocytes were established in complete DMEM/Ham’s F12 media placed in a 95% air/5% CO2, 37° C incubator [41]. The soft tissue on the surface of the mouse femur was carefully peeled off and the femur tissue was rinsed several times with a 1 mL syringe containing α-MEM culture solution, until it was transparent. Four rinsed femur cells were cultured in a medium containing BMSCs growth medium placed in a 95% air/5% CO2, 37° C incubator, and refreshed every three days [42–44].
Gene Expression Omnibus (GEO) database provides gene expression profiles [45]. We downloaded two gene expression profiles of GSE30419 and GSE72088 from the GEO database datasets. GSE30419 included three untreated Mus musculus BMSC samples (GSM795638, GSM795639, and GSM795640), and dataset GSE72088 included three PBS treated Mus musculus primary hepatocytes samples (GSM1375704, GSM1375705, and GSM1375706).
The genes expression profiles of GSE30419 and GSE72088 were analyzed by Transcriptome Analysis Console software. | Fold Change | >4, P <0.05 and FDR <0.05 were used as the standard for analysis and screening of DEGs.
To investigate the functions of these gene signatures, we performed GO enrichment analysis. The DAVID website has multiple functional enrichment for DEGs [46], and the screening criteria for GO functional enrichment is usually set as P <0.05 and FDR <0.05 [47]. The results were presented as bar graphs by Graph Pad Prism 6.0.
The PPI network of differential genes was constructed using the STRING online platform and analyzed and processed by Cytoscape V.3.8.2 software [48]. The means of the degrees were calculated to screen out genes that showed a greater degree than the mean value. The hub genes with BC value greater than 0.05 and degree exceeds the mean were selected.
Total RNA was extracted from hepatocytes and BMSCs using TRIzol reagent (Sigma, St. Louis, MO, USA). We used the cDNA Synthesis Mix Kit (Innovagene, Hunan, China) to generate the first strand cDNA. Real-time PCR was implemented using SYBR Green qPCR Mix (Innovagene). Using the 2−ΔΔCq method, we calculated and analyzed the expression situations of hub genes.
We used the miRWalk website to predict target miRNAs located upstream to the genes [49]. Five databases including miRanda, miRDB, miRWalk, RNA22, and TargetScan were selected for prediction. The interaction between genes and miRNAs was visualized by Cytoscape V 3.8.2. MiRNAs targeting at least two genes were screened.
Interaction between miRNAs and lncRNAs was identified using StarBase website [50], and Cytoscape V 3.8.2 was used for analysis and mapping. LncRNAs targeting multiple miRNAs are required to be carefully analyzed.
Statistical analysis of the data was performed in IBM SPSS Statistics Version 26. The data based on RT-qPCR experiment was expressed as mean ± SD and presented using Graph Pad Prism 6.0. All the genes were tested in three separate experiments. Analysis was done using student’s unpaired t-tests. P<0.05 was statistically significant. |
PMC9648818 | Xuhong Zhang,Changzhi Han,Yuqin Liang,Yang Yang,Yun Liu,Yanpo Cao | Combined full-length transcriptomic and metabolomic analysis reveals the regulatory mechanisms of adaptation to salt stress in asparagus | 27-10-2022 | salt stress,ion transport,metabolic adjustment,salinity tolerance,asparagus | Soil salinity is a very serious abiotic stressor that affects plant growth and threatens crop yield. Thus, it is important to explore the mechanisms of salt tolerance of plant and then to stabilize and improve crop yield. Asparagus is an important cash crop, but its salt tolerance mechanisms are largely unknown. Full-length transcriptomic and metabolomic analyses were performed on two asparagus genotypes: ‘jx1502’ (a salt-tolerant genotype) and ‘gold crown’ (a salt-sensitive genotype). Compared with the distilled water treatment (control), 877 and 1610 differentially expressed genes (DEGs) were identified in ‘jx1502’ and ‘gold crown’ under salt stress treatment, respectively, and 135 and 73 differentially accumulated metabolites (DAMs) were identified in ‘jx1502’ and ‘gold crown’ under salt stress treatment, respectively. DEGs related to ion transport, plant hormone response, and cell division and growth presented differential expression profiles between ‘jx1502’ and ‘gold crown.’ In ‘jx1502,’ 11 ion transport-related DEGs, 8 plant hormone response-related DEGs, and 12 cell division and growth-related DEGs were upregulated, while 7 ion transport-related DEGs, 4 plant hormone response-related DEGs, and 2 cell division and growth-related DEGs were downregulated. Interestingly, in ‘gold crown,’ 14 ion transport-related DEGs, 2 plant hormone response-related DEGs, and 6 cell division and growth-related DEGs were upregulated, while 45 ion transport-related DEGs, 13 plant hormone response-related DEGs, and 16 cell division and growth-related DEGs were downregulated. Genotype ‘jx1502’ can modulate K+/Na+ and water homeostasis and maintain a more constant transport system for nutrient uptake and distribution than ‘gold crown’ under salt stress. Genotype ‘jx1502’ strengthened the response to auxin (IAA), as well as cell division and growth for root remodeling and thus salt tolerance. Therefore, the integration analysis of transcriptomic and metabolomic indicated that ‘jx1502’ enhanced sugar and amino acid metabolism for energy supply and osmotic regulatory substance accumulation to meet the demands of protective mechanisms against salt stress. This work contributed to reveal the underlying salt tolerance mechanism of asparagus at transcription and metabolism level and proposed new directions for asparagus variety improvement. | Combined full-length transcriptomic and metabolomic analysis reveals the regulatory mechanisms of adaptation to salt stress in asparagus
Soil salinity is a very serious abiotic stressor that affects plant growth and threatens crop yield. Thus, it is important to explore the mechanisms of salt tolerance of plant and then to stabilize and improve crop yield. Asparagus is an important cash crop, but its salt tolerance mechanisms are largely unknown. Full-length transcriptomic and metabolomic analyses were performed on two asparagus genotypes: ‘jx1502’ (a salt-tolerant genotype) and ‘gold crown’ (a salt-sensitive genotype). Compared with the distilled water treatment (control), 877 and 1610 differentially expressed genes (DEGs) were identified in ‘jx1502’ and ‘gold crown’ under salt stress treatment, respectively, and 135 and 73 differentially accumulated metabolites (DAMs) were identified in ‘jx1502’ and ‘gold crown’ under salt stress treatment, respectively. DEGs related to ion transport, plant hormone response, and cell division and growth presented differential expression profiles between ‘jx1502’ and ‘gold crown.’ In ‘jx1502,’ 11 ion transport-related DEGs, 8 plant hormone response-related DEGs, and 12 cell division and growth-related DEGs were upregulated, while 7 ion transport-related DEGs, 4 plant hormone response-related DEGs, and 2 cell division and growth-related DEGs were downregulated. Interestingly, in ‘gold crown,’ 14 ion transport-related DEGs, 2 plant hormone response-related DEGs, and 6 cell division and growth-related DEGs were upregulated, while 45 ion transport-related DEGs, 13 plant hormone response-related DEGs, and 16 cell division and growth-related DEGs were downregulated. Genotype ‘jx1502’ can modulate K+/Na+ and water homeostasis and maintain a more constant transport system for nutrient uptake and distribution than ‘gold crown’ under salt stress. Genotype ‘jx1502’ strengthened the response to auxin (IAA), as well as cell division and growth for root remodeling and thus salt tolerance. Therefore, the integration analysis of transcriptomic and metabolomic indicated that ‘jx1502’ enhanced sugar and amino acid metabolism for energy supply and osmotic regulatory substance accumulation to meet the demands of protective mechanisms against salt stress. This work contributed to reveal the underlying salt tolerance mechanism of asparagus at transcription and metabolism level and proposed new directions for asparagus variety improvement.
Soil salinity as one of the most serious abiotic stressors can impact greatly on plant life processes and pose an enormous threat to crop production (Rengasamy, 2010; Duarte et al., 2013; Munns and Gilliham, 2015). To improve the utilization of saline land in the world, the cultivation of plants with tolerance to salt may be a very effective strategy. So it is particularly important to carry out researches on the identification of salt-tolerant plants and the exploration on underlying tolerance mechanisms for further plant variety improvement (Li et al., 2022). High sodium concentrations in the soil limit water uptake and nutrient absorption (Hussain et al., 2021) and cause ionic toxicity, osmotic stress, and oxidative stress (Zhang et al., 2019a). To reduce the possible threats of above stresses, most of plants have developed mature mechanisms to response to external signals (Hasegawa et al., 2000), which may involve every step in the signaling pathways, such as Ca2+ and plant hormone pathways, and subsequent adaptive responses, including the regulation of ion balance, osmotic pressure, and plant development and growth (Van Zelm et al., 2020; Zhang et al., 2021). After the initial perception of excessive Na+, the Ca2+ signal and plant hormones, as important intracellular secondary messengers, are usually triggered. In calcium signaling pathway, some kinases participate in decoding Ca2+ signals and thus cause a variety of cellular responses to sodium, which involving calcium-dependent protein kinases (CDPKs), calcineurin B-like proteins (CBLs), and CBL-interacting protein kinases (CIPKs) (Manishankar et al., 2018; Amirbakhtiar et al., 2019; Van Zelm et al., 2020). Plant hormones are important endogenous signals for regulating plant growth and development under both normal and stressed conditions (Van Zelm et al., 2020). Importantly, roots, as the frontline tissue exposed to soil salinity, need to maintain the growth and the absorption of water and nutrients for adapting the stressed environment (Ouyang et al., 2007). The developmental modifications to root system architecture is vital for tolerance to high soil salinity and it strongly depends on auxin (IAA) signaling (He et al., 2018; Korver et al., 2018). Recent evidence suggested important contributions of IAA metabolism and transport on eventual IAA accumulation and signaling patterns (Korver et al., 2018). It was reported that the Group II members of the GRETCHEN HAGEN 3 (GH3) gene family mediate IAA metabolic inactivation through catalysing the conjugation of IAA to amino acids (Staswick et al., 2002; Staswick et al., 2005), the gh3oct mutant plants, in which the entire group II GH3s were knocked out, showed a more branched root system and tolerance to salinity (Casanova-Sáez et al., 2022). Additionally, the long distance transport of IAA from shoot to root and its movement among local cells are necessary for root growth as well as adaption to external environments (Korver et al., 2018; Van Zelm et al., 2020). In these processes, ATP binding cassette B/P-glycoprotein/Multidrug-resistance (ABCB/PGP/MDR), auxin resistant 1/like aux 1 (AUX/LAX), and pin-formed (PIN) contribute to IAA distribution to mediate plant growth responses and thus adaption to salt stress (Galvan-Ampudia et al., 2013; Yue et al., 2015; Van den Berg et al., 2016). Abscisic acid (ABA) signaling plays a crucial role in the quiescence of root growth induced by salt stress, and its concentration is reduced during the recovery phase (Duan et al., 2013; Duan et al., 2015; Van Zelm et al., 2020). Besides to modulate root growth, ABA signaling also mediates several other processes, such as gene transcription, stomatal closure, ion transport, and reactive oxygen species (ROS) production, through phosphorylating various downstream targets by sucrose non-fermenting 1-related protein kinases (SnRKs) (Van Zelm et al., 2020). In addition, the accumulation of primary metabolites, including amino acids, sugars, and polyols, was reported to contribute to the plant salt tolerance (Ashraf and Foolad, 2007; Li et al., 2011). Sugar metabolism and amino acid metabolism are crucial to providing energy and osmotic regulatory substances in response to salt stress (Liu et al., 2020a). Asparagus (Asparagus officinalis L.), a kind of perennial herb, has important edible, medicinal, as well as economic value. Asparagus has moderate salinity tolerance, whereas depending on the genotypes, it exhibits different salt tolerances in heavy saline soil (Zhang et al., 2020; Gao et al., 2021). But less is known for us about the salt tolerance mechanism of asparagus at molecular level. In a previous study, we preliminarily investigated the global gene transcription response mechanism of leaves of asparagus variety ‘No. 08-2’ to salinity stress using the Illumina HiSeq™ 2500 sequencing platform, suggesting the crucial roles of ion transport, ROS metabolism, and carbon metabolism in response to salinity stress (Zhang et al., 2020). There are still few works on exploring the response of asparagus roots to salt stress. Roots are the key plant tissue exposed to soil salinity and play crucial roles in the uptake of nutrients and water, the secretion of organic acids and enzymes, and the production of hormones (Ouyang et al., 2007; Zhai et al., 2013). Not only the widely changed gene expression, the adjusted metabolite accumulation in response to salt stress also takes part in regulating plant growth and its adaptation to salt stress (Huang et al., 2018; Zhang et al., 2019a; Liu et al., 2020a). However, there is still a lack of research on the asparagus metabolism response to salt stress and its underlying connection with the gene response to salt stress is also not clear. While, these are required for facilitating the breeding of salt-tolerance asparagus. Using an integrated transcript/metabolite approach, a better understanding of salt tolerance mechanisms in many species (Ma et al., 2019; Zhang et al., 2019a; Liu et al., 2020a), has been built. In such reports, second-generation sequencing technology (SGS) has been widely employed for transcriptome analysis to screen differential gene expression under salt stress. With the development of sequencing technology, Oxford Nanopore Technologies (ONT) MinION, a kind of third-generation sequencing technology (TGS), shows more advantages in several respects compared with SGS. Specifically, ONT can achieve full-length transcriptome sequencing, quantify at isoform level, and identify complex structures of gene (Zhao et al., 2019; Zhang et al., 2021), and thus also opens a new point for exploring plant tolerance mechanisms. Here, the transcriptome and metabolome of the seedling roots of ‘jx1502’ and ‘gold crown’ were analyzed three days after salt treatments, in which the ONT MinION platform and widely targeted metabolomic analysis were used. Through the analysis of transcriptome and metabolome data between the two contrasting genotypes, we sought to reveal the crucial salt response genes and metabolites and their potential links.
Asparagus genotypes ‘jx1502’ (a salt-tolerant) and ‘gold crown’ (a salt-sensitive) were selected in this work (Gao et al., 2021). For the acquisition of experimental seedlings, the method in our previous study was applied (Zhang et al., 2020). Then almost 60-day-old seedlings accepted salt stress treatment with 200 ml solution of 100 mM NaCl, while the controls were irrigated with distilled water. After three days of treatment, total 12 root samples of two asparagus genotypes for each treatment with three biological replicates were collected.
The RNA extraction, cDNA library construction, and sequence assay for 12 root samples were conducted by Biomarker Biotechnology Corporation (Beijing, China). The concrete methods were same with those described in Zhang et al’s study (2021). In these processes, full-length cDNA libraries were constructed and Oxford Nanopore Technologies (ONT) MinION sequencer was used for sequence assay
Transcriptome analysis referred to the methods of Zhang et al. (2021). Firstly, through the filter of low quality and short read length reads and the elimination of ribosomal RNA from raw reads, clean reads were obtained and subsequently full-length transcripts were detected. Then the reference transcriptome was constructed for full-length reads mapping and consequence analysis through combining the final transcripts after removing redundancy and the known transcripts of the genome (GCA_001876935.1, 2017). For quantitative analysis, the reads per gene per 10,000 mapped reads were used to estimate expression levels. The differential expression analysis between two treatments were conducted by DESeq2 R package. Genes with the fold change ≥ 2 and a P value< 0.01 were appointed as differentially expressed genes (DEGs). Among the identified DEGs through transcriptome analysis, 9 DEGs were selected to confirm their expression patterns using quantitative real-time PCR (qRT-PCR) analysis, with 3 technical replicates for each sample of 3 biological replicates, following Zhang et al. (2021). The specific primers for asparagus are shown in Supplementary Table 1 . In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses for DEGs were performed in several key comparisons. Alternative splicing (AS) events were identified using the AStalavista tool. Transcription factors (TFs) were identified with iTAK.
The root samples that we collected in advance and stored at -80°C were used for metabolite analysis. The metabolite extraction and its identification and quantification analysis were carried out by Wuhan MetWare Biotechnology Co., Ltd. (http://www.metware.cn). Briefly, the freeze-dried roots were crushed, and 100 mg was used for extraction with 70% aqueous methanol of 1.2 ml at 4°C for overnight. Following centrifugation, extracts were absorbed and filtered before ultra-performance liquid chromatography-mass/mass spectrometry (UPLC-MS/MS) (UPLC, Shim-pack UFLC SHIMADZU CBM30A system, http://www.shimadzu.com.cn/; MS, Applied Biosystems 6500 Q TRAP, http://www.appliedbiosystems.com.cn/) analysis referring to the procedures used in others’ studies (Yuan et al., 2018; Zhang et al., 2019b). With the metabolite information in MetWare Database and public database, the qualitative analysis of metabolite was conducted in accordance with the secondary spectrum information. In addition, quantitative analysis was performed with the multiple reaction monitoring mode. Partial least squares discriminant analysis was used for metabolite information mining. Metabolites with VIP (variable importance in projection) ≥ 1 and absolute Log2FC (fold change) ≥ 1 were appointed as differentially accumulated metabolites (DAMs). The KEGG Compound database (http://www.kegg.jp/kegg/compound/) and KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html) were used for the annotation of identified metabolites.
The transcriptomes of ‘jx1502’ and ‘gold crown’ under different treatments obtained by using ONT were compared ( Supplementary Table 2 ). Through transcriptome sequencing, there were 42.52 million clean reads generating from 12 asparagus roots samples. And the base calling accuracy was all more than 90.00% for every library. The percentages of full-length reads between 77.00% and 80.39% were obtained.
In total, 877 and 1610 DEGs were identified in ‘jx1502’ and ‘gold crown’ under salt stress compared with the distilled water treatment (control), including 519 and 483 upregulated DEGs, respectively, and 358 and 1127 downregulated DEGs, respectively. There were many more DEGs in ‘gold crown’ than in ‘jx1502,’ indicating that the salt-sensitive genotype responds to salinity stress in a wider range. For ‘jx1502,’ the number of upregulated DEGs was much larger than that of downregulated DEGs, while more than 2 times the number of DEGs was downregulated than upregulated in ‘gold crown,’ which revealed their diverse response patterns in relation to corresponding adaptive mechanisms. For further exploring the relationships of DEGs between the two genotypes in response to salt stress, a Venn diagram was constructed ( Figure 1A ). Among these, 291 DEGs were common in both genotypes. There were also 586 and 1319 specific DEGs in ‘jx1502’ and ‘gold crown,’ respectively. GO and KEGG enrichment analyses were performed using common and specific DEG sets ( Figures 1B, C ). GO enrichment analysis revealed that “transport” was significantly enriched in most DEGs in all three DEG sets, suggesting the crucial role of this term in the roots’ response to salt stress. Some DEGs involved in ion transport from both genotype-specific sets were significantly enriched in “ion transmembrane transport” and “hydrogen ion transmembrane transport,” while “chloride transport” was only significantly enriched in ‘jx1502’-specific DEGs. Biological processes related to genetic information processing and protein metabolism were also significantly enriched with a lot of DEGs. In addition, there were some significantly enriched terms related to cell wall metabolism. For example, “cell wall organization” and “beta-glucan metabolic process” were significantly enriched with common DEGs and several genotype-specific DEGs. In addition, “microtubule-based process” was only significantly enriched with ‘jx1502’-specific DEGs, and “xyloglucan metabolic process” and “pectin metabolic process” were only significantly enriched in the ‘gold crown’ genotype. Moreover, “response to auxin” was significantly enriched in both genotype-specific DEG sets. KEGG enrichment analysis revealed that the pathways related to RNA degradation, spliceosome, and protein processing, as well as sugar and amino acid metabolism, were significantly enriched with common DEGs. In addition, ‘jx1502’-specific DEGs were significantly enriched in “Glutathione metabolism” and “Plant hormone signal transduction”, and ‘gold crown’-specific DEGs were significantly enriched in “Tyrosine metabolism” and “Starch and sucrose metabolism”.
In this study, between two asparagus genotypes, several genes encoding for various transporters and proteins related to ion transport and homeostasis were differentially regulated under salt stress ( Figure 2 and Supplementary Table 3 ). In ‘jx1502,’ we found 1 calcium-transporting ATPase (ECA) gene, 1 calcium-dependent protein kinase (CDPK) gene, 1 proton pump gene, 1 cation/H+ antiporter (CHX) gene, and 1 chloride channel (CLC) gene were upregulated, while 1 calmodulin (CAM) gene, 1 calcineurin B-like protein (CBL) gene, 1 calcium-binding protein (CML) gene, and 2 Proton pump genes were downregulated under salt stress. In ‘gold crown,’ 1 annexin D3 (ANN3) gene, 2 CDPK genes, 3 CML genes, 1 CBL-interacting protein kinase (CIPK) gene, and 2 proton pump genes were upregulated, while 4 ECA genes, 1 cation/calcium exchanger (CCX) gene, 1 CAM gene, 1 CML gene, 6 proton pump genes, 1 potassium channel (KOR) gene, and 1 potassium transporter (HAK) gene were downregulated under salt stress. Aquaporins play key roles in responding to salt stress. In ‘jx1502,’ only one tonoplast intrinsic protein (TIP) gene was downregulated under salt stress. In ‘gold crown,’ 1 nodulin-like intrinsic protein (NIP) gene, 1 TIP gene, and 4 plasma membrane intrinsic protein (PIP) genes were downregulated. In response to salt stress, 5 ABC transporter genes were upregulated in ‘jx1502,’ while 2 ABC transporter genes were upregulated and 10 ABC transporter genes were downregulated in ‘gold crown.’ Several important genes for the transport of nutrition ions also showed differential response patterns to salt stress between the two genotypes. In ‘jx1502,’ 1 protein NRT1/PTR FAMILY (NPF) gene was upregulated under salt stress. In ‘gold crown,’ 1 NPF gene, 1 high-affinity nitrate transporter (NRT) gene, 1 ammonium transporter (AMT) gene, 1 phosphate transporter (PHT) gene, 2 magnesium transporter (MRS2, NIPA) genes, 3 Zn2+, Fe2+, Mn2+ transporter (ZIP) genes, 1 copper transporter (COPT) gene, 2 copper transport protein (CCH) genes, and 2 copper-transporting ATPase (HMA, RNA) genes were downregulated under salt stress. In our study, several DEGs related to IAA and ABA signal transduction were identified, which exhibited differential responses to salt stress in ‘jx1502’ and ‘gold crown’ ( Figure 2 and Supplementary Table 3 ). For IAA transport, under salt stress, 1 auxin transport protein BIG gene, 1 elongator complex protein (ELP) gene, 1 auxin-responsive protein SAUR gene, and 1 indole-3-acetic acid-amido synthetase (GH3) gene were upregulated, and 1 auxin-responsive protein SAUR gene and 1 auxin-induced protein/Auxin-responsive protein (AUX/IAA) were downregulated in ‘jx1502,’ while 1 auxin-responsive protein SAUR gene, and 1 AUX/IAA gene were upregulated, and 1 auxin transporter-like protein gene (AUX), 1 auxin efflux carrier component 5c gene (PIN5C), 2 ABC transporter B family member (ABCB) genes, 1 auxin-responsive protein SAUR gene, 3 AUX/IAA genes, and 1 auxin response factor (ARF) gene were downregulated in ‘gold crown.’ As for ABA signal transduction, 2 protein phosphatase 2C (PP2C) genes, 1 snf1-related kinase 2 gene (SnRK2), and 1 ABA responsive element binding factor gene (ABF) were upregulated in ‘jx1502.’ One abscisic acid receptor (PYL) gene and 1 SnRK2 gene were downregulated in ‘jx1502,’ while 4 PP2C genes and 1 SnRK2 gene were downregulated in ‘gold crown.’ In this study, cell cycle-related genes presented different response profiles to salt stress between ‘jx1502’ and ‘gold crown.’ In ‘jx1502,’ 1 cyclin-dependent kinase (CDK) gene and 1 transcription factor E2FA were upregulated. In ‘gold crown,’ 1 cyclin (CYC) gene and 1 CDK gene were upregulated, 1 cell division cycle protein (CDC) gene, 1 CDK gene, and 1 transcription factor E2FA were downregulated. For cell growth, 1 tubulin gene, 2 microtubule-associated protein (MAP) genes, 2 protein PIR genes, and 1 T-complex protein (CCT) gene were upregulated in ‘jx1502,’ while 5 tubulin genes, 1 actin-depolymerizing factor (ADF) gene, and 2 MAP genes were downregulated in ‘gold crown.’ In addition, several DEGs encoding cell wall-related proteins were found to be differentially regulated between two asparagus genotypes in response to salt stress. In ‘jx1502,’ 1 glycosyltransferase-like KOBITO (ELD) gene, 1 callose synthase (CALS) gene, 1 thermospermine synthase ACAULIS5 (ACL) gene, and 1 expansin (EXP) gene were upregulated. In ‘gold crown,’ 1 xyloglucan endotransglucosylase/hydrolase (XTH) gene, 1 UDP-arabinopyranose mutase (RGP) gene, and 1 pectinesterase (PME) gene were upregulated, while 1 ELD gene, 1 CALS gene, 1 EXP gene, and 1 pectinesterase PPE8B gene were downregulated ( Figure 2 and Supplementary Table 3 ).
In this study, total 120 TFs were differentially expressed under salt stress in two genotypes. There were 46 and 89 differentially expressed TFs that were identified in ‘jx1502’ and ‘gold crown,’ respectively, and mainly belonged to the TF families MYBs and MYB-related, bHLH, NAC, C3H, and HSF ( Figure 3A and Supplementary Table 4 ). As shown in Figure 3A , the response pattern of the bHLH, NAC, HSF, and C3H family members was basically the same in both genotypes, with about half or more members upregulated. In particular, MYB and MYB-related family members were mainly upregulated in ‘gold crown’ but downregulated in ‘jx1502.’ Additionally, there were 4712 AS events in all 12 libraries, which were identified from 3443 genes ( Supplementary Table 5 ). Five kinds of AS events were found, among which intron retention IR events (27.06%) were the most, common followed by alternative 3′ splice site A3SS (25.74%), alternative 5′ splice site A5SS (25.13%), exon skipping ES (19.93%), and mutually exclusive exon MXE (2.14%) ( Figure 3B ). In addition, we focused on DEGs with different AS events (DAS-DEGs) in both genotypes. In total, 172 DAS-DEGs were detected in ‘jx1502,’ with 109 upregulated and 63 downregulated genes. In addition, 290 DAS-DEGs were detected in the salt-sensitive genotype, with 110 upregulated and 180 downregulated genes. Based on GO analysis, the DAS-DEGs in the two genotypes were significantly enriched to some common terms, including “transport,” “regulation of RNA metabolic process,” “cell wall organization,” “DNA metabolic process,” “chromosome segregation,” and “nucleotide biosynthetic process” ( Figure 3C ). “Proteolysis” and “response to auxin” were significantly enriched only in ‘gold crown.’ In addition, there were many DAS-DEGs in both genotypes enriched in “response to stimulus” and “anatomical structure development” that were closely related to salt tolerance.
Nine DEGs in ‘jx1502’ and ‘gold crown’ under salt stress were randomly selected for qRT-PCR validation. The qRT-PCR expression trend of these nine genes was highly consistent with the RNA-seq results ( Supplementary Figure 1 ).
For the aim to explore the difference in salt tolerance between ‘jx1502’ and ‘gold crown’ at the metabolic level, the analysis of metabolite profiles in the roots of both genotypes was conducted by using UPLC-MS/MS. A total of 491 metabolites were identified, including sugars, amino acids, organic acids, phenolic acids, lipids, and flavonoids. Principal component analysis (PCA) presented a clear separation between the distilled water treatment and salt treatment in both ‘jx1502’ and ‘gold crown,’ as well as a separation of genotypes under both control and salt treatment in the first two principal components ( Figure 4A ). Compared with the control, 185 metabolites were identified to be significantly changed in ‘jx1502’ and ‘gold crown’ after salt stress. Among these metabolites, 135 DAMs and 73 DAMs were identified in ‘jx1502’ and ‘gold crown’ under salt stress compared with the control, including 32 and 40 increased DAMs, respectively, and 103 and 33 decreased DAMs, respectively. In the intersection analysis ( Figure 4B ), only 23 salt-responsive metabolites were identified across the different genotypes. Additionally, 112 and 50 metabolites were associated with salt stress in ‘jx1502’ and ‘gold crown,’ respectively. Through metabolic pathway mapping, DAMs identified from the two genotypes were both highly enriched in pathways mainly involved in amino acid metabolism, sugar metabolism, biosynthesis of secondary metabolites, and glycerolipid metabolism ( Figures 4C, D ), indicating their important roles contributing to salt tolerance of asparagus. Furthermore, by checking the detailed content adjustment of these metabolites, many showed different adjustment patterns between ‘jx1502’ and ‘gold crown’ under salt stress conditions ( Supplementary Table 6 ). For instance, sugars, and polyols (6 out of 7) were obviously upregulated in ‘jx1502’ after salt stress, such as glucose, melibiose, and turanose, while those (8 out of 9) in ‘gold crown,’ such as glucose, melezitose, panose, and sorbitol, were mainly decreased. Fifteen organic acids, including citrate, succinate, fumaric acid, aminobutyric acid, homoserine, and pipecolinic acid that were upregulated, were regulated by ‘jx1502’ under salt treatment, whereas in ‘gold crown,’ there were only two organic acids (phosphoenol-pyrurate and citrate) in response to salt stress that showed a decrease. In addition, there were 6 and 4 amino acids that were increased in ‘jx1502’ and ‘gold crown,’ respectively, among which only one amino acid (asparagine) was common in the two genotypes and showed greater changes in ‘jx1502.’ In addition, lipids, phenolic acids, and flavonoid metabolites also showed obvious differences between the two genotypes in response to salt stress.
To further explore the intrinsic mechanism of asparagus salt tolerance, it may be a crucial step to integrate the connection between salt-responsive genes and metabolites. In our study, a lot of DEGs and DAMs were widely annotated to several sugar metabolism and amino acid metabolism-related pathways.
Based on transcriptome and metabolome data, a schematic sugar metabolism network was built with DAMs and DEGs. In total, 9 differential metabolites and 41 DEGs from 5 pathways (starch and sucrose metabolism, glycolysis/gluconeogenesis, TCA cycle, galactose metabolism, fructose, and mannose metabolism) related to sugar metabolism were included in the network ( Figure 5A and Supplementary Table 7 ). Among the metabolites, glucose and citrate were upregulated in ‘jx1502’ but decreased in ‘gold crown.’ Specifically, galactinol, melibiose, fumaric acid, and succinate were only increased in ‘jx1502.’ Sorbitol, mannitol, and phosphoenol-pyrurate were specifically decreased in ‘gold crown.’ At the transcription level, the upregulated expression of most DEGs in ‘jx1502’ and the downregulated expression in ‘gold crown’ may contribute to the differences in their metabolite content and salt tolerance. For instance, in starch and sucrose metabolism, glucose accumulation can be regulated by the hydrolysis of sucrose, starch, or cell wall polysaccharides. Our transcriptome data showed that DEGs encoding beta-fructofuranosidase (EC:3.2.1.26) and sucrose synthase (EC:2.4.1.13), which play key roles in sucrose hydrolysis, were upregulated in ‘jx1502’ but downregulated or remained consistent in ‘gold crown.’ Similarly, the DEGs encoding endoglucanase (EC:3.2.1.4) and beta-glucosidase (EC:3.2.1.21), which catalyze the key steps of cellulose hydrolysis, were upregulated in ‘jx1502,’ whereas about half were downregulated in ‘gold crown.’ Moreover, the expression level of a DEG encoding pectinesterase (EC:3.2.1.11) for starch hydrolysis was also downregulated in ‘gold crown.’ The response pattern of the above DEGs may contribute to glucose accumulation in ‘jx1502’ but decrease in ‘gold crown.’ In galactose metabolism, the increase of galactinol and melibiose in ‘jx1502’ could be primarily attributed to the induced expression of genes encoding their synthesis-related enzymes, including sucrose synthase (EC:2.4.1.13), raffinose synthase (EC:2.4.1.82), and beta-fructofuranosidase (EC:3.2.1.26). The related DEGs in this pathway were downregulated in the ‘gold crown,’ and the content of galactinol and melibiose was also decreased accordingly to a certain degree. In addition, most of the DEGs involved in fructose and mannose metabolism, glycolysis/gluconeogenesis, and the TCA cycle were also upregulated in ‘jx1502,’ while downregulated in ‘gold crown,’ which may contribute to the corresponding adjustment of polyols and organic acids in two genotypes.
For amino acid metabolism, 9 differential amino acids and 23 DEGs were involved in the schematic network ( Figure 5B and Supplementary Table 7 ). The amino acid content in response to salt stress was significantly increased in both genotypes, but the types of amino acids, except for asparagine, were different in the two genotypes. Corresponding to the accumulation of amino acids under salt stress, most DEGs involved in their biosynthesis were upregulated. In detail, the asparagine content was increased in both genotypes, especially in ‘jx1502,’ which was consistent with the upregulated expression of a gene encoding asparagine synthase (EC: 6.3.5.4) that catalyzed the synthesis of asparagine from aspartate. In ‘jx1502,’ leading to the strong salt-induced accumulation of threonine, methionine, histidine, arginine, and proline, most DEGs involved in the biosynthesis of these amino acids were upregulated under salt stress, such as those encoding homoserine dehydrogenase (EC: 1.1.1.3), glutamine amidotransferase (EC: 4.3.2.10), acetylornithine deacetylase (EC: 3.5.1.6), and delta-1-pyrroline-5-carboxylate synthetase (EC: 2.7.2.1; EC: 1.2.1.41). Moreover, the downregulated expression of a gene encoding proline dehydrogenase (1.5.5.2), which is involved in the degradation of proline, may further promote proline accumulation in ‘jx1502.’ Although the same degradation genes were also downregulated in the ‘gold crown’ without significantly changing the expression of genes related to synthesis, the increase in proline content in ‘gold crown’ was not obvious. For tyrosine accumulation in ‘gold crown,’ given the greatly downregulated expression of genes encoding the crucial tyrosine degradation enzymes, including 4-hydroxyphenylpyruvate dioxygenase (EC: 1.13.11.27) and fumarylacetoacetase (EC: 3.7.1.2), and the upregulation of related genes in the synthesis pathway, the increased accumulation of tyrosine in ‘gold crown’ may be achieved by reduced degradation combined with increased biosynthesis. In addition, the upregulated expression of genes encoding glutamine synthetase (EC:6.3.1.2) and dihydroxy-acid dehydratase (EC: 4.2.1.9) in ‘gold crown’ under salt stress may contribute to the accumulation of glutamine and leucine in it. Besides amino acids, ‘jx1502’ specifically accumulated some organic acids (aminobutanoste, homoserine, and those in the TCA cycle) in this network, which were also closely related to the upregulation of their synthesis-related genes. For example, salt-induced expression of the gene encoding glutamate decarboxylase (EC: 4.1.1.15) promoted the synthesis of aminobutanoste from glutamate, and the gene encoding homoserine dehydrogenase (EC: 1.1.1.3) promoted the synthesis of homoserine from L-Asparate 4-semialdehyde. In summary, the integration of both omics data suggested that the accumulation of different amino acids and organic acids may be a positive feature of withstanding salt stress.
Soil salinity is a major abiotic stress that seriously constrains plant growth and development (Rengasamy, 2010; Duarte et al., 2013; Munns and Gilliham, 2015). To adapt to salt stress, several plants have evolved complex mechanisms to combat salinized environments. Here, the full-length transcriptome and metabolome of asparagus were comprehensively analyzed after salt stress treatment. As a result, the extensive transcriptional and metabolic adjustments in asparagus were triggered by salt stress and were mainly involved in ion transport, root growth, and sugar and amino acid metabolism.
Salt stress usually causes excess Na+ accumulation and thus plant cell toxicity. In this process, K+ uptake and its related physiological functions were significantly inhibited (Chen et al., 2011; Benito et al., 2014). So, a relatively balanced cytosolic K+/Na+ ratio may be regarded as a key salt tolerance trait for plants (Van Zelm et al., 2020). Proton pumps build up the proton-motive force necessary for ion transport and Na+ homeostasis (Van Zelm et al., 2020). Most DEGs encoding proton pumps, including P-type ATPase and pyrophosphate-energized membrane proton pumps, that fuel the Na+ efflux were downregulated by salt stress in ‘gold crown.’ In addition, the gene encoding HAK, which mainly mediates K+ uptake in roots (Nieves-Cordones et al., 2016), and CCX, which serves as a Na+/K+ exchanger in the tonoplast to perform Na+ sequestration in the vacuole and K+ accumulation in the cytoplasm (Chen et al., 2011), were also downregulated in ‘gold crown’ while remaining constant in ‘jx1502.’ These were disadvantageous for ‘gold crown’ to maintain K+/Na+ homeostasis under salt stress. In contrast to ‘gold crown,’ some more positive responses of transporter DEGs to salt stress in ‘jx1502’ may contribute to effective maintenance of K+/Na+ homeostasis. For example, CHXs that mediate K+ transport in response to salt stress (Jia et al., 2018; Isayenkov et al., 2020) were upregulated in ‘jx1502’ while remaining constant in ‘gold crown.’ Another obviously different response between the two genotypes was shown in the regulation of an ABC transporter that is also involved in the modulation of K+/Na+ homeostasis (Mahajan et al., 2017). In ‘jx1502,’ five DEGs encoding ABC transporters were upregulated, whereas 10 out of 12 DEGs encoding ABC transporters were downregulated in ‘gold crown.’ These results indicate that the more active ion transport systems of ‘jx1502’ may contribute to its superior K+/Na+ homeostasis and higher salt tolerance than ‘gold crown.’ Additionally, large amounts of Cl- accumulation induced by salt stress also have many adverse effects on plants (Liu et al., 2020b). Studies showed that several CLC genes take part in plant salt tolerance and speculated that they play roles by mediating Cl- transport across the tonoplast (Nguyen et al., 2016). Therefore, the specifically upregulated expression of the CLC gene in ‘jx1502’ under salt stress may also contribute to its higher salt tolerance compared with ‘gold crown.’ However, salt usually affects plant uptake and nutrient transport (e.g., , , , Mg2+, Zn2+, Fe2+, and Mn2+) (Tuna et al., 2008) and thus limits plant growth. In our study, several important genes for the transport of nutrition ions, such as NPF, NRT, AMT, PHT, MRS2, NIPA, and ZIP, were significantly downregulated in ‘gold crown’ roots under salt stress; however, they remained constant in ‘jx1502,’ except for an induced NPF gene and a downregulated ZIP gene. Widely downregulated NPF, NRT, AMT, and PHT may lead to a lack and imbalance of nitrogen and phosphorus elements in plants, which may have a serious impact on plant growth and production (Fan et al., 2009; Léran et al., 2014; Bu et al., 2019; Lv et al., 2021). MRS2 family proteins and NIPA have been reported to have a putative function in Mg2+ transport and homeostasis (Zhang et al., 2019c; Kobayashi, 2022). Therefore, the downregulation of DEGs encoding these Mg2+ transporters in ‘gold crown’ may reflect how salt stress is affected by Mg2+ transport. In addition, ZIP family metal transporters are related to iron, zinc, and manganese uptake (Lin et al., 2009; Milner et al., 2013). Notably, three out of four DEGs encoding ZIPs were downregulated by salt stress in ‘gold crown,’ which might lead to a decrease in micronutrient uptake in salt-treated ‘gold crown’ roots. However, the stable expression of genes encoding the above nutrient transporters and the exceptional upregulation of NPF in ‘jx1502’ under salt stress may contribute to its relatively normal nutrient status and growth.
The maintenance of lateral root growth is an obvious benefit for the enhancement of water-use efficiency and nutrient uptake, and it serves as an efficient strategy for stress adaptation (Casimiro et al., 2003; Liu et al., 2020c). Previous studies have indicated that lateral root development in response to salt stress can be mediated by plant hormones, such as ABA and IAA (Lu et al., 2019; Van Zelm et al., 2020). For IAA in this process, its dynamic distribution, mediated by three major AUX/LAX influx carriers, PIN efflux carriers, and ABCB/PGP/MDR, plays a crucial role (Titapiwatanakun and Murphy, 2009; Barbez et al., 2012). In this study, genes encoding such IAA transporters were downregulated in ‘gold crown’ in response to salt stress, while they remained constant in ‘jx1502.’ In addition, genes encoding several other proteins that were also required for polar IAA transport and plant adaptability to environmental stimuli were especially upregulated in ‘jx1502,’ including the auxin transport protein BIG and ELP (Nelissen et al., 2010; Cheng et al., 2019). Thus, more active IAA transport in ‘jx1502’ under salt stress may contribute to its better performance in root architecture remodeling and adaptability to environmental stimuli. In contrast to IAA, ABA produces a negative effect in regulating the growth of lateral root (Duan et al., 2015). It is known that ABA signaling is perceived by the receptor pyrabactin resistance/pyrabactin resistance like (PYR/PYL), which inactivates PP2C, the negative regulator of ABA signaling. Then the activated protein kinase SnRK2 can regulate the expression of certain genes through phosphorylation on transcription factors. Most components of this signaling pathway are required for lateral root growth suppression by ABA (Xing et al., 2016; Liu et al., 2020c). In our data, the DEGs encoding PP2C were upregulated in ‘jx1502’ and downregulated in ‘gold crown.’ The gene encoding PYL was specifically downregulated in ‘jx1502.’ As a result, ‘jx1502’ may obtain a stronger negative regulation capacity for the ABA signal, thus weakening the negative role of ABA on lateral root development. Taken together, the different responses of IAA- and ABA-related genes in the two genotypes to salt stress may contribute to their differences in root system architecture and thus salt tolerance. Compared to plant hormones, root growth results more directly from cell division and growth. CDC plays an important role in regulating the cell cycle and plant development through the turnover of key proteins via ubiquitin-proteasome system degradation, and its mutants result in premature senescence and plant death (Huang et al., 2016). In addition, the transcription factor E2FA induces the genes required for cell cycle progression (Vandepoele et al., 2005). In the present study, DEGs encoding CDK and E2FA were upregulated in ‘jx1502,’ while they were widely downregulated in ‘gold crown.’ A CDC gene was specially downregulated in ‘gold crown.’ Similarly, most DEGs related to cell growth were also induced in ‘jx1502’ but repressed in ‘gold crown.’ For instance, tubulin and actin are key components of the cytoskeleton and play regulatory roles in cell growth (Wasteneys and Galway, 2003). Several DEGs encoding tubulin and MAP were mainly upregulated in ‘jx1502’ and downregulated in ‘gold crown.’ A gene encoding CCT7, which plays a role in the folding of actin and tubulin (Hill and Hemmingsen, 2001), was especially upregulated in ‘jx1502.’ In addition, genes related to actin cytoskeleton reorganization, which is required for cell growth and stress response, were also differentially regulated between two genotypes, such as those encoding protein PIR and ADF4 (Li et al., 2004; Henty et al., 2011), which were upregulated in ‘jx1502’ and downregulated in ‘gold crown,’ respectively. However, as the crucial modulator of cell expansion adaptable to cell division and growth altered by exogenous stimuli, the cell wall is usually in a dynamic changing process, with modifications in structure and the production of its components (Wen et al., 1999). Consistent with the response of cell division and growth in two genotypes, numerous DEGs involved in cell wall construction and modification were identified and were also mainly upregulated in ‘jx1502’ but downregulated in ‘gold crown,’ such as those encoding ELD1, CALS7, and expansin (EXPB1a and EXPA4). Among these, ELD1 can coordinate cell elongation and cellulose synthesis (Pagant et al., 2002). CALS7 plays an important role in phloem development (Barratt et al., 2011; Xie et al., 2011). Expansin has a function in plant growth and development by altering cell wall extensibility, and transgenic plants that overexpress expansin genes display a salt-tolerant phenotype (Han et al., 2012; Lü et al., 2013). Thus, from a cellular perspective, root growth was also reduced in ‘gold crown,’ while it was enhanced in ‘jx1502’ as a possible salt-tolerance trait.
Sugar metabolism and amino acid metabolism are crucial to providing energy and osmotic regulatory substances in response to salt stress (Liu et al., 2020a). It was reported that the increased accumulation of sugar and polyols (sucrose, melibiose, galactinol, sorbitol, and trehalose) from sugar metabolism was related to better growth performance in salt-tolerant plants under salt stress (Widodo et al., 2009; Huang et al., 2018). Similarly, the accumulation of melibiose and galactinol significantly increased in ‘jx1502’ under salt stress, in accordance with the altered expression of related genes in sugar metabolism processes. Moreover, glucose, the most important energy source that contributes to plant growth under salt stress (Wang et al., 2019), was also increased in jx502 by the enhanced hydrolysis of sucrose, starch, and cell wall polysaccharides. The crucial catabolic pathways of glucose, glycolysis, and the TCA cycle are enhanced by the upregulated expression of related DEGs, during which the generated intermediate metabolites and energy are important for plant growth and salt stress defense (Plaxton, 1996; Fernie et al., 2004; Zhong et al., 2015). Therefore, the regulation of DEGs and metabolites in the sugar metabolism processes of ‘jx1502’ may act together to resist salt stress. In contrast, most DEGs related to sugar metabolism in ‘gold crown’ were repressed, consistent with its altered accumulation of metabolites, including sugars, such as glucose, and polyols, such as mannitol and sorbitol, revealing that ‘gold crown’ failed to produce more energy and osmotic regulatory substances to combat salt stress. Serving as basic elements of proteins, increased levels of amino acids are considered essential for plant salt stress tolerance by maintaining cell membrane stability, improving osmotic regulation, and avoiding oxidative damage (Liu et al., 2020a). Currently, salt stress leads to the increased accumulation of several amino acids in both genotypes. Asparagine can scavenge cytotoxins and protect protein SH groups from oxidation and ROS (Liu et al., 2020a), and it was common in both genotypes and showed greater changes in ‘jx1502,’ which was consistent with the upregulated expression of genes encoding asparagine synthase that catalyzed the synthesis of asparagine from aspartate. In addition, the accumulation of proline, histidine, threonine, arginine, and methionine specifically increased in ‘jx1502,’ and leucine, tyrosine, and glutamine specifically increased in ‘gold crown,’ which was also in accordance with the regulation of genes in complex metabolic processes. For example, the increased accumulation of proline, an important osmoregulation substance (Wu et al., 2017), in ‘jx1502’ under salt stress may be achieved by the upregulated expression of synthesis-related genes encoding delta-1-pyrroline-5-carboxylate synthetase and the downregulated expression of degradation-related genes encoding proline dehydrogenase. In the ‘gold crown,’ the increased accumulation of tyrosine was also achieved by its reduced degradation combined with increased biosynthesis. Similar results were also reported by Huang et al. (2018) and Zhang et al. (2019a), in which the increased accumulation of amino acids generally occurred in both contrasting genotypes differing in salt tolerance under salt stress, with differences in response intensity and amino acid types. Therefore, these results indicate that the increased accumulation of amino acids may be a general response of plants to salt stress, and there is also the possibility that the response intensity and types participate in determining their difference in tolerance.
In this study, the different responses of ‘jx1502’ and ‘gold crown’ to salt stress were investigated by transcriptome and metabolome analyses. First, ‘jx1502’ induced more transporters and proteins for modulating K+/Na+ and water homeostasis and maintained a more constant transport system for nutrient uptake and distribution than ‘gold crown’ under salt stress. In addition, ‘jx1502’ strengthens the response to IAA and cell division and growth for root remodeling and thus salt tolerance. Moreover, ‘jx1502’ enhanced sugar and amino acid metabolism for energy supply and osmotic regulatory substance accumulation to meet the demands of protective mechanisms against salt stress.
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/, PRJNA871773.
YC designed this study. XZ, CH, YuqL, YY, and YunL performed the experiments. YC, CH and XZ analyzed the data. YC, and XZ drafted the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by S&D Program of Hebei (22326309D), HAAFS Science and Technology Innovation Special Project (2022KJCXZX-JZS-08), The Earmarked Fund for Hebei Modern Agro-industry Technology Research System (HBCT2021200201,HBCT2021200202), and “Giant Project” of Hebei Province (2018-3).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. |
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PMC9648822 | 34849611 | Lifen Xu,Melania Balzarolo,Emma L Robinson,Vera Lorenz,Giacomo Della Verde,Lydia Joray,Michika Mochizuki,Beat A Kaufmann,Gideon Valstar,Saskia C A de Jager,Hester M den Ruijter,Stephane Heymans,Otmar Pfister,Gabriela M Kuster | NOX1 mediates metabolic heart disease in mice and is upregulated in monocytes of humans with diastolic dysfunction | 26-11-2021 | Diastolic dysfunction,Hypertrophy,Inflammation,Metabolic heart disease,NOX1 | Abstract Aims Microvascular inflammation plays an important role in the pathogenesis of diastolic dysfunction (DD) and metabolic heart disease. NOX1 is expressed in vascular and immune cells and has been implicated in the vascular pathology of metabolic disease. However, its contribution to metabolic heart disease is less understood. Methods and results NOX1-deficient mice (KO) and male wild-type (WT) littermates were fed a high-fat high-sucrose diet (HFHS) and injected streptozotocin (75 mg/kg i.p.) or control diet (CTD) and sodium citrate. Despite similar weight gain and increase in fasting blood glucose and insulin, only WT-HFHS but not KO-HFHS mice developed concentric cardiac hypertrophy and elevated left ventricular filling pressure. This was associated with increased endothelial adhesion molecule expression, accumulation of Mac-2-, IL-1β-, and NLRP3-positive cells and nitrosative stress in WT-HFHS but not KO-HFHS hearts. Nox1 mRNA was solidly expressed in CD45+ immune cells isolated from healthy mouse hearts but was negligible in cardiac CD31+ endothelial cells. However, in vitro, Nox1 expression increased in response to lipopolysaccharide (LPS) in endothelial cells and contributed to LPS-induced upregulation of Icam-1. Nox1 was also upregulated in mouse bone marrow-derived macrophages in response to LPS. In peripheral monocytes from age- and sex-matched symptomatic patients with and without DD, NOX1 was significantly higher in patients with DD compared to those without DD. Conclusions NOX1 mediates endothelial activation and contributes to myocardial inflammation and remodelling in metabolic disease in mice. Given its high expression in monocytes of humans with DD, NOX1 may represent a potential target to mitigate heart disease associated with DD. | NOX1 mediates metabolic heart disease in mice and is upregulated in monocytes of humans with diastolic dysfunction
Microvascular inflammation plays an important role in the pathogenesis of diastolic dysfunction (DD) and metabolic heart disease. NOX1 is expressed in vascular and immune cells and has been implicated in the vascular pathology of metabolic disease. However, its contribution to metabolic heart disease is less understood.
NOX1-deficient mice (KO) and male wild-type (WT) littermates were fed a high-fat high-sucrose diet (HFHS) and injected streptozotocin (75 mg/kg i.p.) or control diet (CTD) and sodium citrate. Despite similar weight gain and increase in fasting blood glucose and insulin, only WT-HFHS but not KO-HFHS mice developed concentric cardiac hypertrophy and elevated left ventricular filling pressure. This was associated with increased endothelial adhesion molecule expression, accumulation of Mac-2-, IL-1β-, and NLRP3-positive cells and nitrosative stress in WT-HFHS but not KO-HFHS hearts. Nox1 mRNA was solidly expressed in CD45+ immune cells isolated from healthy mouse hearts but was negligible in cardiac CD31+ endothelial cells. However, in vitro, Nox1 expression increased in response to lipopolysaccharide (LPS) in endothelial cells and contributed to LPS-induced upregulation of Icam-1. Nox1 was also upregulated in mouse bone marrow-derived macrophages in response to LPS. In peripheral monocytes from age- and sex-matched symptomatic patients with and without DD, NOX1 was significantly higher in patients with DD compared to those without DD.
NOX1 mediates endothelial activation and contributes to myocardial inflammation and remodelling in metabolic disease in mice. Given its high expression in monocytes of humans with DD, NOX1 may represent a potential target to mitigate heart disease associated with DD.
In their multifactorial pathogenesis, diastolic dysfunction (DD) and heart failure with preserved ejection fraction (HFpEF) still remain poorly understood. They frequently occur in patients with obesity and metabolic syndrome. Microvascular inflammation and dysfunction have recently been recognized as major driving forces. We show that genetic deletion of Nox1 prevents cardiac inflammation, remodelling, and dysfunction in metabolic disease in mice and find NOX1 upregulated in peripheral monocytes of patients with DD. These findings add to our understanding how obesity, inflammation, and heart disease are linked, which is a pre-requisite to find therapeutic strategies beyond the control of co-morbidities in HFpEF.
Obesity and metabolic syndrome are major health burdens in Western countries with reported 10–30% of individuals affected across Europe and >30% in the USA. A leading cause of death and morbidity in patients with metabolic syndrome are cardiovascular diseases. Besides coronary artery disease and hypertension, metabolic syndrome can induce a specific form of cardiomyopathy that occurs independently from macrovascular complications and is referred to as metabolic heart disease. Patients with metabolic heart disease may present with heart failure (HF) with reduced (HFrEF) or, more frequently, preserved ejection fraction (HFpEF). In the multifactorial pathogenesis of HFpEF, low-grade systemic inflammation elicited by obesity and diabetes leads to microvascular endothelial dysfunction, endothelial cell activation, and disturbed endothelial cell-cardiomyocyte crosstalk. This contributes to cardiomyocyte hypertrophy, stiffening, and impaired relaxation, which are hallmark features of the HFpEF phenotype of metabolic heart disease in humans. NADPH oxidases (NOX) are transmembrane proteins that catalyze the production of reactive oxygen species (ROS), mostly superoxide, by transferring electrons from NADPH to molecular oxygen. The superoxide production by the isoforms NOX1 and 2 is inducible and depends on the agonist-dependent assembly of multiple subunits. NOX1 is expressed in endothelial and vascular smooth muscle cells and upregulated in diabetic arteries. Enhanced NOX activity reduces the bioavailability of nitric oxide (NO) through peroxynitrite formation and uncoupling of endothelial NO synthase (eNOS), which has specifically been shown for NOX1 in diabetic mouse aorta. NOX1 has also been implicated in the accumulation of macrophages in the aorta of diabetic and atherosclerosis-prone mice. In addition, NOX1 is expressed in mesenteric resistance arteries, where it contributes to microvascular dysfunction in metabolic disease in mice. However, little is known about the expression and function of NOX1 in the heart and its role in cardiac disease. We therefore sought to assess the role of NOX1 in metabolic heart disease in mice, and validated our findings in monocytes derived from patients with and without clinically defined diastolic dysfunction (DD).
An Expanded Methods section is available in the Supplementary material online.
NOX1-deficient mice were a kind gift from Dr Karl-Heinz Krause, University Hospitals of Geneva and University of Geneva, Switzerland, and have previously been described. Upon receipt, the mice were bred to C57Bl/6N mice (Charles River) for at least four generations before use. For all studies, hemizygous NOX1y/− (NOX1 is located on the X chromosome) (KO) and male wild-type (WT) littermates were used (see Supplementary material online, Figure S1 for genotyping). All animal procedures complied with the NIH Guide for the Care and Use of Laboratory Animals and were approved by the Swiss Cantonal Authorities (Licence number 28347).
Four-week-old mice were randomly assigned to receiving high-fat high-sucrose (HFHS) diet (D12331, Research Diets Inc., New Brunswick, USA; g%: 23 protein, 17.5 sucrose, 17 maltodextrin 10, 0 starch, 35.8 fat, 5.56 kcal/g) (HFHS) or being continued on regular chow (3436, KLIBA NAFAG, Kaiseraugst, Switzerland; g%: 18.5 protein, 35 starch, 4.5 fat, 3.84 kcal/g) (control diet, CTD). After 3 weeks on diet, HFHS mice were injected a single dose of streptozotocin (STZ, 75 mg/kg; i.p.) diluted in sodium citrate (vehicle, 20 mM in 0.9% saline, pH 4.5), and CTD mice were injected with vehicle only (Supplementary material online, Figure S2).
Transthoracic echocardiography was performed prior to STZ/vehicle injection and at 12, 24, 36, and 44 weeks after injection using a Vevo 2100 high-resolution small animal digital ultrasound system (VisualSonics) equipped with a linear-array transducer (MS550) operating at a centreline frequency of 40 MHz. Echocardiography (five sessions per animal in total) was performed in light anaesthesia using isoflurane (5% for induction, 2% for maintenance). After removal of the chest hair using a depilatory agent (NairTM), mice were placed on a heated platform in the supine position. Body temperature was kept around 37°C. Left parasternal short-axis views at the mid papillary muscle level of the left ventricle (LV) and 2D-guided M-mode images were used to measure LV internal diameter at end-diastole and end-systole (LVID; d and LVID; s) and LV anterior wall (LVAW) and posterior wall (LVPW) thicknesses. LV mass was calculated based on the corrected cube formula (1.053 × [(LVID; d + LVPW; d + LVAW; d)3—LVID; d3] × 0.8). LV volumes in diastole (according to Teichholz: LV Vol; d = (7.0/(2.4 + LVID; d)) × LVID; d3) and systole (LV Vol; s = (7.0/(2.4 + LVID; s)) × LVID; s3) were used to calculate the left ventricular ejection fraction (LVEF) as follows: LVEF = 100 × (LV Vol; d—LV Vol; s)/LV Vol; d. For the evaluation of LV diastolic function, the transmitral inflow velocities were recorded with pulsed wave Doppler in the apical four-chamber view and mitral annular velocity was assessed with tissue Doppler imaging with the sample volume placed at the base of the posterior wall in a parasternal long axis. Thus, the measured and calculated Doppler indexes included the ratio of peak velocity of early to late filling of mitral inflow (E/A), the early diastolic mitral annulus velocity (E′), the ratio of E to E′ (E/E′), ejection time (ET), isovolumic contraction time (IVCT) and isovolumic relaxation time (IVRT). The LV myocardial performance index (Tei index) was calculated as (IVCT + IVRT)/ET. In addition, speckle tracking echocardiography was used to assess diastolic longitudinal peak strain rate [reverse longitudinal strain rate (rLSR)] at 44 weeks. For this purpose, the endocardium was manually traced on a parasternal long-axis view. Manual adjustments were done when needed. Endocardial strain values were then calculated with the strain package (Vevo Strain Package, Vevo 2100 version 1.6.0). All parameters were measured in three consecutive cardiac cycles and values were averaged. All data were acquired and analysed by one investigator blinded for animal treatment allocation.
Invasive haemodynamic measurements were performed 44 weeks after STZ/vehicle injection. Mice were anaesthetized with urethane (1200 mg/kg) mixed with alpha-chloralose (50 mg/kg) i.p., intubated via the orotracheal route and ventilated on a Harvard Apparatus HSE Mouse Ventilator MiniVent 845. A jugular vein was cannulated for administration of fluid and a pressure volume (PV) catheter (1.0F, PVR-1035, ADInstruments, Houston, USA) was inserted through the carotid artery. Heart rate and arterial blood pressure were continuously monitored. The catheter was then advanced into the LV and pancuronium (2 mg/mL; 50 µl i.p.) was applied for final recording of the PV-loops to avoid breathing artefacts. After recording of steady-state measurements, LV preload was reduced by pressure on the inferior caval vein and load-independent parameters of contractility were measured (ML870 Powerlab 8/30, ADInstruments; MPVS Ultra, Millar Instruments). Recordings were then analysed offline in a blinded fashion using LabChart 7 Pro (ADInstruments). At the end of invasive haemodynamic measurements, 10 IU heparin was infused through the jugular vein just before maximum blood collection (∼1 mL) from the right ventricle for volume calibration. The heart was arrested in diastole with 35 mmol/L KCl in PBS infused through the jugular vein, followed by perfusion fixation with 15 ml 4% paraformaldehyde (PFA) at a pressure between 80 and 100 mm Hg. The heart was excised and fixed in 4% PBS buffered formaldehyde (Polysciences, Inc., Warrington, USA) at 4°C for 24 h before further processing and embedding in paraffin. Mice that were not subjected to haemodynamic assessment were sacrificed directly after the last echocardiography by infusion of 35 mmol/L KCl/PBS through the jugular vein under deep anaesthesia with constant exposure to 5% isoflurane flow. The heart was perfused and fixed the same way as described above. Mice used for cell analyses were euthanized by i.p. injection of 200 mg/kg sodium pentobarbital.
Gene expression analysis was performed on flow-sorted CD14+ monocytes in a case–control selection of 20 individuals from the HELPFul study conducted at the University Medical Center (Universitair Medisch Centrum) Utrecht, the Netherlands. This single-centre, prospective, case-cohort study has been previously described. Patients aged 45 years and older, without previous cardiac interventions or congenital heart disease, that were referred by the general practitioner for diagnostic work-up to the cardiologist and gave written informed consent were eligible for inclusion. Patients were deemed as having DD based on the algorithm for LV DD of the HFA-PEFF score described by Pieske et al. and on current guidelines. The study protocol was approved by the Institutional Review Board of the University Medical Center Utrecht and complied with the principles outlined in the Declaration of Helsinki in October 2013. Trial registration NTR6016; Pre-results.
Data are shown in the figures as median and 10–90 percentile and listed in tables as mean ± SEM, unless indicated otherwise. Repeated measurements over time (echocardiography, weight) were tested for significance using a linear mixed effect model on R (version 3.4.1) and RStudio (1.0.153). Endpoint data were analysed with two-way ANOVA followed by Sidak post hoc test using Prism 9.2.0 (GraphPad Software, Inc.). Comparisons of two groups were done by Student’s t-test or non-parametric test as indicated. A P-value <0.05 was considered statistically significant.
The graphical abstract was drafted with PowerPoint using a cell shape for cardiomyocytes from Servier Medical Art (smart.servier.com).
To test the role of NOX1 in metabolic heart disease, we induced metabolic disease in WT and KO mice by HFHS and a single low-dose STZ injection (75 mg/kg i.p.). Wild-type and KO mice fed regular chow (CTD) and injected with vehicle (sodium citrate) were used as controls (Supplementary material online, Figure S2). Over 44 weeks from injection, WT-HFHS and KO-HFHS mice showed a continuous and comparable weight gain that was significantly greater than the one of WT-CTD and KO-CTD mice (Supplementary material online, Figure S3A). Compared to WT-CTD mice, WT-HFHS mice also showed a transient increase of fasting blood glucose, peaking around 8 weeks after STZ injection (Supplementary material online, Figure S3B), impaired glucose clearance in the glucose tolerance test (Supplementary material online, Figure S3C) and higher fasting plasma insulin levels (Supplementary material online, Figure S3D), and the same was true for KO-HFHS compared to KO-CTD mice. In summary, HFHS/STZ led to a metabolic disease phenotype in mice, independently of NOX1.
The HFpEF phenotype of metabolic heart disease in humans includes LV hypertrophy associated with DD. Repeated echocardiography showed an increase in diastolic LV anterior (LVAW) and posterior wall (LVPW) thicknesses beginning week 36 after STZ injection in WT-HFHS mice, resulting in a significant increase in total wall thickness (calculated as LVAW+LVPW) compared to WT-CTD mice. This increase was not observed in KO-HFHS mice (Supplementary material online, Table S1 and Figure 1A). Similarly, HFHS led to an increase in LV mass in WT, but not in KO mice (Figure 1B), whereas LV internal diameters were not different (Supplementary material online, Table S1). This indicates that genetic deletion of NOX1 prevents the development of concentric hypertrophy in our model. Metabolic heart disease may be associated with HFpEF or HFrEF. We found no significant differences in LV ejection fraction over time within or between the groups (Supplementary material online, Table S1). Similarly, and despite the observed concentric hypertrophy in WT-HFHS mice, the E/A and E/E′ ratios were also not different (Supplementary material online, Table S1). In addition, no significant differences were found for ET, IVCT, IVRT, and Tei index (data not shown). However, the reverse longitudinal strain rate (rLSR), which has recently been identified as a sensitive parameter of impaired LV relaxation in mice, significantly decreased in WT-HFHS compared to WT-CTD, but not in KO-HFHS compared to KO-CTD mice (Figure 1C). Because echocardiography has limited sensitivity for detection of elevated LV filling pressure in mice, a subset of consecutive mice was subjected to hemodynamic assessment prior to sacrifice. Forty-four weeks after STZ injection, WT-HFHS mice exhibited significantly elevated LV end-diastolic pressure (Ped) compared to WT-CTD mice. In contrast, Ped in KO-HFHS mice was comparable to that of KO-CTD mice and significantly lower than that of WT-HFHS mice (Figure 1D). Consistently, in comparison to WT-CTD, the maximum rate of LV pressure decay (dP/dt min), a measure of LV relaxation, was significantly decreased in WT-HFHS, but not in KO-HFHS mice compared to KO-CTD (Supplementary material online, Table S2). In turn, LV end-systolic pressure (Pes) was not different between groups, whereas the maximum rate of LV pressure rise (dP/dt max), a measure of global contractility, was decreased in WT-HFHS compared to WT-CTD mice, indicating some compromise of systolic function (Supplementary material online, Table S2). Sustained blood pressure elevation can induce cardiac hypertrophy. At 44 weeks, aortic systolic pressure showed no significant difference between WT-HFHS and KO-HFHS mice (Figure 1E), and the same was true for mean blood pressure and pulse pressure, suggesting that the concentric hypertrophy occurs in the absence of sustained blood pressure elevation.
Forty-four weeks after STZ/vehicle injection, we found that the heart weight was significantly increased in WT-HFHS compared to WT-CTD mice. This increase was not observed in KO-HFHS mice, which showed a comparable heart weight to that of KO-CTD mice (Figure 2A). To examine whether the increase in heart weight was due to increased cardiomyocyte size, increased extracellular matrix deposition, or both, cross-sectional heart sections were stained with wheat germ agglutinin (WGA) and picrosirius red. WT-HFHS mice had a significantly higher average cardiomyocyte size than WT-CTD and KO-HFHS mice (Figure 2B and C). In contrast, the amount of fibrosis was not different between groups (Figure 2D and Supplementary material online, Figure S4A–C). Because myocardial fibrosis inversely correlates with microvascular density, we assessed myocardial microvascular density using isolectin B (IB4) staining. Consistent with the lack of enhanced fibrosis, we did not find differences in microvascular density between the groups (Figure 2B and E). Superoxide, produced by intrinsic cardiac and immigrated inflammatory cells, can react with NO to form peroxynitrite, which exacerbates tissue damage and decreases NO bioavailability, and which can lead to nitration of protein tyrosine residues. We therefore assessed protein nitrotyrosinylation in the hearts after 44 weeks by immunohistochemistry. Immunoreactivity for nitrotyrosine was increased in the hearts of WT-HFHS as compared to WT-CTD, but not in KO-HFHS versus KO-CTD mice (Figure 2F and G).
Metabolic disease elicits chronic inflammation, characterized by increased expression of adhesion molecules in the vascular endothelium and accumulation of inflammatory cells in the tissue. We compared the expression of the vascular cell adhesion molecule 1 (VCAM-1) and intercellular adhesion molecule 1 (ICAM-1) on the endothelial surface of coronary arteries and venules in WT and KO mice under HFHS or CTD by immunohistochemistry. At 44 weeks after STZ/vehicle injection, WT-HFHS mice exhibited significantly enhanced VCAM-1 and ICAM-1 expression compared to WT-CTD and to KO-HFHS (Figure 3A–H). Similarly, the number of Mac-2 positive cells was significantly increased in WT-HFHS compared to WT-CTD and KO-HFHS mice (Figure 3I and L). We also assessed whether the expression of the pro-inflammatory cytokine interleukin-1β (IL-1β), a key factor in the inflammation associated with obesity and metabolic syndrome, was modulated in the heart upon HFHS and by NOX1-deficiency. Circulating levels of IL-1β are increased in HFpEF and secretion of mature IL-1β depends on the activation of the NOD-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, which can be activated by NOX-derived ROS. We found significantly more IL-1β- (Figure 3J and M) and NLRP3-positive cells (Figure 3K andN) in the hearts of WT-HFHS compared to both WT-CTD and KO-HFHS mice. We further investigated whether the increased numbers of cells showing inflammatory activation in the hearts of WT-HFHS mice could result from the enhanced mobilization of immune cells to the peripheral blood. We did not find differences in the frequency of peripheral blood monocytes between WT-CTD and WT-HFHS mice at any of the analysed timepoints up to 24 weeks after STZ/vehicle injection. Monocyte frequencies in WT-HFHS and KO-HFHS were also comparable (Supplementary material online, Figure S5A and B). This suggests that the increase in myocardial tissue inflammation upon STZ/HFHS may not arise from differences in blood monocyte mobilization but may depend on other mechanisms that could be endothelium- and/or immune cell-mediated.
Because NOX1 regulates endothelial and immune cell functions, we sorted CD45+ haematopoietic and CD31+ endothelial cells from the heart of WT mice and assessed Nox1 mRNA expression (Figure 4A and Supplementary material online, Figure S6). Nox1 mRNA was primarily expressed in CD45+ cells, whereas CD31+ cells showed undetectable to more than 5-fold lower levels of Nox1 mRNA (Figure 4B). Given that NOX1 has been implicated in macrophage differentiation and function, we further analysed its level of expression in this cell lineage in the healthy heart. We sorted cardiac CD11b+CD64+ cells (Supplementary material online, Figure S6), which in the healthy murine heart contain cardiac macrophages and compared their Nox1 mRNA levels to those of CD64− cells, which comprise all remaining immune cells isolated from the heart. Whereas we could detect Nox1 mRNA in CD64− cells, Nox1 mRNA was undetected in CD11b+CD64+ cells (Figure 4C). These observations suggest that in the healthy heart NOX1 is primarily expressed in immune cells other than cardiac macrophages. We found increased expression of adhesion molecules on the endothelium of WT-HFHS, but not of KO-HFHS hearts. Therefore, even if not notably expressed in endothelial cells in the healthy heart, NOX1 could be upregulated in response to metabolic stress and this could also be the case in macrophages and other cell types represented in the heart. In addition, other isoforms of NOX, in particular NOX2, might also be regulated. In order to test this, we analysed Nox1 and Nox2 mRNA expression in neonatal rat ventricular myocytes (NRVM), human coronary artery smooth muscle cells (HCASMC), mouse bEnd.3 endothelial cells, and human monocytes (THP-1 cells) at baseline and upon exposure to lipopolysaccharide (LPS) or phorbol 12-myristate 13-acetate (PMA, THP-1 cells). Nox1 was significantly upregulated in response to LPS in NRVM and bEnd.3 cells and in response to PMA in THP-1 cells, but not in LPS-stimulated HCASMC (Figure 4C–F). In contrast, Nox2 mRNA could be induced in NRVM and THP-1, but not in bEnd.3 cells (Supplementary material online, Figure S7A–C). These findings support a NOX2-independent role of NOX1 in endothelial cells. To further study the potential mechanism of NOX1-action in our model, we tested whether NOX1 was involved in the regulation of adhesion molecule expression in endothelial cells. We focused on ICAM-1, which was upregulated in cardiac arteries and venules of WT-HFHS compared to WT-CTD mice, but not of KO-HFHS versus KO-CTD mice. Icam-1 mRNA expression was upregulated in bEnd.3 cells in response to LPS stimulation. This upregulation was significantly reduced upon knock-down of NOX1 through siRNA (Figure 4G and H).
Because we found NOX1 upregulated in response to PMA in human monocytes in vitro, we sought to further explore whether NOX1 is regulated in human disease. We therefore sorted CD14+ monocytes from the peripheral blood of a small subset of patients with DD and of age- and sex-matched controls from the HELPFul cohort. Patient characteristics are given in Supplementary material online, Table S3. NOX1 mRNA was robustly expressed in peripheral monocytes from all individuals but was significantly higher in patients with DD (Supplementary material online, Figure S8A), which also showed higher expression of NOX2 expression in these cells (Supplementary material online, Figure S7D). In addition, Nox1 mRNA was also detectable in bone marrow-derived macrophages (BMDMs) from mice and induced upon stimulation with LPS as previously also shown by others (Supplementary material online, Figure S8B).
In a mouse model of metabolic disease, we demonstrate that NOX1 is mandatory for cardiac endothelial activation and myocardial remodelling. Lack of NOX1 correlates with a lower abundance of inflammatory cells in the heart of HFHS/STZ mice, and with less VCAM-1 and ICAM-1 expression in cardiac endothelial cells. We also find that NOX1 is upregulated in peripheral monocytes from patients with DD. These findings provide novel evidence for a central role of NOX1 in myocardial inflammation in metabolic disease, a condition frequently associated with DD in humans. We induced metabolic disease through a combination of HFHS diet with an early single injection of low dose STZ, which resulted in a reproducible phenotype of metabolic disease featuring obesity and early signs of type II diabetes without the need for insulin treatment. This metabolic phenotype was similar in WT and KO mice regarding the degree of weight gain and glucose and insulin elevation. STZ/HFHS mice did not show an increase in systolic, diastolic, or mean arterial pressure as measured invasively after 44 weeks on diet, which is consistent with previous observations in diet-only, STZ, or genetic models of metabolic disease. Our findings therefore support the concept that arterial hypertension is not a pre-requisite for cardiac hypertrophy and also argue against large vessels being the exclusive site of NOX1 action in our model. NOX1 contributes to macrovascular complications in diabetes and metabolic disease. In NOX1-deleted ApoE−/− mice, atherosclerosis formation in the aorta upon STZ-induced diabetes was attenuated and this correlated with less ROS production and reduced macrophage infiltration. NOX1 also plays a role in metabolic microvascular disease. Introducing a genetic deletion of NOX1 in leptin receptor-deficient db/db mice restored endothelial function, myogenic tone, and NO-dependent vascular regulation in mesenteric resistance arteries. Our study expands the role of NOX1 in metabolic disease from the peripheral to the myocardial vasculature, and from atherosclerosis and hypertension to the clinical entity of metabolic heart disease. WT-HFHS hearts exhibited increased expression of the adhesion molecules VCAM-1 and ICAM-1 on endothelial cells, and higher numbers of Mac-2-, IL-1β-, and NLRP3-positive cells in the myocardial tissue. These inflammatory features were not observed in NOX1-deficient mice. Inflammation plays a key role in the pathogenesis of metabolic heart disease and HFpEF. Furthermore, IL-1 has been implicated in DD and clinical trials inhibiting IL-1 in HFpEF have been initiated. The precise mechanisms responsible for the lower inflammation seen in the heart in the absence of NOX1 remain to be established. Our data indicate that up to 24 weeks after STZ there were no differences in the frequencies of blood monocytes between WT- and KO-HFHS mice. Although we cannot exclude that monocyte mobilization may occur at a later time point in our model, our in vitro data suggest that the differences in the cardiac abundance of inflammatory cells may relate to an endothelium-dependent mechanism. Specifically, the lack of endothelial activation in NOX1-deficient mice may prevent immune cell recruitment to the heart, which is supported by the lower expression of ICAM-1 in NOX1-siRNA-treated endothelial cells and by previous observations of a role for NOX1 in oxidative stress, inflammation, and dysfunction in the extracardiac vasculature. Additionally, however, immune cell-intrinsic mechanisms in the absence of NOX1 may lead to impaired cell adhesion and transendothelial migration, or inhibit the expansion of cardiac resident immune cells. Although Nox1 was not detectable in cardiac macrophages in the healthy heart, its expression might be induced under inflammatory conditions. Nox1 was indeed expressed in BMDMs and upregulated in response to LPS, which is consistent with previous reports. Interestingly, we also found that NOX1 was significantly higher expressed in peripheral monocytes from patients with DD compared to controls, although these data were retrieved from a small sample size of patients that were not diabetic. Still, whereas human DD and HFpEF present with many different phenotypes and are associated with various comorbidities, inflammation, endothelial dysfunction, and NO-depletion are known and common denominators of HFpEF in humans. Although our data indicate that NOX1 may be regulated in peripheral monocytes in pro-inflammatory conditions associated with DD and HFpEF in humans, further studies are needed to delineate the precise contribution of NOX1 in macrophages or other inflammatory cells in DD and HFpEF in general and in metabolic heart disease in particular. Superoxide as produced primarily by NOXs reacts with NO to form the highly reactive peroxynitrite, which leads to nitration of protein tyrosine residues. We found increased nitrotyrosinylation at 44 weeks in the hearts of WT, but not of NOX1-deficient metabolic disease mice. This finding is consistent with the hypothesis that NOX1-derived superoxide leads to the consumption of NO through peroxynitrite formation and gives rise to the hypothesis that NO-depletion contributes to the cardiomyocyte hypertrophy in our model. NOX1 could also mediate metabolic heart disease through regulation of vascular smooth muscle cells (VSMCs). In a mouse model of smooth muscle cell-specific deficiency of the NOX1 regulating subunit NOXA1, NOX1 contributed to VSMC activation, ROS production, and VCAM-1 expression in the aorta after endovascular injury and in atherogenesis. However, we found low expression of NOX1 in HCASMC and no regulation in response to LPS, suggesting that NOX1 in VSMCs may not play a major role in our model. Although our data support that NOX1-deficiency is sufficient to prevent endothelial activation and myocardial remodelling in a mouse model of metabolic heart disease and find NOX1 upregulated in peripheral monocytes of humans with DD due to other cause, it has to be pointed out that until now, no NOX1-targeting therapy is available for human use. In addition, potential side effects of systemic, non-isoform-specific NOX inhibition have to be kept in mind. Therefore, therapeutic targeting of NOX1 downstream signalling in cardiovascular and/or immune cells may offer a therapeutic alternative. Interestingly, indirect inhibition of NOX1 was recently suggested as a therapeutic strategy in the context of aging-related cardiac remodelling and HFpEF. This proposition was based on beneficial effects seen in aged mice deficient for G protein-coupled oestrogen receptor (GPER), a molecule that has previously been shown to act as a constitutive activator of NOX1. However, to translate these and our findings to human DD and HFpEF, further studies are needed to identify the roles of NOX1 and to also explore NOX1 up- and downstream signalling in different cell types in human disease.
Our model is based on the constitutive genetic deletion of Nox1. However, such a model may more closely reflect anticipated effects of the pharmacological inhibition of NOX1. Although our data support a central role of the cardiac microvasculature, macrovascular alterations may likewise play a role. Moderately decreased baseline blood pressure has been described in NOX1y/- mice in some studies, but not in others. We did not observe significant differences in blood pressure at 44 weeks between WT and NOX1-deficient mice, nor increase in arterial pressure in metabolic disease, which is consistent with previous reports. Nevertheless, increased aortic stiffness evoked by obesity and metabolic disease and possibly ameliorated by NOX1-deficiency may contribute to the cardiac phenotype. Similarly, we cannot rule out differences in blood pressure in the early stages of metabolic disease. In the healthy mouse heart, Nox1 mRNA was predominantly expressed in CD64− immune cells rather than in cardiac CD11b+CD64+ immune cells. Whether NOX1 is upregulated and contributes to CD11b+CD64+ or other immune cell function in metabolic disease remains to be determined. Finally, human data are retrieved from only a small subset of patients that were not diabetic. Therefore, how precisely NOX1 expression in peripheral monocytes in humans is linked to cardiac disease and to diabetes still needs to be established. In this context, it is also important to point out that whereas our model reliably produced endothelial activation and myocardial remodelling in metabolic disease, it did not reproduce DD or HFpEF as seen in humans.
NOX1 is upregulated in peripheral monocytes in patients with DD and its genetic deletion prevents cardiac endothelial activation, inflammation, and remodelling in metabolic disease in mice. NOX1 or NOX1-dependent signalling could therefore qualify as potential therapeutic target for the treatment of metabolic heart disease or other pro-inflammatory disease states associated with DD.
Supplementary material is available at Cardiovascular Research online.
This work was supported by a grant from the Swiss National Science Foundation (grant number 310030_144208 to G.M.K.), the Foundation for Cardiovascular Research, Basel, the Swiss Diabetes Foundation, and the Medical Division of the Margarete and Walter Lichtenstein Foundation, University of Basel, Switzerland (all to G.M.K.). S.H. was supported by funding from the European Union Commission’s Seventh Framework programme under grant agreement N° 305507 (HOMAGE), the ERA-Net-CVD project MacroERA, 01KL1706, and IMI2-CARDIATEAM (N° 821508) and funding from the Netherlands Cardiovascular Research Initiative CVON, an initiative with support of the Dutch Heart Foundation (Hartstichting), CVON2016-Early HFPEF, 2015-10, CVON She-PREDICTS, 2017-21, and CVON Arena-PRIME, 2017-18. Furthermore, S.H. acknowledges the support of FWO G091018N and FWO G0B5930N. The HELPFul case-cohort study primarily received funding from the Dutch Heart Foundation (Hartstichting) 2013T084 (Queen of Hearts). A comprehensive list of investigators involved in the Queen of Hearts consortium can be found at: http://www.queen-of-hearts.eu. E.L.R. was supported by a CVON-RECONNECT Talent program grant from the Dutch Heart Foundation (Hartstichting). G.B.V. was supported by the RECONNECT program with a grant of the Netherlands Cardiovascular Research Initiative CVON 2014-11 RECONNECT.
Overall conceptualization: L.X., O.P., and G.M.K.; experimental and human study design: L.X., M.B., G.V., S.C.A.d.J., H.M.d.R., S.H., and G.M.K.; data acquisition, analysis, and interpretation of data: all authors; manuscript drafting: L.X., M.B., and G.M.K.; revision for important content: all authors; final approval: all authors; supervision: G.M.K.
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PMC9648824 | 35212715 | Ian R McCracken,Ross Dobie,Matthew Bennett,Rainha Passi,Abdelaziz Beqqali,Neil C Henderson,Joanne C Mountford,Paul R Riley,Chris P Ponting,Nicola Smart,Mairi Brittan,Andrew H Baker | Mapping the developing human cardiac endothelium at single-cell resolution identifies MECOM as a regulator of arteriovenous gene expression | 25-02-2022 | Human cardiac development,Single-cell RNA sequencing,Endothelial heterogeneity,Coronary vasculature formation,MECOM,Vascular regeneration | Abstract Aims Coronary vasculature formation is a critical event during cardiac development, essential for heart function throughout perinatal and adult life. However, current understanding of coronary vascular development has largely been derived from transgenic mouse models. The aim of this study was to characterize the transcriptome of the human foetal cardiac endothelium using single-cell RNA sequencing (scRNA-seq) to provide critical new insights into the cellular heterogeneity and transcriptional dynamics that underpin endothelial specification within the vasculature of the developing heart. Methods and results We acquired scRNA-seq data of over 10 000 foetal cardiac endothelial cells (ECs), revealing divergent EC subtypes including endocardial, capillary, venous, arterial, and lymphatic populations. Gene regulatory network analyses predicted roles for SMAD1 and MECOM in determining the identity of capillary and arterial populations, respectively. Trajectory inference analysis suggested an endocardial contribution to the coronary vasculature and subsequent arterialization of capillary endothelium accompanied by increasing MECOM expression. Comparative analysis of equivalent data from murine cardiac development demonstrated that transcriptional signatures defining endothelial subpopulations are largely conserved between human and mouse. Comprehensive characterization of the transcriptional response to MECOM knockdown in human embryonic stem cell-derived EC (hESC-EC) demonstrated an increase in the expression of non-arterial markers, including those enriched in venous EC. Conclusions scRNA-seq of the human foetal cardiac endothelium identified distinct EC populations. A predicted endocardial contribution to the developing coronary vasculature was identified, as well as subsequent arterial specification of capillary EC. Loss of MECOM in hESC-EC increased expression of non-arterial markers, suggesting a role in maintaining arterial EC identity. | Mapping the developing human cardiac endothelium at single-cell resolution identifies MECOM as a regulator of arteriovenous gene expression
Coronary vasculature formation is a critical event during cardiac development, essential for heart function throughout perinatal and adult life. However, current understanding of coronary vascular development has largely been derived from transgenic mouse models. The aim of this study was to characterize the transcriptome of the human foetal cardiac endothelium using single-cell RNA sequencing (scRNA-seq) to provide critical new insights into the cellular heterogeneity and transcriptional dynamics that underpin endothelial specification within the vasculature of the developing heart.
We acquired scRNA-seq data of over 10 000 foetal cardiac endothelial cells (ECs), revealing divergent EC subtypes including endocardial, capillary, venous, arterial, and lymphatic populations. Gene regulatory network analyses predicted roles for SMAD1 and MECOM in determining the identity of capillary and arterial populations, respectively. Trajectory inference analysis suggested an endocardial contribution to the coronary vasculature and subsequent arterialization of capillary endothelium accompanied by increasing MECOM expression. Comparative analysis of equivalent data from murine cardiac development demonstrated that transcriptional signatures defining endothelial subpopulations are largely conserved between human and mouse. Comprehensive characterization of the transcriptional response to MECOM knockdown in human embryonic stem cell-derived EC (hESC-EC) demonstrated an increase in the expression of non-arterial markers, including those enriched in venous EC.
scRNA-seq of the human foetal cardiac endothelium identified distinct EC populations. A predicted endocardial contribution to the developing coronary vasculature was identified, as well as subsequent arterial specification of capillary EC. Loss of MECOM in hESC-EC increased expression of non-arterial markers, suggesting a role in maintaining arterial EC identity.
See the editorial comment for this article ‘A new resource for human coronary vessel development’, by Ragini Phansalkar and Kristy Red-Horse, https://doi.org/10.1093/cvr/cvac094.
While the formation and homeostasis of the coronary vasculature is essential for heart muscle function, the molecular mechanisms underlying coronary vascular development remain incompletely understood. Previous studies using lineage-tracing tools in mouse have provided much needed insight into these mechanisms, including identifying the endocardium and sinus venosus (SV) as the two major sources of coronary vascular endothelium during cardiac development. A third source, the proepicardium, was previously proposed to contribute a minor population of coronary endothelial cells (ECs), although this notion has recently been challenged. Following the formation of the primitive coronary vascular plexus from these sources and onset of blood flow, subsequent remodelling occurs, giving rise to the distinct EC populations present in the mature vascular bed of the fully developed heart. Recent studies have elegantly mapped the remodelling of the immature coronary EC plexus in mouse cardiac development, including identification of a role for the transcription factor (TF) Dach1 in potentiating developmental arterial remodelling. However, given that these advances in our understanding of coronary vascular development primarily originate from murine lineage-tracing studies, the relevance of these findings for human cardiac development remains largely unknown. Advances in single-cell RNA sequencing (scRNA-seq) have been instrumental in enhancing our understanding of embryonic development, permitting the objective mapping of underlying transcriptional changes at single-cell resolution. In addition, improvements in high-throughput scRNA-seq platforms have facilitated the characterization of tens of thousands of cells in parallel, thus allowing for ‘atlas’ studies to map the gene expression profile of entire organs during embryogenesis. In recent years, such scRNA-seq studies have mapped the transcriptional profile of both murine and human heart development. In the study by Cui et al., scRNA-seq was conducted using cells isolated from specific regions of 18 human foetal hearts, ranging from 5 to 24 weeks gestation. Subsequent dimensionality reduction and clustering analysis revealed an EC cluster of 595 cells characterized by expression of endothelial markers, such as PECAM1. Similarly, a clear EC population was identified in a study from Suryawanshi et al. in which cells isolated from three healthy human foetal hearts (19–22 weeks) were processed using scRNA-seq. Both studies mapped the expression of known EC marker subtypes to allow annotation of clusters corresponding to endocardium, coronary vascular EC, and valvular EC. Nevertheless, the relatively low numbers of EC in these datasets prevented further characterization of cardiac EC subtypes, including the identification of distinct arterial, venous, capillary, and lymphatic populations. In addition, these low EC numbers also prevented the application of methods to infer the dynamic cellular changes accompanying cardiac EC development. While scRNA-seq studies of the developing mouse heart yielded large numbers of EC in their datasets, their analysis focused on other cell types, such as the cardiac conduction system, with minimal interpretation of EC heterogeneity and potential function. These included a study from Goodyer et al., which analysed distinct vascular EC and endocardial cell populations from E16.5 mouse hearts. In this study, we used scRNA-seq to comprehensively map the transcriptional signature of over 10 000 human foetal cardiac ECs isolated by fluorescence activated cell sorting (FACS) from two human foetal hearts at 13- and 14-weeks’ gestation. Unsupervised clustering, gene regulatory analysis, and trajectory inference methods revealed the transcriptional profile of heterogeneous EC populations and predicted dynamic cellular changes including arterial EC specification. In addition, we functionally validated MECOM as a regulator of arterial EC identity, thereby demonstrating the suitability of our novel scRNA-seq dataset to make in-silico predictions, capable of informing future strategies to guide endothelial identity. Collectively, findings from this study complement and expand upon knowledge previously obtained from murine development, bringing insights into human EC heterogeneity and pathways determining specification of subpopulations that are essential for understanding human coronary vascular formation.
Human foetal cardiac tissue was acquired following elective termination of pregnancy. Informed written parental consent was obtained from all participants. Tissue was not collected in cases where termination of pregnancy was conducted due to an identified foetal or pregnancy abnormality. Ethical approval for the collection of foetal tissue was performed in accordance with all relevant guidelines and following study approval from the Lothian Research Ethics Committee (Study code: 08/1101/1) and the Research and Development Office (Study code: 2007/R/RM/10). This study was performed in accordance with the Declaration of Helsinki.
Cardiac ECs were isolated from the ventricular tissue of freshly collected human foetal hearts using a method adapted from van Beijnum et al. Digestion was performed at 37°C using a digestion solution containing 9 mL 0.1% collagenase II and 1 mL of 2.5 U/mL dispase. Then, 75 µL of 1 mg/mL DNaseI was added following 20 min incubation prior to a further 15 min incubation at 37°C. Digestion was quenched by the addition of 10 mL cold RPMI with 10% FCS and undigested clumps of tissue removed using a 100 µM cell strainer. Red blood cell lysis was performed by incubating cells for 2 min in red blood cell lysis buffer at room temperature prior to neutralizing with RPMI + 0.1% BSA. Cells were stained on ice for 45 min with APC anti-human CD31 and PE anti-human CD45 (Supplementary material online, Table S1). CD31+ CD45− ECs were isolated by FACS with DAPI staining being used to allow exclusion of dead cells.
Sorted CD31+ CD45− ECs were counted manually using a haemocytometer with trypan blue staining used to identify non-viable cells. Viability exceeded 85% for both samples. A total of 8000 cells were loaded onto the 10X Chromium controller and library construction conducted using the Single Cell 3’ Reagent Kit (V3.1) in accordance with the manufacturer’s instructions. Libraries were sequenced using the Illumina NovaSeq 6000 platform.
Raw-sequencing data was processed using the 10X CellRanger pipeline (Version 3.1.0.) aligning reads to the GRCh38-3.0.0 genome reference. Barcodes corresponding to cells were distinguished from those corresponding to empty droplets using both the DropUtils package and the default cell calling method applied within the CellRanger pipeline. Cells with a total UMI count exceeding three median absolute deviations (MADs) from the median value were removed from downstream analysis using the R Scater package. Similarly, cells with a high proportion of counts from mitochondrial genes (>3 MADs) or with a low total gene count (<2 MADs) were also excluded. Data normalization was performed using the MultiBatchNormalisation method prior to merging datasets. Normalized count data were then scaled, and principal component analysis (PCA) applied using genes with the most variable expression across the combined dataset. Following batch correction using Harmony, non-supervised clustering was performed, and data visualized using Uniform Manifold Approximation and Projection (UMAP). A small cluster (155 cells) characterized by increased expression of fibroblast/smooth muscle cell markers (ACTA2 and MYH11) and reduced EC marker expression (PECAM1 and CDH5) was removed from the dataset prior to rerunning data normalization, PCA, and data visualization. Significantly differentially expressed genes (DEGs) within individual clusters were identified using the Wilcoxon signed-rank test (Bonferroni corrected P-value <0.05) and a minimum loge(fold change) threshold of 0.3. Additionally, only DEGs expressed in more than 30% of cells within their corresponding cluster were retained for further analysis. Enriched metagene signatures were identified using the R package SCRAT v1.0.0. Gene regulatory analysis was performed using the standard R Single-Cell rEgulatory Network Inference and Clustering (SCENIC) workflow. RNA velocity analysis was conducted using the python package scVelo with the stochastic model being applied to predict the direction and magnitude of cellular dynamics. Trajectory inference tool Slingshot was performed using the standard workflow. Genes significantly differentially expressed over pseudotime were identified using the TradeSeq package with the top 2000 most variably expressed genes in the dataset being used to fit the negative binomial generalized additive model.
Human ESC lines were used in accordance with the UK Stem Cell Bank Steering Committee guidelines (Project Approvals SCS11-51 and SCSC17-26). H9 hESC were differentiated to human embryonic stem cell-derived EC (hESC-EC) as previously described. Small interfering RNA (siRNA)-mediated knockdown of MECOM was performed using Day 7 hESC-EC using predesigned siRNA at a final concentration of 5 nM (Supplementary material online, Table S2). After 6 h, transfection media was replaced with EGM-2 media supplemented with 1% human AB serum and 50 ng/mL VEGF-A. At Day 10, CD144+ hESC-EC were isolated by magnetic activated cell sorting and cell pellets stored at −80°C for subsequent isolation of RNA and protein.
RNA was isolated from Day 10 CD144+ hESC-EC previously subjected to either transfection with siRNA targeting MECOM (siRNA 1) or control siRNA (n = 4 biological replicates). Illumina strand-specific RNA sequencing libraries with PolyA selection were prepared by GeneWiz (New Jersey, USA) and sequenced using the Illumina NovaSeq sequencer to achieve a read depth of 20 million paired end reads per sample. Reads from each sample were mapped and quantified using RSEM (v1.3.0, –bowtie2) and the GENCODE v38 primary assembly transcriptome. Genes with an average FPKM >1 in one or the other experimental group were considered to be expressed. To identify DEGs, tximport (v1.22.0) was used to supply DESeq2 (v1.34.0) with the isoform read counts from RSEM before using the default DESeq2 method (Wald test) to obtain gene-level P-values and fold changes between experimental conditions. Those genes with an absolute fold change value >1.5 (absolute Log2FC value >0.584) and adjusted P-value of <0.05 were considered differentially expressed. Over-represented KEGG terms amongst siMECOM-up-regulated genes were identified using clusterProfiler (v4.2.0) and Benjamini–Hochberg multiple hypothesis correction (P < 0.05). All further experimental and analysis details are included in the Supplementary material online, Methods.
scRNA-seq was performed on CD31+ CD45− cardiac ECs isolated by FACS from ventricular tissue obtained from two human foetuses at 13 and 14 weeks of gestation (Figure 1A and B). At this developmental stage all major structures in the heart have formed, including the coronary vasculature. However, studies from equivalent timepoints in murine development (E15.5–E17.5) have revealed extensive remodelling occurring within the established coronary vasculature producing a mature vascular bed containing heterogeneous EC populations. Following quality control, unsupervised clustering, and UMAP visualization of transcriptomic data from 10 267 cells, 11 distinct clusters (numbered 0–10) were revealed, each with expression of typical pan-EC markers (Figure 1C and Supplementary material online, Figure S1A). Leukocyte marker PTPRC and fibroblast/smooth muscle cell markers ACTA2 and MYH11 demonstrated negligible expression across all clusters (Supplementary material online, Figure S1A). Annotating cells by sample demonstrated successful integration of datasets, with each cluster containing cells from both samples (Supplementary material online, Figure S1B). Expression of NPR3, a known endocardial marker, was localized to cluster 4 (Figure 1D and Supplementary material online, Figure S1C). Clusters 0, 1, 2, 5, and 6 were defined by expression of capillary marker RGCC, whilst arterial marker, HEY1, was expressed predominantly in clusters 2, 3, and 9 (Figure 1D and Supplementary material online, Figure S1C). Metagene analysis revealed five major signatures (A–E) indicating five key populations within the data (Figure 1E and Supplementary material online, File S1). Signature A was enriched in clusters 2, 3, and 9, and included genes involved in arterial EC function, such as JAG1, DLL4, and HEY1 (Figure 1E). Furthermore, Gene Ontology (GO) term enrichment analysis using signature A genes identified ‘artery morphogenesis’ and ‘positive regulation of Notch signalling pathway’ as significantly enriched terms (Figure 1F). Notch signalling is known to be required for arterial EC specification. Signature B was predominantly enriched in Cluster 4 and contained known endocardial markers CDH11 and NPR3 (Figure 1E and F). Lower levels of signature B enrichment were also observed in Clusters 7, 8, and 9 (Figure 1E). Clusters 0, 1, and 5 were enriched for signature C, which included capillary EC marker genes, such as CA4 and RGCC (Figure 1E and Supplementary material online, Figure S1D). Cluster 10 was enriched for signature D, for which GO term analysis returned terms relating to lymphatic EC (LEC) (Figure 1E and Supplementary material online, Figure S1D). LEC markers,LYVE1, FLT4, PROX1, and PDPN, were differentially expressed in Cluster 10 (Supplementary material online, Figure S1G). Signature E was selective for Cluster 6 with GO term analysis identifying enriched terms relating to proliferation (Supplementary material online, Figure S1E). In line with this, categorizing cells according to their predicted cell cycle phase revealed that 77% of cells in Cluster 6 were predicted to be in the G2M phase of rapid growth, whereas 95% of cells in cluster 5 were in S phase (Supplementary material online, Figure S1F). The remaining cells in the dataset were predominantly in the G1 phase (79%), with only a small proportion predicted to be in G2M (4%) and S phases (17%) (Supplementary material online, Figure S1F). For clusters not associated with a metagene signature, analysis of DEGs revealed enrichment of NR2F2 and ACKR1 in Cluster 7, suggesting a venous/venular EC identity (Figure 2A and Supplementary material online, Figure S1G). A valvular identity of Cluster 8 was supported by its differential expression of NFATC1 and BMP4, both with known roles in valvulogenesis (Supplementary material online, Figure S1G). Collectively, these analyses demonstrate that each major subtype of EC within the heart (endocardial, venous, lymphatic, capillary, arterial, proliferating, and valvular EC) is represented by one or more of the identified 10 clusters.
Global differential gene expression analysis revealed markers for subpopulations of arterial and capillary cardiac EC (Figure 2A and Supplementary material online, File S2). Notably, Cluster 2, an arterial EC population, is more closely correlated with capillary EC clusters (0 and 1) than with the other two minor arterial clusters (3 and 9) (Supplementary material online, Figure S2A). Together with the co-expression of both arterial and capillary markers, this suggested that Cluster 2 represents an arterial microvascular population. In contrast, arterial clusters 3 and 9 DEGs associated with ECM organization (FBLN5, ELN, and FBN1) and shear stress (KLF4) suggesting a macrovascular identity (Supplementary material online, Figure S2B and C). Expression of fatty acid translocase encoding CD36 was absent from macrovascular and endocardial populations, corresponding with a previous report of microvasculature-restricted expression (Figure 2A and B). Differential gene expression analysis revealed heterogeneity between the two major capillary clusters, 0 and 1. Cluster 0 was defined by differential expression of amine methyltransferase encoding gene, INMT, whilst KIT expression was enriched in Cluster 1 (Figure 2A and B). Selective expression of KIT (also known as C-KIT) in Cluster 1 was accompanied by up-regulated expression of the TF SMAD1 (Supplementary material online, Figure S2D and E). Gene regulatory network (GRN) analysis was applied to identify gene modules, known as regulons, predicted to be controlled by an individual TF, giving insight into the likely transcriptional regulators of EC heterogeneity. Visualization of differentially expressed TFs and their predicted targets within the GRN largely recapitulated the data structure observed following unsupervised clustering (Figure 2C). Genes differentially expressed in LEC (Cluster 10) localized together in the GRN and included TFs, such as PROX1, a known master regulator of LEC identity, as well as HOXD9 and TBX1 (Figure 2C). A set of differentially expressed endocardial TFs (Cluster 4) was evident, including GATA6, MEIS2, and FOXC1, as well as GATA4, known to be implicated in endocardial cushion development.MECOM and MAFF were located amongst known regulators of arterial EC specification, such as HEY1 and SOX17 (Figure 2C). A distinct cluster of genes differentially expressed in KIT1+ capillary EC (Cluster 1) included SOX4 and SMAD1 (Figure 2C). Enrichment of SOX4 and SMAD1 regulons was also observed in KIT1+ capillary EC (Figure 2D).
Several recent studies using murine models of coronary vascular development have provided insight into the origin of the coronary endothelium and the dynamic changes that occur during its subsequent remodelling. Endocardial-derived vessels vascularize the heart from the inside-out contributing to vessels of the interventricular septum and inner myocardial wall. Conversely, SV-derived vessels populate the outer ventricular free walls of the heart from the outside-in. Following the formation of the primitive coronary vascular plexus, EC undergo further remodelling to form a functional network of veins, arteries, and capillaries. We used trajectory inference methods to determine whether these processes could be identified during human cardiac development and to characterize their accompanying transcriptional changes. Given that these dynamic changes are known to originate from microvascular EC, we excluded the two previously identified arterial macrovascular clusters (Clusters 3 and 9) from the dataset and performed secondary clustering of the remaining cells (Figure 3A). The distinct LEC cluster was also excluded prior to re-clustering. The same marker genes used for annotating the complete dataset were used for the annotation of re-clustered data (Supplementary material online, Figure S3A). RNA velocity analysis, which utilizes the ratio of spliced to unspliced transcripts to infer the direction and magnitude of cellular transitions, was first used to gain an overview of the pseudotemporal dynamics of the foetal cardiac endothelium (Figure 3A and Supplementary material online, Figure S3B). We identified a proportion of the endocardial cluster with velocity vectors indicating a probable transition towards a venous identity (Figure 3A and Supplementary material online, Figure S3B). Evidence for this transition was further supported by venous EC-associated genes, such as PLVAP and NR2F2, having positive residuals/velocities in endocardial cells (Supplementary material online, Figure S3C). In turn, venous EC were subsequently predicted to transition to INMT+ capillary EC. This predicted transition of endocardium to coronary vascular EC concurs with studies that identified the endocardium as a significant source of EC for the coronary vasculature. To further substantiate this finding, cells belonging to endocardial, venous, and INMT+ capillary clusters were isolated in silico and re-clustered. UMAP visualization of re-clustered data revealed a comparable result to previous analysis with endocardial and INMT+ capillary populations connected by a ACKR1+ venous population (Supplementary material online, Figure S3D). Additionally, the omission of cell cycle-related genes from clustering and visualization calculations generated a comparable finding, thus confirming localization of identified clusters was not confounded by cell cycle-related effects (Supplementary material online, Figure S3E). Velocity analysis also predicted a likely transition of both capillary EC populations towards an arterial EC fate (Figure 3A and Supplementary material online, Figure S3B). This is an agreement with previous reports of developmental arterial remodelling in mouse. In addition to the RNA velocity analysis, we also independently applied the trajectory inference tool Slingshot, which yielded a comparable interpretation (Figure 3B). Identification of the top 200 genes with most variable expression over pseudotime revealed four temporal patterns of expression, arranged in Modules 1–4 (Figure 3C and D). Average sample module gene expression was visualized over pseudotime to ensure concordant expression dynamics between individual samples. Module 2 genes were expressed early in pseudotime with their reduction in expression occurring in conjunction with the loss of endocardial identity (Figure 3D). These included known markers of endocardium, such as CDH11 and NPR3 as well as the TFs DKK3 and GATA6 (Figure 3E and Supplementary material online, Figure S4A). TFs CEBPD and FOS were identified in Module 1 along with NR2F2, a known regulator of venous EC specification (Supplementary material online, Figure S4A). Interestingly, despite not being identified within Module 1, expression of BMP2 was found to increase within the pseudotime range corresponding to the predicted transitioning venous population (Supplementary material online, Figure S4B). As well as demonstrating enriched expression in venous EC in zebrafish, BMP2 has also recently been identified as positive regulator of endocardial to coronary vascular EC transition during murine cardiac development. Module 4 genes demonstrated peak expression within IMNT+ capillary EC and included TFs TCF15 and MEOX1 (Figure 3E). Expression of DACH1 was found to peak within the IMNT+ capillary cluster before gradually decreasing again within the arterial population (Supplementary material online, Figure S4C). Previous studies have identified Dach1 as a driver of developmental arterial remodelling in murine cardiac development. The predicted transition of capillary EC to arterial EC was defined by increased expression of Module 3 genes (Figure 3C and D). This included HEY1, known to mediate arterial EC specification. Interestingly, Module 3 also contained the TF MECOM, earlier predicted by GRN analysis to underlie arterial EC identity (Figures 2C and 3C–E and Supplementary material online, Figure S4A). Subsequent in situ hybridization (ISH) validation conducted across four independent foetal hearts (aged 13–14 weeks) demonstrated clear co-expression of MECOM with the arterial EC enriched TF HEY1 within arterial vessels (Figure 3F and Supplementary material online, Figure S5A and B). Notably, a lack of MECOM expression was observed in vessels with venous morphology validating its arterial EC specificity (Figure 3F and Supplementary material online, Figure S5A and B). In addition, reanalysis of publicly available scRNA-seq data from healthy human foetal heart data from Suryawanshi et al. revealed MECOM expression to be enriched within a subset of the endocardial/endothelial population with minimal expression in other identified cell types (Supplementary material online, Figure S6A–C).
Our current understanding of cardiac vascular development is derived predominantly from murine models. Consequently, we next compared the transcriptional profiles of developing foetal human and embryonic mouse cardiac EC. For this comparison, a publicly available mouse embryonic heart scRNA-seq dataset was used due to its good representation of cardiac EC and because its embryonic stage (E16.5) corresponded with the later developmental stage of our human foetal heart data (13–14 weeks). Dimensionality reduction revealed successful integration of mouse and human cardiac EC (Supplementary material online, Figure S7A). Unlike our observation in the human heart, no distinct populations of KIT+ or INMT+ capillary populations were observed in the mouse data (Supplementary material online, Figure S7B). Clusters were therefore merged to represent the major subtypes of EC within the heart (endocardial, venous, lymphatic, capillary, arterial, proliferating, and valvular EC). Genes found to be amongst the most significantly differentially expressed in the same population in both human and mouse were classified as conserved markers (Figure 4A). MECOM and UNC5B were among genes with enriched arterial EC expression in both species (Figure 4A and B). In agreement with previous findings in mouse,NPR3 expression was highly specific to the endocardial population with minimal expression in valvular EC. A lack of clear conserved venous EC markers was observed, with partial overlap of DEG in some lymphatic, valvular, and endocardial populations (Figure 4A and B). Interestingly, whilst the known LEC TF, PROX1, was expressed in both valvular EC and LEC in both species, PTX3 and a LEC marker, LYVE1, were found to be highly LEC specific (Figure 4A and B). Species-specific markers were also identified for each EC dataset (Figure 4A and Supplementary material online, Figure S7C). A human endocardial marker, NPCC, described elsewhere as specifically defining human foetal cardiac endocardium, was not enriched in the corresponding mouse population (Figure 4C).
Given the in-silico predictions of a role for MECOM in arterial fate and enriched MECOM expression in arterial EC for both human and mouse, suggesting an evolutionarily conserved role in arterial EC, we sought to validate its role in determining human arterial EC identity. Our previous scRNA-seq-based characterization of our 8 day hESC-EC differentiation protocol demonstrated its suitability as an in vitro model of human EC development. Additionally, we determined that after acquisition of an early EC identity by Day 6, hESC-EC assume a clear arterial-like EC transcriptional signature by Day 8, characterized by expression of arterial markers, such as SOX17 and DLL4 (Supplementary material online, Figure S8A). Expression of venous (NR2F2 and EPHB4) and lymphatic markers (PROX1) in hESC-EC was low by Day 8 of the differentiation (Supplementary material online, Figure S8A). Notably, in agreement with its arterial EC specificity, MECOM was specifically expressed in hESC-EC at Days 6 and 8 of differentiation (Supplementary material online, Figure S8A). Using hESC-EC as a developmental model for arterial EC specification, we next determined whether siRNA-mediated MECOM knockdown in hESC-EC resulted in changes to their arteriovenous identity (Figure 5A). Significant knockdown (>50%) of MECOM was observed at the RNA and protein level in hESC-EC 72 h after siRNA transfection (Figure 5B and Supplementary material online, Figure S8B and C). Bulk RNA-sequencing analysis revealed a distinct transcriptional profile for hESC-EC following MECOM knockdown compared to that of control hESC-EC (Supplementary material online, Figure S8D). Differential gene expression analysis demonstrated a reduction of MECOM resulted in a global increase in expression of non-arterial markers, including NR2F2 (Log2FC = 1.89) and VWF (Log2FC = 0.95) known to be enriched in venous EC (Figure 5C and Supplementary material online, File S3). Notably, known arterial markers including HEY1 and DLL4 were found not to be significantly down-regulated in response to MECOM knockdown. In addition to the up-regulation of known venous markers, several genes with previously reported differential expression in LEC including LYVE1 (Log2FC = 1.29), STAB2 (Log2FC = 4.05), and CEACAM1 (Log2FC = 1.74) were also found to be significantly up-regulated (Figure 5C). However, expression of key LEC TF PROX1 was not detected in either condition. Application of an up-regulated gene expression signature (constructed using the top 20 up-regulated genes following MECOM KD in hESC-EC) to our foetal cardiac EC scRNA-seq dataset revealed the lowest level of signature enrichment in arterial populations, with highest levels of enrichment observed in lymphatic and venous clusters (Figure 5D). KEGG pathway enrichment analysis conducted using genes significantly up-regulated following MECOM KD identified enrichment of genes belonging to the PI3K-AKT signalling pathway, reported to play a role in venous EC specification (Figure 5E). qRT–PCR validation aligned with bulk RNA-seq findings demonstrating knockdown of MECOM resulted in significant up-regulation of venous EC markers (NR2F2 and EPHB4) whilst arterial (HEY1, DLL4, JAG1, and JAG2) markers remained unchanged (Figure 5F and Supplementary material online, Figure S8E). Importantly, reduction of MECOM did not result in altered expression of the pan-endothelial marker, CDH5, suggesting that the changes observed in arteriovenous marker expression are not due to a loss of general EC identity (Supplementary material online, Figure S8E).
In this study, we comprehensively mapped the transcriptional landscape of the developing human foetal heart endothelium using scRNA-seq. Isolation of foetal cardiac EC by FACS prior to performing high-throughput scRNA-seq empowered this study to identify the full extent of EC heterogeneity. This included identifying distinct endocardial, valvular, venous, capillary, and arterial EC populations each expressing a separate transcriptional signature. GRN analysis identified the TFs most likely responsible for establishing the observed EC heterogeneity. Application of trajectory inference methods to microvascular ECs was used to map the cellular dynamics accompanying coronary vascular EC development. This revealed a small proportion of endocardial cells that appeared to transition to a vascular EC identity via a venous EC population. In addition, capillary EC was predicted to be undergoing specification to assume an arterial EC identity, defined by increasing expression of the TF, MECOM. Comparison of our human foetal heart EC data with E16.5 murine cardiac EC scRNA-seq data demonstrated the existence of several conserved, as well as species-specific, markers for each of the major cardiac EC populations. This included identifying NPR3 and MECOM as conserved markers of endocardial and arterial populations, respectively. Finally, we demonstrated that loss of MECOM in arterial-like hESC-EC resulted in a global increased expression of non-arterial markers, suggesting a function to maintain identity in arterial EC. In contrast to the capillary EC cluster defined by differential expression of methyltransferase-encoding gene, INMT, GRN analysis revealed several regulons enriched within the KIT+ capillary population. This included the SMAD1 regulon. BMP/SMAD1 signalling has been demonstrated to promote angiogenesis whilst KIT/C-KIT has been shown to mediate neovascularization in retinal microvascular ECs in response to hypoxia. This suggests that SMAD1 may mediate angiogenesis within hypoxic regions in the developing heart wall, although this will require further investigation to verify. The existence of two capillary populations with distinct transcriptional signatures, including the differential expression of KIT and INMT, was recently confirmed in an independent study from Phansalkar et al., which performed low-throughput scRNA-seq on EC isolated from 11-, 14-, and 22-week human foetal hearts. Our in-silico findings suggested a transition of endocardium to coronary vascular endothelium. This is consistent with previous findings from murine lineage-tracing studies, in which a proportion of the endocardium gives rise to coronary vascular EC via angiogenic sprouting. A second method of endocardial-derived coronary vessel formation during was also proposed to occur at the murine perinatal stage and involve the formation of new coronary vessels by the segregation of endocardial trabeculae protruding into the myocardium during compaction. However, this model has recently been challenged by Lu et al., which concluded that formation of new coronary vessels during the perinatal stage is instead due to angiogenic expansion of the pre-existing coronary plexus. Although our trajectory analysis indicated the transition of endocardium to coronary vasculature occurs via a venous EC population, the arteriovenous identity of cells undergoing this process has not previously been explored. Whilst studies in mouse have demonstrated a significant proportion of coronary vascular EC to be derived from venous cells of the SV, this is thought to occur much earlier in cardiac development than the comparative gestational age of the human foetal samples used in this analysis. However, the observed enrichment of BMP2 expression within the identified venous cluster aligns closely with recent scRNA-seq evidence from D’Amato et al. identifying Bmp2 as a marker of the transitioning endocardial population in E12 mouse embryos. Additionally, enriched venous expression of bmp2 has previously been described in zebrafish, thus, further indicating the identified venous cluster may represent a transitioning endocardial-derived population. Trajectory inference analysis also revealed subsequent arterial specification of capillary EC. This predicted cellular transition in the human foetal heart was also recently identified by Phansalkar et al., thus, collectively providing human relevance to current understanding of coronary artery development derived from murine studies. However, in addition to confirming the up-regulation of known mediators of arterial specification, such as HEY1 and SOX17,MECOM was also identified as having a role in the establishment of an arterial EC identity. Furthermore, enriched arterial expression of MECOM was also observed in coronary EC from E16.5 mouse hearts, suggesting an evolutionarily conserved function. The localization of MECOM in the developing human heart was validated using ISH methods and its function in arterial EC identity demonstrated by siRNA-mediated knockdown in arterial-like hESC-EC. Previous work from Li et al. demonstrated that MECOM acts upstream of Notch signalling during zebrafish nephrogenesis. Given the importance of Notch signalling in arterial EC specification, this suggested that MECOM may alter arteriovenous identity by regulating Notch signalling. Whilst reduction in MECOM expression in hESC-EC did not alter the expression of arterial EC markers, including those belonging to the Notch pathway, a global increase in the expression of non-arterial-enriched genes was observed. This included the TF, NR2F2, which is known to establish venous identity, in part via repression of Notch signalling. Although a subset of genes up-regulated following MECOM knockdown have been reported to be differentially expressed in LEC, the absence of increased PROX1 expression indicated the reduction in MECOM does not specify EC towards a lymphatic identity. Collectively, these findings suggest that MECOM may be required to supress non-arterial gene expression during arterial EC specification. Goyama et al. previously demonstrated that loss of MECOM within Tie2+ cells results in severe vascular abnormalities leading to embryonic lethality in mouse between E13.5 and E16.5. However, considering our described findings, further investigation is required to characterize MECOM expression across the murine embryonic and adult coronary vascular endothelium, as well as to evaluate the resultant effect of EC-specific loss of MECOM on arteriovenous identity. Previous studies from mouse have hypothesized that a venous identity is the default state for EC, with venous identity needing to be repressed via Notch signalling during arterial specification. Our finding that MECOM knockdown altered venous marker (NR2F2 and EPHB4) expression, without changes to expression of Notch signalling genes, suggests that additional factors are required to supress venous identity during human arterial EC specification. However, further studies simultaneously targeting the expression of characterized arterial EC regulators is required to determine the position of MECOM within the hierarchal network of arteriovenous regulators. Previous findings in mouse demonstrated overexpression of arterial EC regulator Dach1 resulted in an increase in perfused arteries following myocardial infarction. Our finding from human data suggesting MECOM may function to maintain the transcriptional identity of arterial EC highlights it as a prime therapeutic candidate to drive arterialization in cardiovascular disease. Although this study is the most comprehensive of its type to date, due to limited sample availability its data provides only a snapshot of a narrow developmental window (13–14 weeks). This limitation prevented the comparison of gene expression and cluster proportion between different gestational ages. Careful batch correction and visualization of gene expression dynamics across pseudotime for individual samples ensured findings from trajectory inference analysis were not biased by unequal representation of individual clusters. Whilst trajectory inference methods permit the dynamical changes to be characterized within individual datasets, inclusion of foetal samples from a wider range of gestational ages would provide a more comprehensive understanding of human coronary vascular development, especially at earlier stages. In summary, we have used a high-throughput scRNA-seq platform to comprehensively map the transcriptional landscape of the human foetal heart endothelium at 13–14 weeks. This study complements studies using murine models of cardiovascular development by providing novel insight into EC heterogeneity within the developing human heart, as well as the dynamical changes accompanying coronary vasculature formation. In addition to helping understand the mechanisms giving rise to congenital coronary vascular abnormalities, this information may prove valuable in future strategies to guide coronary vascular formation for the treatment of coronary vascular disease.
Supplementary material is available at Cardiovascular Research online.
I.R.M., J.C.M., N.S., M.B., and A.H.B. were involved in the design of the described study. I.R.M. and R.D. carried out foetal tissue collection and sample processing. Bioinformatic analysis was performed by I.R.M. and M.B. In-vitro experiments, qRT–PCR, and western blotting analysis were conducted by I.R.M., R.P., and A.B. A.H.B., C.P.P., and M.B. supervised the research. A.H.B. secured research funding. I.R.M., M.B., N.C.H., J.C.M., P.R.R., C.P.P., N.S., M.B., and A.H.B. were involved in interpreting bioinformatics data. I.R.M. and A.H.B. wrote the manuscript with input from all authors. All authors discussed the data and edited the manuscript.
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PMC9648829 | 34662387 | Christine M Loescher,Anastasia J Hobbach,Wolfgang A Linke | Titin (TTN): from molecule to modifications, mechanics, and medical significance | 18-10-2021 | Heart failure,Cardiomyopathy,Sarcomere,Mechanical function,Signalling | Abstract The giant sarcomere protein titin is a major determinant of cardiomyocyte stiffness and contributor to cardiac strain sensing. Titin-based forces are highly regulated in health and disease, which aids in the regulation of myocardial function, including cardiac filling and output. Due to the enormous size, complexity, and malleability of the titin molecule, titin properties are also vulnerable to dysregulation, as observed in various cardiac disorders. This review provides an overview of how cardiac titin properties can be changed at a molecular level, including the role isoform diversity and post-translational modifications (acetylation, oxidation, and phosphorylation) play in regulating myocardial stiffness and contractility. We then consider how this regulation becomes unbalanced in heart disease, with an emphasis on changes in titin stiffness and protein quality control. In this context, new insights into the key pathomechanisms of human cardiomyopathy due to a truncation in the titin gene (TTN) are discussed. Along the way, we touch on the potential for titin to be therapeutically targeted to treat acquired or inherited cardiac conditions, such as HFpEF or TTN-truncation cardiomyopathy. | Titin (TTN): from molecule to modifications, mechanics, and medical significance
The giant sarcomere protein titin is a major determinant of cardiomyocyte stiffness and contributor to cardiac strain sensing. Titin-based forces are highly regulated in health and disease, which aids in the regulation of myocardial function, including cardiac filling and output. Due to the enormous size, complexity, and malleability of the titin molecule, titin properties are also vulnerable to dysregulation, as observed in various cardiac disorders. This review provides an overview of how cardiac titin properties can be changed at a molecular level, including the role isoform diversity and post-translational modifications (acetylation, oxidation, and phosphorylation) play in regulating myocardial stiffness and contractility. We then consider how this regulation becomes unbalanced in heart disease, with an emphasis on changes in titin stiffness and protein quality control. In this context, new insights into the key pathomechanisms of human cardiomyopathy due to a truncation in the titin gene (TTN) are discussed. Along the way, we touch on the potential for titin to be therapeutically targeted to treat acquired or inherited cardiac conditions, such as HFpEF or TTN-truncation cardiomyopathy.
The heart needs to continually pump throughout a lifetime and meet the ever-changing requirements of the body. Some changes take place relatively slowly throughout development, ageing, and disease, while others occur almost on a beat-to-beat basis. How the heart adapts to these changes continues to be the subject of an entire research field, as many details remain incompletely understood. For example, increased ventricular filling and pressure induce stretch and stress signalling pathways within the heart, which are complex, intertwined in many ways, and challenging to study—notably in vivo. At the level of the cardiac cells, a myriad of proteins has been identified which participate in mechanical and chemical signalling in various ways. The focus of this review is the mechanosensitive protein titin in the sarcomeres, the contractile units of the cardiomyocytes (CMs). We zoom in on titin’s properties as a molecular spring whose elasticity can be extensively modulated under physiological and pathophysiological conditions, resulting in larger modulations in cardiac function, such as diastolic filling or length-dependent activation (LDA), the basis of the Frank-Starling law. Titin has several mechanisms it can exploit to physiologically modulate myocardial stiffness and distensibility, including post-translational modifications (PTMs, e.g. acetylation, oxidation, and phosphorylation) and isoform switch (Figure 1). Other possibilities to fine-tune the mechanical properties of the cardiac sarcomere/CMs/heart involve the binding of specific titin regions to Ca2+, chaperones, or protein ligands, such as actin (not reviewed here, but see Ref.). However, titin’s giant size and complexity leaves it vulnerable to dysregulation, which can result in the development of cardiac disease. To date, the dysregulation of titin, its binding partners, and associated signalling pathways have been implicated in various forms of heart disease, such as heart failure (HF) with preserved ejection fraction (HFpEF), HF with reduced ejection fraction (HFrEF), aortic stenosis, and ischaemic injury (Figure 1). Moreover, next-generation sequencing has revealed pathogenic variants in the titin gene (TTN) as a major cause of inherited cardiomyopathies, the pathomechanisms of which have only recently become clearer, as discussed further below in this review. The overall goal of our review is to explore how cardiac titin properties can be changed at a molecular level, with an emphasis on titin stiffness and protein quality control (PQC), and how this regulation becomes unbalanced in heart disease. We also highlight new insight suggesting that titin can be therapeutically targeted to help treat acquired or inherited cardiac conditions, such as HFpEF or TTN-truncation cardiomyopathy.
Spanning the half-sarcomere from the Z-disc to the M-band as the ‘third’ sarcomeric filament system (along with myosin and actin filaments), titin is well placed to assist in the regulation of both passive and, to some extent, active force development of the heart (Figure 2A). The intrinsic properties of titin vary along its length, depending on the location within the sarcomere: A-band titin is bound to myosin filaments and is functionally inextensible, I-band titin lays slightly oblique between the Z-disc and the I/A-band junction and is elastic, and the ends of the titin filament are anchored at the Z-disc (via α-actinin, actin, and telethonin) and M-band (via myomesin). The specific amino acid sequences and domain structures in each region determine the structural and functional roles of these titin segments. A-band titin is largely comprised of super-repeats of relatively stable fibronectin-type 3 and immunoglobulin-like (Ig) domains providing a template for thick-filament assembly and A-band length, while the M-band region engages in protein–protein interactions that serve structural and regulatory functions. I-band titin is variable and contains several extensible elements, such as the PEVK-repeats (motifs rich in proline, glutamic acid, valine, and lysine), Ig-domain regions, and unique sequences, notably the N2B-unique sequence (N2Bus), enabling titin’s spring-like properties. Unique sequence elements are also present in Z-disc and M-band titin. Part of the variability within I-band titin comes from the high degree of differential splicing that occurs in this region. Of note, this continues to cause inconsistencies with the nomenclature when assigning specific domain numbers to I-band titin. Further adding to the confusion is that commonly referred-to databases for human titin, such as UniProtKB and NCBI (canonical sequence accession numbers, Q8WZ42-1 and NP_001243779.1, respectively), falsely recognize some PEVK-repeats or globular Ig domains as insertion sequences. Human TTN has 364 exons and the complete meta-transcript 363 exons, from which several common transcripts (isoforms) are generated (Figure 2B). A good overview is provided at http://cardiodb.org/titin. Because the frequency at which an exon is incorporated into the titin molecule varies greatly in the elastic I-band region, the different isoforms have different spring length and variable compliance. The principal cardiac isoforms are the Z-disc-anchored N2B, N2BA, and Novex-3 variants, as well as the recently identified C-terminal Cronos isoform driven by an internal promoter (Figure 2B). On a typical Coomassie-stained titin-protein gel of human heart tissue (Figure 2C), these isoforms appear together with a proteolytic fragment, T2 (2.4 MDa). The two most common cardiac isoforms, N2B (3 MDa) and N2BA (3.2–3.8 MDa), determine titin extensibility and myofibrillar passive stiffness. The N2BA isoforms exist in many splice variants that have different I-band length. The N2B isoform (named after the N2B element coded by TTN exon 49) is shorter and less compliant than the N2BA isoforms, as its spring segment has only 6 PEVK-repeats, no ‘middle’-Ig domains, and no N2A element (Figure 2B). The short isoform Novex-3 (∼650 kDa), which splices-in exon 48 (thus introducing a stop), does not appear to be relevant for titin stiffness; however, the exon 48-encoded Novex-3 region may have a role as a structural and regulatory element, e.g. through its interaction with obscurin. Cronos (2.3 MDa; close in size to the T2 fragment) is more highly expressed in developing CMs than in adult hearts, where it makes up only ∼10% of the total titin-protein pool (human adult hearts). In human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), Cronos has been shown to enable partial sarcomere formation in the absence of full-length titin. However, Cronos failed to rescue cardiac sarcomerogenesis in adult mice lacking Z-disc-anchored titin. Moreover, Cronos transcript and protein content were found to be unaltered in end-stage failing hearts from dilated cardiomyopathy (DCM) patients vs. non-failing human donor hearts.
The titin N2B vs. N2BA isoform expression pattern is mediated, at least in part, by the splicing factor RNA-binding motif protein-20 (RBM20). RBM20 suppresses the splicing-in of I-band titin exons, thereby promoting expression of the shorter, stiffer N2B isoform (Figure 2D, inset). RBM20 is also regulated by titin splicing itself: spliced-out regions of TTN can form functional motifs of circular RNA, such as cTTN1, which regulate the activity of RBM20 and that of another splicing factor, SRSF10. RMB20-deficient rats or mice have been found to produce aberrantly large N2BA-isoforms and no N2B, resulting in reduced myocardial passive stiffness and dilated ventricles. In humans, RBM20 pathogenic variants are associated with the expression of oversized N2BA isoforms, greater titin compliance, and irregular calcium homeostasis leading to the development of DCM. Interestingly, both insulin and thyroid hormone, T3, modulate RBM20 expression and activity pathways. This could explain why T3 and insulin (and also Angiotensin II) promote the expression of the N2B titin isoform, e.g. during heart development. Long and compliant, foetal N2BA titin isoforms render titin-based stiffness very low in the embryonic heart, but a transition to shorter and less stretchable (adult) N2BA isoforms and N2B in the pre-/perinatal period greatly increases this stiffness (Figure 2D). Thus, long-term cardiac stiffness regulation can be achieved by changing the relative abundance of the N2BA and N2B isoforms, known as isoform switching.
Titin–isoform transitions also regulate titin-based stiffness in heart disease. A healthy adult human heart (left ventricle) has an N2BA: N2B ratio of 30:70 to 40:60, and this ratio remains relatively constant during normal ageing. Isoform switching occurs in many forms of HF, including HFrEF (e.g. DCM or chronic ischaemic heart disease), HFpEF, and aortic stenosis (Figure 2D). Various studies have found that relatively more N2BA and less N2B isoform is present in failing vs. non-failing human hearts, such as in HFrEF, HFpEF, and aortic stenosis. An increased proportion of N2BA is correlated with an increased end-diastolic volume and lower titin-based passive stiffness (Figure 2D). However, earlier studies reported a reduced N2BA:N2B ratio in aortic stenosis and HFpEF patients, whereas no change in titin–isoform composition was found in human hypertrophic cardiomyopathy and sometimes even in human DCM or aortic stenosis. Conversely, the N2BA:N2B ratio has sometimes (e.g. Refs45,46) but not consistently been found to be reduced in animal models of HF. These discrepancies may be related to the stage of the heart disease during which the cardiac muscle samples were studied, the type of HF syndrome, species characteristics, heart chamber-specific differences, and/or methodological limitations. It is still incompletely understood whether titin–isoform switch causes or compensates for cardiac stiffness changes that occur during disease progression. Our knowledge of the influence titin–isoform switch can have on cardiac stiffness has made it a potential therapeutic target for the treatment of HF. Successful attempts have been made to modulate the titin–isoform composition in animal models by reducing RBM20 expression, increasing the N2BA to N2B ratio and lowering cardiac stiffness. However, RBM20 is responsible for the splicing of many other cardiac proteins and the reduction of RBM20 will have consequences for the function of at least some of them, including crucial Ca2+-handling proteins. The lack of titin specificity therefore limits the therapeutic scope of RBM20 inhibition. Regardless, targeting titin–isoform switching might improve prognosis and symptoms of some HF patients, potentially making it a therapeutic approach in the future.
The mechanical properties of titin not only determine a significant portion of CM passive stiffness but also regulate active contraction. A prime example is LDA, which is characterized by an immediate increase in the Ca2+ sensitivity of the myofilaments with a CM stretch. A consistent observation is the correlation between I-band titin length or titin-based stiffness and LDA: CMs with a short/stiff titin spring show a larger increase in myofilament Ca2+ sensitivity upon cell stretching than those with a long/soft titin spring. Similarly, RBM20-deficient rat CMs expressing long and very compliant N2BA titin have a blunted LDA, whereas mouse CMs lacking extensible I-band titin regions show increased titin-based stiffness and improved LDA compared to wildtype (wt) CMs. Therefore, the titin–isoform switch towards more compliant N2BA present in failing hearts is expected to reduce LDA and (via the Frank–Starling mechanism) cardiac output. These and other observations have suggested that stretch effects mediated by titin cause alterations to both thick- and thin-filament properties; however, the underlying molecular mechanisms remain a matter of debate. Titin also has the potential to more directly support the work output of the contractile system by storing and releasing elastic energy via Ig-domain unfolding–refolding transitions, which occur under physiological (low) stretch forces. While the magnitude of this contribution remains to be tested in vivo, the mechanism may be relevant to synchronize the mechanical activities of the actomyosin and titin systems. Collectively, available data leave no doubt that the mechanical design of titin aids active contraction of cardiac muscle in multiple ways. It will be worth studying how the connectivity between titin spring stiffness and cardiac contractility may change in failing hearts.
Cardiac filling and output are optimized according to the actual demands of the body, and these adjustments also employ changes to titin stiffness. The question is how do you fine-tune stiffness in such a large protein potentially on almost a beat-to-beat basis? One of the quickest ways to adjust protein activity is through PTMs. For titin, phosphorylation, oxidation, and more recently acetylation have been shown to modulate its spring stiffness. Differences in these modifications have also been detected under pathophysiological conditions, giving rise to novel treatment strategies aiming at reversing pathological passive stiffness of patient hearts.
Lysine acetylation/deacetylation has traditionally been studied as a modification of histones and a regulator of transcription but it is increasingly becoming recognized as a modulator of cytoplasmic proteins, particularly relating to cellular metabolism. Acetylation regulation has become of interest to the cardiac community, not least because of its association with obesity, a worsening global burden of disease and a major comorbidity in HF. Acetylation has been abundantly detected along the length of titin (Figure 3A); however, both the native acetylation state of titin and how it changes in CMs require additional studies, especially on human hearts. Proteomic analysis has revealed an increase in titin acetylation in obese mice but no significant difference in acetylation levels in HFpEF models compared with the experimental controls. Proposed treatments targeting acetylation pathways have also uncovered changes to titin acetylation, which may play a role in the modulation of cardiac stiffness. The caloric restriction mimetic, nicotinamide (NAM), is a precursor of nicotinamide adenine dinucleotide (NAD+), which is required for the activation of deacetylase enzymes, such as sirtuin-1 (SIRT1). NAD+ has been suggested as a potential therapeutic for HFpEF. Direct application of SIRT1 on skinned rat CMs was found to reduce their stiffness. This was suggested to be a contributing factor of improved diastolic function in HFpEF after treatment with NAM. However, conflicting results in similar animal models have shown that the use of histone deacetylase inhibitors can also ameliorate diastolic dysfunction and increase myofibril relaxation. although changes in the acetylation state of titin after treatment were not assessed. Another open question is how deacetylation causes a decrease in titin stiffness. The additional negative charge added to positively charged lysines during acetylation has been shown to stabilize enzymes and decrease the rate of unfolding. Based on our findings, we propose that the increased acetylation of the titin spring, e.g. in HFpEF due to reduced SIRT1 activity, promotes intramolecular interactions within elastic titin via addition of negative charges in a positively charged environment; as a consequence, titin becomes stiffer (Figure 3B). Conversely, the increased deacetylation of titin, e.g. by elevated SIRT1 activity following NAD+/NAM treatment, partially reverses this stiffening (Figure 3B). Additionally, titin acetylation may play a role in competitive PTM crosstalk with titin ubiquitination as both occur on lysine residues. The exact role acetylation plays in titin regulation and its association with cardio-metabolic disorders provides some exciting prospects for future research and therapeutics.
Oxidative stress is a generalized term for an imbalance between reactive oxygen species (ROS) and antioxidants. While low levels of ROS are required for normal cellular function and regulation, higher levels are associated with the pathogenesis of many diseases including cardiovascular disorders. Excess ROS leads to the modification of various cellular processes including modifications of amino acids containing thiol groups (commonly cysteines) in either a reversible or irreversible manner. Titin is a prime target of oxidation and I-band titin oxidation–reduction modulates titin-based stiffness. Mass spectrometric analyses of cardiac titin have detected oxidation sites along the entire molecule (Figure 4A). In mouse hearts exposed to oxidative stress, the highest relative increase in titin oxidation was found within I-band ‘hotspots’, specifically, the N2Bus and Ig domains of the distal I-band region. However, both the location of oxidation site and the type of oxidation are critical in determining the consequence on titin stiffness: titin domains can undergo either S-glutathionylation, which leads to an increase in titin compliance, or disulphide bonding, which causes increased titin stiffness (Figure 4B). These modifications are reversible, e.g. under the influence of reductants like dithiothreitol or glutathione. Mechanistically relevant is that titin oxidation within Ig domains may rarely occur spontaneously but may rather require cysteines buried within the Ig domains (cryptic cysteines) to be exposed through domain unfolding—a process we have coined UnDOx (unfolded domain oxidation). If UnDOx occurs, these domains cannot refold back to their native state, which affects titin-based stiffness. Importantly, the type of oxidation that occurs at the cryptic cysteines determines the effect on stiffness. UnDOx, through S-glutathionylation, prevents the Ig domain from refolding, thereby maintaining a longer contour length and lowering titin stiffness (Figure 4B, left). Additionally, this unfolded oxidized state allows for the controlled homotypic interactions and in-register aggregation of the distal I-band region (Figure 4C), which may aid stiffness regulation and force propagation. Alternatively, UnDOx can involve disulphide bond formation (Figure 4B, top right). A disulphide bridge can arise between any two (of a diad, triad, or quadripartite group of) cysteines within an unfolded Ig domain and prevent the extension of the domain to its full contour length, therefore increasing the passive stiffness of titin. It has been suggested that the formation of disulphide bonds in titin may fine-tune muscle work production by providing additional power during Ig-domain refolding under a (low) stretch force. The formation of disulphide bonds in Ig domains may also be aided by intermediatory S‐sulfenylation, but due to the highly volatile nature of S‐sulphenylation, there is yet to be any evidence showing that this occurs in vivo. On the other hand, disulphide bond formation also occurs within the N2Bus region of I-band titin (Figure 4B, bottom right) and contributes to increased titin stiffness by producing additional scaffolding to this normally disordered I-band region and preventing it from full extension. Both S-glutathionylation and disulphide bonding are a natural consequence of exertion and increased metabolism. Therefore, low levels are detected in cardiac titin under healthy conditions, but they rise with an increase in cardiac preload or afterload (Figure 4C). However, in the case of cardiac ischaemia or chronic HF due to metabolic syndrome (e.g. HFpEF), there is a loss in the balance of these oxidative modifications and what was once a normal short-term regulatory process becomes an extensive modification and marker of disease (Figure 4C). Targeting titin oxidation therapeutically could be useful in the treatment for these cardiac conditions to modulate myocardial stiffness.
Phosphorylation is a well-established PTM in many cellular processes. In the heart, it regulates cardiac output on multiple levels. Phosphorylation is also the best-studied titin PTM, modulating the molecule’s stiffness. Over 300 titin phosphorylation sites have been detected using proteomic (mass spectrometric) techniques, with some phosphosites consistently being identified in multiple studies (Figure 5A). At this stage, it appears that the titin phosphorylation-dependent changes in CM stiffness are related to where the titin spring becomes phosphorylated and the net protein charge present in that titin region (Figure 5B). Caution needs to be taken with this assumption, however, due to the bias in where along the titin molecule phosphorylation has been assessed historically. The gigantic size of titin currently makes it impossible to recombinantly express the whole protein. Therefore, small regions of titin in specific locations, such as the Z-disc or M-band, or with unique mechanical properties, such as the PEVK and N2Bus regions, have been the focus of most low-throughput approaches (e.g. antibody-specific targeting or mutagenesis of phosphosites). Initial studies determined that phosphorylation within the Z-disc region could be attained by cyclin-dependent kinase (cdc2) and extracellular signal‐regulated kinase (ERK)2 and similarly, cdc2 could also phosphorylate the M-band titin region. Additionally, early studies showed that the titin kinase domain (TK) also required phosphorylation of tyrosine residue 32341, as well as the binding of Ca2+/calmodulin, to enable activation (Figure 5B, right); however, TK currently is considered an inactive pseudo-kinase. Since then, two main regions, the N2Bus and PEVK segments, have been the focus of most titin phosphorylation studies. There is much compelling evidence to suggest that phosphorylation at these regions plays a crucial role in titin-stiffness regulation. Interestingly, the phosphorylation of the N2Bus and PEVK regions has opposing effects (Figure 5B). Increased phosphorylation of the N2Bus causes increased titin compliance through an increase in persistence length, as determined by single molecule atomic force microscopy stretch experiments. Conversely, phosphorylation of the PEVK region leads to increased titin stiffness. The mechanisms behind this change in stiffness are still not completely clear; however, we have previously suggested that the difference may be due to the N2Bus region and the PEVK having different localized net charges. The N2Bus has a net negative charge and therefore, the introduction of additional negative phosphate groups could cause electrostatic repulsion within the N2Bus region, leading to improved extensibility and a decrease in titin-based force. Conversely, the positive net charge of the PEVK region (notably: only the constitutively expressed and not the differentially spliced segment, which has a net negative charge!) would see increased intramolecular interactions, reduced extensibility, and increased force (Figure 5B). Firm experimental proof for this ‘charge theory’ is still lacking. To complicate matters further, there is an array of protein kinases (PKs) that can phosphorylate multiple sites in titin. At least four main phosphoserines have been identified in the human N2Bus region (Figure 5B): S4010, S4062, S4099, and S4185 (according to human titin consensus sequence, UniProKB #Q8WZ42-1). The first three are evolutionary conserved (mouse equivalents S3991, S4043, and S4080, respectively). These phosphosites have been identified using site-specific methods, such as mutagenesis of phosphorylated residues or antibody-specific targeting. The N2Bus sites have been determined to be phosphorylated by various PKs, such as PKA and PKG, PKD, ERK2, and/or CaMKIIδ. These phosphosites have also been shown to be dephosphorylated by serine/threonine protein phosphatase 5, a highly regulated protein phosphatase with low basal activity. Similarly, within titin’s PEVK region, several sites have been detected, which are phosphorylated by PKCα, CaMKIIδ, and/or PKD, in particular conserved phosphoserines S11878 and S12022 (mouse equivalents S12742 and S12884). In part, this would imply conflicting signalling as both PKD and CaMKIIδ phosphorylate N2Bus and PEVK titin—which in theory should have opposite effects on titin stiffness (Figure 5B). However, both kinases cause a decrease in titin-based force of isolated cardiac preparations, suggesting the effect on N2Bus dominates. However, caution is required when interpreting these results, considering the number of phosphorylation sites that have been detected but not yet thoroughly investigated in titin (Figure 5A). Indeed, at least one of the above PKs, CaMKIIδ, can also phosphorylate Ig domains in I-band titin, but only after domain unfolding, and the process is further promoted by UnDOx through S-glutathionylation (Figure 5B). This additional possibility for titin regulation by phosphorylation may aid in stabilizing the unfolded state of the domain and support mechano-chemical signalling events. In summary, titin-stiffness regulation by phosphorylation is complex and involves several signalling hotspots within I-band titin and various PKs/phosphatases; currently, it is the best-understood mode of titin regulation by PTMs backed by a large body of evidence.
Irregular phosphorylation of titin is also highly implicated in HF. Frequently, HF is associated with pathologically increased myocardial and CM passive stiffness, at least in HFpEF patients. Some of this stiffness increase can be explained by hypo-phosphorylation of phosphoserines within the N2Bus region of titin and hyper-phosphorylation of select sites within the PEVK region, e.g. S11878 (Figure 5C). However, exceptions exist depending on the form of human HF and specific heart chamber investigated, or the animal model used. A detailed review of this topic has recently been compiled. Generally, it is more informative to determine site-specific titin phosphorylation rather than global titin phosphorylation, considering the huge number of potential phosphosites in the full-length titin protein. However, given where phosphorylation occurs affects how titin stiffness is modulated, more site-specific information on titin phosphorylation is needed to better understand the diseased state. Altogether, reduced phosphorylation of titin’s N2Bus region appears to be a frequent alteration in various HF types, and this alteration may be an important contributor to increased CM stiffness in disease.
Disruption in phosphorylation regulation is a common feature in cardiac diseases not only affecting titin but also many other cardiac proteins. This makes phosphorylation regulation an attractive target for therapeutic treatment. Pathologically increased myocardial passive stiffness is a sign of diastolic dysfunction often seen in HF patients when associated with comorbidities, including diabetes mellitus. Interestingly, conventional therapeutics for diabetes, including metformin and insulin, have been found to improve diastolic function in these patients. Specifically, metformin and insulin increased the activity of titin-targeting PKs (ERK1/2; PKCα; PKA), improving phosphorylation of the N2Bus (while marginally increasing PEVK phosphorylation) and causing a reduction in titin-based stiffness (Figure 5D). Similarly, chronic admission of the cardiac growth factor neuregulin-1 was shown to activate ERK1/2 and PKG while suppressing PKCα, resulting in hyper-phosphorylation of N2Bus and hypo-phosphorylation of PEVK titin, both of which are conducive in reducing titin stiffness (Figure 5D). In this context, a main focus has been on the cyclic guanosine monophosphate (cGMP)-PKG pathway, because dysregulation of this pathway is strongly associated with cardiac remodelling and HF. There are now multiple substances available which stimulate this pathway (see Ref.102 for a recent, comprehensive review on the role cGMP plays in the heart). Phosphodiesterase-5A inhibitors (such as sildenafil) and B-type natriuretic peptide (BNP), which boost cGMP levels, showed promise as a potential treatment for diastolic dysfunction in pre-clinical tests (Figure 5D). In regards to titin, both sildenafil and BNP increased (total) titin phosphorylation, reduced CM stiffness and increased left ventricular (LV) distensibility in a dog model of diastolic dysfunction. However, the use of PDE5 inhibitors in the RELAX trial failed to show significant improvement of diastolic function in HFpEF patients that received the treatment (Figure 5D). Similar trials are underway with PDE9A inhibitors, which also aim to increase the cGMP concentration in the heart and benefit cardiac function. In mouse models of diastolic function, PDE9A inhibition reduced LV diastolic stiffness through reduction of CM stiffness; however, titin phosphorylation was not measured. Furthermore, soluble guanylyl cyclase (sGC) activators/stimulators are considered a promising treatment for HFpEF, having been found to raise cGMP levels leading to increased PKG (and also PKA and ERK2) activity, while reducing PKCα and CaMKIIδ activity. This again resulted in increased N2Bus and total titin phosphorylation, reduced CM stiffness and reduced LV stiffness (Figure 5D). However, these results were not replicated in either the VITALITY or the SOCRATES clinical trials where no improvement in diastolic function was seen in HFpEF patients treated with sGC-stimulator. Although these treatments showed promise in the laboratory setting, discrepancies between dosages and metabolic differences in animals vs. humans may in part be the cause of clinical trial failures. This highlights one of the many barriers in finding effective new treatments for HF. Additionally, the complexity of pathways, such as cGMP-PKG, means that the regulation of intermediate steps in the pathway can also be unintentionally changed and counteract the desired effect of the treatment. Therefore, increasing local cGMP levels within titin microdomains by exploiting compartmentalization might prove more useful, but such microdomains are yet to be determined. Regardless, these treatments may still be successful for a subset of patients with only modestly impaired cGMP-PKG signalling.
Maintaining a protein as large as titin over a lifetime requires a sophisticated PQC system. Titin is thought to be turned over at least every ∼3 days in cell culture, but the protein’s half-life in adult mice is 2–3 weeks. This enables sarcomere maintenance and CM remodelling during development and repair of stress-related protein damage in adulthood. However, with advancing age, the cellular PQC machinery, including the titin-directed PQC systems, may slow down. Consequently, defective titin protein could accumulate intracellularly and form cytoplasmic aggregates that are cytotoxic. As shown for other proteins, this may be one of the contributing factors to the development of cardiac disease in the elderly. An initial step in PQC is to stabilize or repair (refold) damaged (unfolded) proteins under stress with the aid of chaperones. This appears to be particularly important for the elastic I-band titin as it contains many regions that unfold during a stretch. Small heat shock proteins (sHSPs) are chaperones that protect unfolded proteins from permanent damage but do not necessarily mediate the refolding (which requires an ATP-dependent chaperone). The sHSPs alpha-B-crystallin (HSPB5) and HSP27 (HSPB1) are abundant in the cytosol of CMs and are also present at the Z-disc under physiological conditions. Upon physiological stretch, as well as in failing hearts (e.g. ischaemia; cardiomyopathy), these chaperones are up-regulated and play a protective role by translocating to the N2Bus, N2A, and proximal/middle I-band Ig-domain regions of titin (Figure 6A and B). There, they stabilize unfolded segments and prevent aggregation. Moreover, the ATP-dependent HSP90 associates with titin’s N2B region and also with the N2A element if first methylated by the co-chaperone and methyltransferase Smyd2, a direct N2A ligand (Figure 6A). This interaction protects I-band integrity. The protective roles of HSPs on titin may include the prevention of pathological stiffening; however, their stabilizing function could also impede the elastic properties of titin and contribute to increased stiffness. If chaperones fail to protect the protein, it then becomes marked for degradation and turnover. For titin turnover to occur, this large protein likely needs to be (partially) released from its binding partners within the sarcomere. Proteases, such as calpain-1 and matrix-metalloproteinase (MMP)-2 can bind to the proximal I-band and Z-disc or M-band regions of titin (Figure 6A) and aid in the pre-digestion of titin. Under ischaemic conditions or in the presence of anthracyclines used in cancer treatment (e.g. doxorubicin), there is an increase in the expression and activity of these proteases leading to increased titin breakdown and cardiac remodelling (Figure 6B). Protection from titin degradation by these proteases would be possible by using protease inhibitors, as is known from in vitro work. Protease pre-digestion of titin is thought to further expose binding sites for targeted degradation. Two interlinked pathways, the ubiquitin-proteasome (UPS) and autophagy-lysosomal systems, regulate the subsequent degradation steps. Through the UPS, damaged or aged titin molecules can become ubiquitinated by ubiquitin E3 ligase(s) and thus marked for proteasomal degradation. The E3 ligase mouse-double-minute 2 homolog interacts with the titin-capping protein telethonin, but it is unknown whether it ubiquitinates titin. Other E3 ligases, the muscle ring-finger proteins (MuRF)-1 and -2, bind in the titin A-/M-band transition zone and to the TK (Figure 6A). They preferentially ubiquitinate A-band proteins, including the TK. In diseased states, protective effects on sarcomere proteins (including titin?) and contractile improvements have been observed with MuRF1-interfering small molecules as a potential therapeutic approach. It is likely that one or more other, not yet identified, E3 ligase ubiquitinate titin. The TK is a well-established hub for protein–protein interactions as it binds the Nbr1/SQSTM1(p62) complex (Figure 6A), which acts as an autophagy receptor for ubiquitinated proteins. Autophagosome activity reduces with age; however, the role of autophagy in heart disease is incompletely understood: both increased and reduced activities have been reported (Figure 6B). A more common finding is that the activity of the UPS is reduced in HF and cardiomyopathy, resulting in the accumulation of ubiquitinated but not degraded proteins. These alterations also include titin (Figure 6B). In end-stage failing human DCM hearts due to a TTN-truncating variant (TTNtv), increased ubiquitination of wt-titin was found, whereas truncated (tr-)titin proteins were barely ubiquitinated and stably expressed (Figure 6B). Truncated titin accumulated in cytoplasmic aggregates. Thus, titin-degradation pathways appeared to be deregulated in TTNtv hearts. The changes observed in TTNtv-DCM patient hearts also included reduced MuRF1 expression, whereas autophagy was not impaired or even activated. Taken together, despite recent advances, many details of the titin turnover and degradation processes are still poorly understood.
Several approaches have been made to target the PQC machinery therapeutically in cardiac diseases, which may also work for disorders related to titin dysfunction. Chaperone induction in mice has been shown to improve cardiomyopathy associated with muscular dystrophy and perhaps can also be employed to enhance titin protection against ischaemia or DCM (Figure 6B). Moreover, autophagy activators, such as spermidine, have been shown to be protective against age-related cardiovascular disease, including animal models of diastolic dysfunction. Further, the inhibition of MMP-2 or calpain proteases might be advantageous as a prophylactic therapy during cancer treatment, e.g. to reduce doxorubicin-induced cardiotoxicity (Figure 6B). Given the dysfunction of the UPS in several forms of heart disease, modulators of the UPS may show promise as a potential treatment, as demonstrated for UPS inhibitors. However, the toxicity of proteasome inhibitors may limit their therapeutic value. Interestingly, in hiPSC-CMs with a TTNtv, UPS-inhibition raised wt-titin-protein expression (as well as tr-titin-protein content) and boosted contractility. Increased wt-titin levels may be beneficial as they promote the formation of sarcomeres. At this stage, caution needs to be taken with any speculations on the therapeutic benefit of protease or UPS inhibitors, due to their unspecific nature and our limited understanding of their complex cellular interactions.
Importantly, deregulated PQC of titin is central to the pathomechanisms of TTN-truncation cardiomyopathy. Earlier, groundbreaking work established a titin truncation as the most frequent genetic cause of human DCM: 15–25% of most DCM patient cohorts studied carry a heterozygous TTNtv—by far the largest share among all cardiomyopathy gene variants known. Similarly, a TTNtv is the most common genetic predisposition in other types of inherited cardiac disorders, including restrictive, non-compaction, and peripartum cardiomyopathy, but also in acquired cardiomyopathies, such as those induced by alcohol abuse or cancer treatment. Heterozygous TTNtv can occur anywhere along titin; however, the prevalence of having a TTN truncation and the odds of getting DCM from a TTNtv vary depending on the location of the pathogenic variant (Figure 7A and B). The odds ratio is highest if the truncation is in the A-band segment (prevalence in DCM 10.74% vs. control 0.24%, odds ratio 49.8), followed by truncations in constitutive exons of I-band TTN (Figure 7B). In contrast, the odds ratio is lowest for central I-band titin (prevalence in DCM 0.24% vs. control 0.17%, odds ratio 1.5). This is a consequence of the extensive alternative splicing of I-band TTN, where exon usage is low for all regions not expressed in the N2B titin isoform (Figure 7A). Thus, while TTNtv are found in 0.5–3% of the healthy (control) population, many of these variants occur in I-band titin exons with low percentage spliced-in (PSI) and do not cause DCM (Figure 7B). Previous suggestions about the possible pathomechanisms of TTNtv-DCM included sarcomere insufficiency, modest nonsense-mediated decay of TTNtv-mRNA, or translational deregulation, whereas a poison-peptide (dominant-negative) mechanism was considered unlikely because truncated titin proteins were detected only in hiPSC-CMs but not in adult heart tissue. However, recent studies have unequivocally shown that tr-titin proteins are stably expressed in human end-stage failing, adult TTNtv-DCM hearts. Their concentration reaches up to 50% of the total titin-protein pool but is highly variable. Strikingly, the higher the tr-titin-protein content of a TTNtv-heart, the younger the (adult) patients at the time of transplantation, suggesting that these proteins are disease-relevant. The tr-titin proteins (unlike the wt-titin proteins) are not built into the sarcomeres at meaningful amounts but are sequestered in aggregates. Thus, a poison-peptide mechanism is likely part of the pathomechanisms of TTNtv-DCM (Figure 7C). Apart from a dominant-negative mechanism, TTNtv-DCM patient hearts contain less wt-titin protein than DCM hearts without TTNtv or non-failing (donor) hearts, which demonstrates titin haploinsufficiency. However, nonsense-mediated decay of TTNtv-mRNA is not a prominent feature of TTNtv-DCM patient hearts (Figure 7C). With wt-titin being lost, the CMs of TTNtv-DCM hearts also have fewer sarcomeres per unit area than non-TTNtv-DCM hearts, confirming sarcomere insufficiency and explaining chronic contractile deficiency. Recent findings underscored that TTNtv-DCM hearts have a problem with intracellular PQC (Figure 7C; cf. section above). With increasing patient age, the UPS may be overwhelmed by the large amounts of tr-titin protein produced and partially shut down, whereas E3-ubiquitin ligases, such as MuRF1 become down-regulated. Conversely, the intracellular aggregate formation may promote autophagy. Interestingly, disease modelling in hiPSC-CMs with a TTNtv demonstrated that UPS-inhibition, but not autophagy-modulation, increased both tr- and wt-titin-protein content, with larger effects on tr-titin. Reversal of the titin haploinsufficiency by UPS-inhibition improved TTNtv-hiPSC-CMs contractility, despite the raised tr-titin-protein content. If the TTNtv was repaired by CRISPR/Cas9 gene-editing, the titin haploinsufficiency was corrected, tr-titin proteins were absent, and contractility was fully recovered. These findings can be exploited for new therapies of TTNtv-related cardiomyopathies (Figure 7D). In summary, several key pathomechanisms come together in TTNtv-patient hearts, providing a rationale for phenotypic diversity. Disease mechanisms include titin haploinsufficiency as a life-long condition and truncated titin-protein enrichment with aggregate formation, as well as aberrant PQC, presumably both as additional late-onset pathomechanisms. Having a TTNtv may represent a major risk factor to get DCM later in life, especially when other stressors hit.
New technologies emerging over the last decade (e.g. next-generation sequencing, 2D and 3D culture of hiPSC-CMs, gene-editing, or ‘omics’) have allowed amazing progress to made also in the titin field. This has greatly improved our understanding of the role titin plays in HF and the disease mechanisms of TTN-truncation cardiomyopathy. However, much work still lies ahead of us. For example, TTN pathogenic variants include not only truncations but also missense variants whose pathophysiological relevance is only slowly evolving. Missense and truncation variants can occur together in the same patient, typically amplifying the pathophenotype. Moreover, the role of titin–isoform switch and titin PTMs in HF (notably HFpEF) remains fuzzy, as it is not yet clear whether these changes are a cause or consequence of the disease, and whether targeting these titin properties specifically (if possible) could improve the syndrome. The ageing population is also highlighting the importance of proteostasis regulated by PQC, and we are only just starting to appreciate how deregulated PQC in ageing and disease may affect titin. Similarly, the discovery that circular RNA of titin can also play a regulatory role in cellular function potentially opens the door to a whole new relevance of titin. Finally, this giant protein may still carry more molecular mysteries awaiting discovery. Perhaps we will know soon, how titin processes a stretch signal to boost active myocardial contraction and support increased cardiac output. |
PMC9648838 | Amitabh Bipin Suthar,Christopher Dye | Infection, immunity, and surveillance of COVID-19 | 10-11-2022 | Dr Amitabh Suthar and Dr Christopher Dye give their perspective on infection, immunity and surveillance of COVID-19. | Infection, immunity, and surveillance of COVID-19
Dr Amitabh Suthar and Dr Christopher Dye give their perspective on infection, immunity and surveillance of COVID-19.
Between January 2020 and September 2022, nearly 7 million Coronavirus Disease 2019 (COVID-19) deaths were counted worldwide while the total number of deaths associated with COVID-19, i.e., deaths directly and indirectly associated with the COVID-19 pandemic, was probably 2 to 4 times higher [1,2]. While the COVID-19 death toll has been a headline statistic in daily news bulletins throughout the pandemic, COVID-19 case surveillance—including case incidence, hospital admissions, and deaths—and virologic surveillance (Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) test positivity rates) are used to guide the application of public health and social measures [3,4]. Although case surveillance may capture most COVID-19 cases admitted to hospital and most COVID-19 deaths, variable testing criteria and case definitions, limited access to diagnostics, and inconsistent reporting affect the completeness of COVID-19 cases reported. Therefore, additional methods for tracking SARS-CoV-2 and COVID-19 are needed.
One of the additional measurement methods is serological surveillance (serosurveillance), in which the detection of specific antibodies signals exposure to SARS-CoV-2 infection among members of a selected population [5]. The virtue, in principle, of serology is that it records all exposures to infection whereas cases of COVID-19 illness are likely to be under-reported. The serological profile of a population indicates not simply the number of people infected, but who, where, and when. Risk factors for infection can be investigated by comparing exposures among infected and non-infected people. Repeated cross-sectional, seroprevalence surveys further allow calculations of the rate of spread of infection through a population. Moreover, given the underreporting associated with case surveillance, coupling hospitalizations and deaths with serosurveillance to calculate infection-hospitalization rates and infection-fatality rates may provide more reliable COVID-19 severity estimates than can be gleaned from case-hospitalization and case-fatality rates from case surveillance data [6]. WHO has recommended COVID-19 serosurveillance using a standardized methodology—the UNITY Protocol—since early 2020 [7]. To date, there has been no comprehensive synthesis of surveillance data using this approach. Now, in their new meta-analysis, Bergeri and colleagues included nearly 965 seroprevalence studies sampling 5,346,069 participants from 100 countries to present a composite picture of the temporal and spatial distribution of SARS-CoV-2 infection worldwide [8]. We provide our perspectives on this article and SARS-CoV-2 serosurveillance. Some of their findings confirm expectations: They show how seroprevalence has risen during the pandemic, but with geographical variation. Other findings reinforce data from other sources: they describe the surge of infections due to the spread of novel SARS-CoV-2 variants in Africa (beta), Southeast Asia (delta), and in Europe and the Americas (omicron). They provide some evidence that stringent public health and social measures limited SARS-CoV-2 transmission, as reflected by lower seroprevalence rates. Their data also reinforce concerns about inequitable access to vaccines: Seroprevalence changes due to vaccination were more common in high-income countries while seroprevalence changes due to infection were more common in low- and middle income countries. The data also point to uneven access to health services and diagnostics because the ratio of infections to reported cases was high in resource-constrained regions of the world, particularly Africa. But the analysis by Bergeri and colleagues also poses questions about the current and future value of serosurveillance for SARS-CoV-2 and other emerging pathogens. We comment on 3. The first concerns the precision of the serological assays used. At their core, accurate measures of seroprevalence depend on having antibody tests with high sensitivity and specificity. On sensitivity, Bergeri and colleagues found that seroprevalence was relatively low in children less than 10 years old. Perhaps children were less frequently exposed to infection; but low prevalence might also be explained by the milder infections experienced by children, which perhaps stimulated weaker antibody responses and more false negatives. Antibody titers also tend to be lower in asymptomatic cases, a proportion of which may never become positive during the course of infection [7]. Another challenge to serosurveillance is that infection can be confounded by vaccination. Bergeri and colleagues countered this by using antinucleocapsid (N) antibodies to measure infection in countries where vaccines using only spike (S) protein antigens, i.e., mRNA vaccines, where delivered. However, in many low- and middle-income countries inactivated vaccines, such as Sinovac’s CoronaVac, Sinopharm’s BBIBP-CorV, or Bharat Biotech’s BBV152 COVAXIN, are also delivered [9]. Inactivated vaccines elicit both anti-S and anti-N responses and therefore antinucleocapsid (N) antibodies would not differentiate between infection and vaccination. In these countries, seroprevalence measurements had to be adjusted using accessory data on the fraction of people vaccinated. Given the challenges of tracking vaccinations administered, this may have biased estimates. Second, serosurveillance has limited utility in tracking rapidly spreading infections. Point seroprevalence is an aggregate between seroconversion and seroreversion [10]. For SARS-CoV-2, the median time from exposure to seroconversion is about 3 weeks; the time to reversion is about 25 weeks [11]. So serosurveillance captures neither recent infection nor past reversion (Bergeri and colleagues did not allow for reversion in their estimation of seroprevalence). In a rapidly growing epidemic with a doubling time of less than 1 week [12], seroprevalence lags far behind the spread of infection. In general, failing to allow for antibody dynamics will typically underestimate the cumulative prevalence of infection. In the extreme, if serological surveys are spaced too far apart, they could entirely miss explosive, short-lived outbreaks of disease (or waves of transmission). Third, Bergeri and colleagues argue that anti-SARS-CoV-2 antibodies are highly predictive of immune protection, as stated in WHO guidelines [8]. However, the detection of antibody does not guarantee immunity, whether it be protection from SARS-CoV-2 infection or from COVID-19 illness and death, nor does the absence of antibody reliably indicate susceptibility to infection or disease. The relationship between antibody and protection against SARS-CoV-2 or COVID-19 requires quantitative calibration [13,14], recognizing that protection depends both on humoral (antibodies and memory B cells) and cellular immunity (T cells) [15]. The calibration is necessarily different for infection and disease, and no general rules yet exist. It is telling that just 6 (0.6%) of the serological studies described by Bergeri and colleagues were based on tests that detect neutralizing antibodies—the antibodies that are most closely linked to functional immunity.
Bergeri and colleagues have shown how serosurveillance can help to characterize nearly 3 years of the COVID-19 pandemic. They do not discuss, either on technical grounds or with respect to the limited financial resources of many national health services, how to prioritize serological surveys alongside other key elements of disease surveillance systems and health system strengthening. While core surveillance systems serve priority objectives (Table 1), WHO gives serological surveys a limited role during COVID-19 outbreak investigations, tracking infection, and retrospectively measuring the attack rate or the size of an outbreak [3]. Furthermore, serosurveillance is not considered to be a source of information to guide public health and social measures [4]. As we learn how to safely live with SARS-CoV-2, the experience that lies behind nearly a thousand serological surveys will be valuable in updating WHO guidance on the role, requirements, and use of serosurveillance data for SARS-CoV-2 and future health emergencies. Those updated recommendations should inform the decision of whether and how to invest, as Bergeri and colleagues propose, in “a global system or network for targeted, multi-pathogen, high-quality, and standardized collaborative serosurveillance” to monitor COVID-19 and other emerging pathogens. |
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PMC9648840 | 36315588 | Huimin Liu,Chen Li,Wenfeng He,Jing Chen,Guoqing Yang,Lu Chen,Hongtao Chang | Free ISG15 inhibits Pseudorabies virus infection by positively regulating type I IFN signaling | 31-10-2022 | Interferon-stimulated gene 15 (ISG15) is strongly upregulated during viral infections and exerts pro-viral or antiviral actions. While many viruses combat host antiviral defenses by limiting ISG expression, PRV infection notably increases expression of ISG15. However, studies on the viral strategies to regulate ISG15-mediated antiviral responses are limited. Here, we demonstrate that PRV-induced free ISG15 and conjugated proteins accumulation require viral gene expression. Conjugation inhibition assays showed that ISG15 imposes its antiviral effects via unconjugated (free) ISG15 and restricts the viral release. Knockout of ISG15 in PK15 cells interferes with IFN-β production by blocking IRF3 activation and promotes PRV replication. Mechanistically, ISG15 facilitates IFNα-mediated antiviral activity against PRV by accelerating the activation and nuclear translocation of STAT1 and STAT2. Furthermore, ISG15 facilitated STAT1/STAT2/IRF9 (ISGF3) formation and ISGF3-induced IFN-stimulated response elements (ISRE) activity for efficient gene transcription by directly interacting with STAT2. Significantly, ISG15 knockout mice displayed enhanced susceptibility to PRV, as evidenced by increased mortality and viral loads, as well as more severe pathology caused by excessive production of the inflammatory cytokines. Our studies establish the importance of free ISG15 in IFNα-induced antiviral immunity and in the control of viral infections. | Free ISG15 inhibits Pseudorabies virus infection by positively regulating type I IFN signaling
Interferon-stimulated gene 15 (ISG15) is strongly upregulated during viral infections and exerts pro-viral or antiviral actions. While many viruses combat host antiviral defenses by limiting ISG expression, PRV infection notably increases expression of ISG15. However, studies on the viral strategies to regulate ISG15-mediated antiviral responses are limited. Here, we demonstrate that PRV-induced free ISG15 and conjugated proteins accumulation require viral gene expression. Conjugation inhibition assays showed that ISG15 imposes its antiviral effects via unconjugated (free) ISG15 and restricts the viral release. Knockout of ISG15 in PK15 cells interferes with IFN-β production by blocking IRF3 activation and promotes PRV replication. Mechanistically, ISG15 facilitates IFNα-mediated antiviral activity against PRV by accelerating the activation and nuclear translocation of STAT1 and STAT2. Furthermore, ISG15 facilitated STAT1/STAT2/IRF9 (ISGF3) formation and ISGF3-induced IFN-stimulated response elements (ISRE) activity for efficient gene transcription by directly interacting with STAT2. Significantly, ISG15 knockout mice displayed enhanced susceptibility to PRV, as evidenced by increased mortality and viral loads, as well as more severe pathology caused by excessive production of the inflammatory cytokines. Our studies establish the importance of free ISG15 in IFNα-induced antiviral immunity and in the control of viral infections.
Pseudorabies virus (PRV), also called suid herpesvirus 1 (SuHV-1) or Aujeszky’s disease virus (ADV), belongs to the alphaherpesvirus subfamily and infects a broad host range including its natural host swine [1]. Particularly, recent evidence revealed that PRV can induce serious encephalitis in a small portion of the infected individuals, raising the concern of PRV cross-species transmission [2–6]. Like other herpesvirus, PRV can establish a latent infection in peripheral nerve cells, which is usually used as a model for studying the biology of alphaherpesvirus [7,8]. Despite intensive research, neither viable therapeutic options nor effective vaccines is currently available to prevent PRV infection [2,9]. Therefore, understanding the interplay between PRV and host cells will improve antiviral treatment. In response to viral invasion, the host evolves various defense mechanisms. Among these, the type I interferon (IFN-I) plays a critical role in host innate immunity defense against viral infection. IFN-I, represented by IFNα/β, binds to their respective receptors and activates the JAKs, which subsequently phosphorylate STAT1 and STAT2. The phosphorylated (p-) STAT1 and p-STAT2 complex with IRF9, resulting in the formation of ISG factor 3 (ISGF3). ISGF3 shuttles to the nucleus, where it binds to the IFN-stimulated response element (ISRE) in DNA and stimulates the transcription of hundreds of interferon-stimulated genes (ISGs) involved in antiviral immune responses [10,11]. Increasing evidence indicates that PRV utilizes its encoded proteins to antagonize the IFN response by suppressing IFN-I signaling, or blocking IFN downstream ISGs expression [12–14]. Interferon-stimulated gene 15 (ISG15) is an IFNα/β-induced ubiquitin-like protein that exists in two distinct states: as a free molecule (intracellular and extracellular) or covalent conjugation to lysine residues of target proteins (ISGylation). Similar to ubiquitination, ISGylation involves a cascading reaction catalyzed by E1 activating (UbE1L), E2 conjugating (UbcH8) and E3 ligase (Herc5) enzymes, which are also induced by IFNα/β [15]. ISG15 can be removed from its target proteins by the ubiquitin-specific protease USP18, making the ISGylation process reversible [16–19]. Several studies have suggested a role for ISG15 has proviral or antiviral activities, depending on the virus and host species [20,21]. However, the role of ISG15 in viral infection remains controversial [22]. In vitro studies in mouse cells have demonstrated an antiviral role for ISG15 during several viral infection [23–25], although there are some reports of viruses displaying no enhanced replication when ISG15 is deficient [26,27]. Knocking down ISG15 in human cells has also suggested an antiviral role for ISG15 during infection with numerous viruses [23,28–30], while other studies have suggested no role at all [31,32]. Furthermore, mice lacking ISG15 exhibit enhanced susceptibility to some but not all viruses [26,27,33–35], while ISG15 deficiency in human enhanced viral resistance [22,36–38]. More recently, ISG15 was reported to have an immunomodulatory effect by acting as a negative regulator of IFN-I signaling, thus regulating the antiviral response during viral infection [39]. ISG15 is strongly upregulated in porcine kidney epithelial cells (PK15) following PRV infection [40]; however, its regulation during infection and the role of ISG15 in viral growth have not been characterized. In this study, we show that ISG15 expression and ISGylation are initially induced after PRV infection but later suppressed by viral responses, and that gE, plays an important role in reducing ISG15 expression. By silencing the expression of ISG15, we show that ISG15 inhibits PRV replication via the free ISG15, which promotes IFN-β production to suppress viral growth. Moreover, we reveal that ISG15 silencing impairs IFNα-mediated anti-PRV effect by blocking phosphorylation and nuclear translocation of STAT1 and STAT2. Significantly, ISG15 knockout mice exhibited highly susceptible to PRV infection, as evidenced by high mortality, increased viral titer and more severe inflammatory. Our results reveal a critical role for ISG15 in IFNα-mediated antiviral activity and will provide a potential cellular therapeutic strategy.
Although ISG15 expression has reportedly increased during PRV infection in our previous study, how free ISG15 and ISG15 conjugates impact PRV growth and their roles in host defenses against PRV remain largely unknown. We sought to dissect the expression of free ISG15 and ISG15 conjugates during PRV infection with different multiplicity of infections (MOIs) and different times post-infection. In PRV-infected PK15 cells, the greater levels of free ISG15 and ISG15 conjugates were observed at 24 hours post-infection (hpi), even at relatively low MOIs (0.5, 1 and 5). However, the levels of free ISG15 and ISG15 conjugates at high MOI (10) were much lower than those at low MOIs (Fig 1A–1C), and the decline in ISG15 expression was correlated with increased abundance of a representative viral late protein, gE, by 12 h and 24 h. This suggested that the high levels of ISG15 present in the cells to prevent PRV growth, whereas high expression of gE reduced the level of ISG15. The expression profiles of free ISG15 and ISG15 conjugates were also examined in cells infected with UV-inactivated virus (UV-PRV). In UV-PRV infection, the levels of free ISG15 and ISG15 conjugates were elevated at 12 h and 24 h and correlated proportionally with MOI (Fig 1A, lanes 6–9 and 1B, lanes 6–9). Levels of free ISG15 at 36 h induced by UV-PRV were lowered than those at 24 h, probably due to the termination of signaling (Fig 1C, lanes 6–9). The lack of viral gene expression in UV-PRV infection was verified by the absence of PRV-gE protein expression (Fig 1A–1C). Collectively, these results comparing PRV and UV-PRV infection demonstrate that free ISG15 and ISG15 conjugates are initially induced by PRV infection at low MOIs, but are suppressed in a manner dependent on viral gene expression at high MOI.
Considering the PRV-gE expression may be responsible for the upregulation of free ISG15 and ISG15 conjugates during PRV infection, we observed that the ISG15 abundance was associated with viral gene expression by confocal immunofluorescence assay (Fig 1D). We next compared the effects of PRV, UV-PRV, and gE-deleted mutant PRV infection (MOI of 1) on ISG15 transcription by RT-qPCR. All virus increased ISG15 mRNA levels 12 h after infection, however, ISG15 induction was terminated earlier for UV-PRV than PRV, and not notably terminated for gE-deleted PRV, which continued to produce high levels of ISG15 transcripts even at a late stage of infection (36 h) (Fig 1E). ISG15 transcription induced by PRV and UV-PRV infection gradually decreased might be through negative regulation of IFN-I signaling. This result indicated that PRV-gE plays a vital role in inducing ISG15 transcription during PRV infection. We further detected the expression levels of free ISG15 and ISG15 conjugates during PRV, UV-PRV, and gE-deleted PRV infection. In consistent with the results on ISG15 transcript level, the expression of free ISG15 and ISG15 conjugates were elevated at 12 h by all viruses, whereas the level of ISG15 expression markedly decreased after 24 h in PRV and UV-PRV groups (Fig 1F). This might suggest that viral processes mediated by gE may be implicated in the downregulation of ISG15 expression profiles. Consistent with the results shown in Fig 1B, PRV induced more ISG15 conjugates than UV-PRV at this MOI (Fig 1F, compare lanes 2 to 4 and 6 to 8). Importantly, gE-deleted virus induces a sustained increase in free ISG15, but has little effect on ISG15 conjugates, indicating that gE is required for the induction of free ISG15 expression during PRV infection. The effect of gE on free ISG15 expression was further investigated using gE-overexpressing PK15 cells. Control and gE-overexpressing PK15 cells were infected with PRV, and immunoblot results showed that overexpression of gE suppressed the induction of free ISG15, whereas had no effect on the ISG15 conjugates (Fig 1G), further supporting that gE is required for free ISG15 expression.
The role of ISG15 in PRV infection was investigated by knocking out ISG15 in PK15 cells. wild-type (WT) PK15 cells and ISG15 knockout PK15 cells (ISG15-/-) were either mock-infected or infected with PRV at an MOI of 1. PRV growth were remarkably increased in ISG15-/- cells, as evidenced by viral plague, IFA and Western blot assays (Fig 2A–2C), suggesting that ISG15 inhibited PRV replication. These results were also confirmed in mouse embryo fibroblasts (MEF) cells (S1 Fig). Additionally, it has been reported that ISG15 may affect virus entry [35] or release [41,42]. Thus, two experiments were carried out to gain information about the role of ISG15 in those steps of PRV replication. First, PK15 cells were transfected with a plasmid expressing ISG15 or a control plasmid before being infected with PRV, meanwhile, ISG15-/- cells were also infected with PRV. The RNA level of PRV-gE, a protein encoded by late gene [43], was quantified at various times post-infection. We found that a significant RNA reduction in the ISG15-overexpressing cells compared to control cells starting 6 hpi, while loss of ISG15 significantly enhanced PRV-gE RNA level (Fig 2D). This result showed that ISG15 restricts PRV growth at a post-entry stage of infection. Furthermore, the virus associated with cells and released to the supernatant were also quantified in control- or ISG15-transfected WT cells and ISG15-/- cells. As shown in Fig 2E, a significant decrease was observed in virus titers from cell-associated fraction of ISG15-transfected cells compared to control-transfected cells, while an obvious increase in ISG15 silencing cells. Moreover, more than 3-fold reduction was observed in virus titer from the supernatant fraction of ISG15-transfected cells compared to the same fraction of cell-associated cells (Fig 2E). These results indicate that ISG15 limits PRV replication occurring before virus release.
To further determine whether ISG15 achieves its antiviral effect against PRV via free or conjugated form, two different experiments were carried out. First, due to the fact that exposure of the C-terminal Gly-Gly motif is essential for conjugation of ISG15 to substrates [21], these two residues were replaced with Ala using site-directed mutagenesis to generate a mutate plasmid ISG15AA employed as a control. PK15 cells were transfected with an empty vector, a ISG15-expressing plasmid, or an ISG15AA expressing plasmid. After 24 h post-transfection (hpt), the cells were infected with PRV and then viral protein expression and viral titer were measured. As expected, we found a significant decrease in PRV-gE expression level, as well as PRV titer (3.2-fold) (Fig 3A), compared to cells transfected with empty vector. Notably, there wasn’t any significant differences detected in PRV titer and protein levels between these cells transfected with ISG15 and ISG15AA plasmid (Fig 3A), suggesting ISGylation did not seem to play an antiviral effect against PRV. Similar results were obtained from a parallel transfection / infection in ISG15-/- cells (Fig 3B). These data suggest that ISG15 exerts its antiviral activity against PRV through free ISG15-dependent or ISG15 conjugation-independent mechanisms. The effect of free ISG15 on PRV growth was further investigated by depleting UbE1L, a known E1 enzyme for ISGylation [44]. Prior to infection with PRV, PK15 cells were transfected with either a control siRNA or a siRNA targeting UbE1L, and expression of free ISG15 and conjugates were measured by immunoblotting. We observed that UbE1L-knockdown cells displayed higher levels of free ISG15 than control cells, but a slight decrease in the expression levels of ISG15 conjugates (Fig 3C). This might be due to the fact that ISGylation was incompletely inhibited by UbE1L knockdown. However, a significant drop in viral titer was found in UbE1L-knockdown cells compared with the control cells (Fig 3C), suggesting free ISG15 exerted anti-PRV activity. Meanwhile, the antiviral activity of free ISG15 was also confirmed in MEF cells (S2 Fig). Collectively, these results indicate that ISG15 inhibits PRV replication in a free ISG15-dependent manner, rather than ISGylation.
Like other alphaherpesviruses, PRV establish persistent infections rely partly on their ability to inhibit IFN-I signaling [13]. To determine if free ISG15 is a factor involved in the innate antiviral response, we firstly test the effect of free ISG15 on type I IFN production. We complemented the ISG15-/- cells with conjugation-deficient ISG15 mutant ISG15AA as a control, and no significant difference was observed for ISG15 expression between ISG15-/- cells transfected with ISG15AA-expressing plasmid and WT cells, indicating successful rescue of ISG15 (Fig 4A). The cells responded to PRV infection with induction of IRF3(S396) phosphorylation, but this event was suppressed upon ISG15 knockdown or knockout at 24 hpi (Fig 4A). Reduced levels of IRF3 activation are due to enhanced viral transcription/translation, resulting in enhanced viral growth upon ISG15 depletion (Fig 4A and 4B). Elevated levels of IRF3 activation corresponded with significantly enhanced induction of IFN-β transcripts, which induction of IFN-β mRNA was also increased (Fig 4C). Meanwhile, ISG15 depletion statistically diminished PRV-induced IFNβ production and release (Fig 4D). Moreover, we found that IFN-β promoter activity was dramatically enhanced in ISG15-expressing WT cells, while reduced in siISG15-expressing WT cells, compared with samples transfected with empty vector (Fig 4C), further suggesting that free ISG15 accelerates IFN-β production. Overall, these findings indicate that ISG15 knockout attenuates PRV-triggered IFN-β production and release, subsequently potentiating PRV replication.
Previous reports have indicated that IFNα treatment of ISG15-deficient patient cells exhibited increased resistance to several viral infection [22]. Considering that ISG15 promotes IFN-β production during PRV infection, we wonder whether ISG15 is involved in type I IFN-mediated antiviral effect against PRV. We firstly compared the ISG15 expression patterns induced by PRV infection or IFNα stimulation, and found that PRV-induced ISGylation differs to some extent from that of IFNα, as well as some specific bands were apparent in PRV-infected cells (Fig 5A). This might be involved in some strategies used by PRV to antagonize IFNα-mediated antiviral effect. Subsequently, we found a significant increase in viral RNA and viral titers in ISG15-/- cells with or without IFNα stimulation compared with WT controls (Fig 5B and 5C), implying that complete loss of ISG15 impairs IFNα-mediated antiviral activity against PRV. With the aim of confirming that the effect observed was specifically dependent on free ISG15, we complemented the ISG15-/- cells with conjugation-deficient ISG15 mutant ISG15AA as a control. No significant difference was observed for PRV-gE expression between ISG15-/- cells transfected with ISG15AA-expressing plasmid and WT cells, indicating successful rescue of ISG15 (Fig 5D). Results showed that, as expected, transfected with ISG15AA led to a high suppression of the PRV-gE expression (Fig 5D), suggesting ISG15-deficient cells with or without IFNα stimulation are more susceptible to PRV. Notably, substantial suppression of IFNα-induced ISRE promoter activity was also observed in PRV-infected ISG15-/- cells by a dual-luciferase reporter assay (Fig 5E), implying that ISG15 deficiency can interfere with IFN signaling. To further confirm the inhibition effect of ISG15 on ISRE transcription, mRNA expression levels of three common ISGs were detected by RT-qPCR, including interferon induced protein with tetratricopeptide repeats 1 (IFIT1), oligoadenylate synthetase 1 (OAS1), and myxovirus-resistance A (MxA). Results showed that mRNA levels of IFIT1, OAS1 and MxA induced by PRV were significantly reduced in ISG15-/- cells (Fig 5F). Altogether, these data support that ISG15 deficiency interferes with type I IFN signaling and impairs IFNα-mediated antiviral effect against in PRV.
Phosphorylation of STAT1 and STAT2 is required for activation of IFN-I-mediated antiviral response [45], and STAT degradation is a common mechanism of viral IFN antagonism [46]. We next sought to investigate the mechanism by which ISG15 interferes with IFN-I signaling. Since ISG15 deficiency inhibits IFN-I signaling pathway, the ability of ISG15 to affect STAT1 and STAT2 phosphorylation after PRV infection was initially investigated. Western blot analysis showed that phosphorylation of endogenous STAT1 and STAT2 proteins were degraded in PRV-infected ISG15-/- cells, and the protein levels of total STAT1 and STAT2 remained constant before and after IFNα treatment (Fig 6A). To confirm the effect observed was specifically dependent on ISG15 deficiency, we complemented the ISG15-/- cells with ISG15AA transfection, showing no difference compared with WT cells (Fig 6A). This result clearly showed that ISG15 deficiency suppressed the activation of STAT1 and STAT2. Furthermore, opposite results were observed in siISG15-transfected WT cells (Fig 6B). Consistent with the results above, no significant difference was observed in the ratio of p-STAT1/STAT1 and p-STAT2/STAT2 between ISG15-/- cells and siISG15-transfected WT cells (Fig 6B). These data indicated ISG15 deficiency blocked endogenous STAT1 and STAT2 phosphorylation independent of IFNα treatment. Once activation is triggered by type I IFN, STAT1 and STAT2 form a heterodimer and associate with IRF9 to form the mature ISGF3 complex [47]. To examine the effect of free ISG15 on the formation of STAT1 and STAT2 heterodimerization, we performed Co-IP assays to detect the interaction between ISG15 and STAT1/STAT2. IP with STAT1 antibody followed by immunoblotting with STAT2-Y690 antibody showed ISG15 deletion inhibited the STAT1-STAT2 interaction (Fig 6C), and the same result was observed in samples with STAT2 antibody and then blotting with STAT1-Y701 antibody (Fig 6D). Moreover, no significant difference was observed between WT cells and ISG15AA-transfected ISG15-/- cells, indicating the inhibition role of free ISG15 on the formation of STAT1 and STAT2 heterodimer (Fig 6C and 6D). Similar results were obtained in siISG15-transfected WT cells, further confirming ISG15 knockout inhibit the formation of STAT1 and STAT2 heterodimerization (Fig 6E and 6F). The above data suggested that ISG15 facilitates phosphorylation and dimerization of STAT1 and STAT2.
Next, we set out to determine how ISG15 affects the activation of STAT1 and STAT2. We first examine the direct interactions between ISG15 with STAT1/STAT2 by Co-IP assays. Immunoprecipitation results showed that ISG15 was only able to interact with STAT2 (Fig 7A and 7B), hinting that STAT2 is the crucial component connecting STAT1/STAT2 to ISG15. The resulting phosphorylation of STAT1 and STAT2 allows their heterodimerization and associated with IRF9, forming the ISGF3 (STAT1/STAT2/IRF9) complex that subsequently translocated to the nucleus to initiate ISRE-dependent transcription. To further clarify the positive regulation of ISG15 on IFN-I signaling, the formation of ISGF3 heterotrimers was also examined by Co-IP. Results showed ISG15 deficiency impaired the formation of ISGF3 (Fig 7C). Next, we examined whether the impaired STAT1/STAT2 phosphorylation correlated with the inhibition of STAT1/STAT2 nuclear translocation or a reduction in steady state levels of ISGF3 members. Subcellular fractionation and Western blot analysis showed that ISG15 deficiency prevents STAT1 and STAT2 nuclear translocation in the absence of IFNα treatment (Fig 7D), indicating that ISG15 deficiency blocks the nuclear translocation of STAT1/STAT2. We further determined whether ISG15 deficiency is involved in the ISRE activity by a dual-luciferase reporter gene assay, and identified that ISG15-/- cells significantly reduced luciferase activity driven by the ISRE (Fig 7E), confirming ISG15 deficiency prevents the ISGF3-induced ISRE activity. These findings indicated that ISG15 knockout inhibited the nuclear translocation and ISGF3-induced ISRE activity by blocking the ISG15-STAT2 interaction. Taken together, these results suggested that ISG15 interacts with STAT2, which is important for ISG15 to enhance the activation of ISRE.
PRV can infect a variety wide of mammals including rodents, thus mouse model has been widely used to study PRV pathogenesis [48]. To investigate whether ISG15 possesses an antiviral role in vivo, we infected ISG15 knockout (ISG15-/-) mice with PRV for 7 days, and analyzed the clinical symptoms, survival rate, viral loads and pathological changes. The results showed that the ISG15-/- mice displayed severe typical neurological symptoms, including greatly reduced activity and pruritus at 3 days post infection (dpi), began to die at 4 dpi and all die on day 6 (Fig 8A). Whereas the PRV-infected WT mice developed only mild symptoms at 4dpi under the same condition, and started to die from the day 5 and over 50% of them eventually survived from the challenge (Fig 8A). Additionally, the ISG15 expression was remarkably upregulated during PRV infection in WT mice (Fig 8B), consistent with the results from our cell model. Because PRV infection mainly causes neurological and respiratory symptoms, and encephalitis is a key factor contributing to animal death [49]. As expected, ISG15-/- mice displayed more susceptible to PRV than WT mice, as evidenced by increased viral loads within the brains and lungs of infected ISG15-/- mice (Fig 8C). Next, to detect the degree of encephalitis and pneumonia in the PRV-infected mice, the histopathological analysis of the brains and lungs at 4dpi were performed. As illustrated in Fig 8D and 8E, the brains of ISG15-/- mice show greater inflammatory damage, necrotic neurons, more glial cells and more obvious microgliosis and hyperemia compared with WT mice (Fig 8D). Histological analyses of infected lungs showed greater inflammatory cells infiltration, severe congestion and higher level of lung tissue impairment in ISG15-/- mice in comparison with WT mice (Fig 8E). These results indicated that ISG15 deletion aggravated virus-induced pathogenicity during PRV infection in vivo. Cytokines are crucial in combating viral infection and are involved in the regulation of immune and inflammatory responses, including interleukin 1β (IL-1β), interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) [50]. Thus, we also evaluated whether ISG15 deficiency influences cytokines production in the brains and lungs of PRV-infected mice. The mRNA levels of IL-1β, IL-6 and TNF-α were significantly higher in the brains and lungs of ISG15-/- mice as compared with those of WT mice at different time points post infection (Fig 8F). Moreover, the protein concentrations of IL-1β, IL-6 and TNF-α from serum of the infected mice were measured using ELISA assay, and we found ISG15-deficient mice displayed higher cytokines concentrations (Fig 8G). This suggested that ISG15-/- mice produced more inflammatory cytokines, which is responsible for severe brain and lung pathology. These data demonstrate that ISG15 exhibits antiviral function in vivo.
The function of ISG15 in viral immunity remains an area of active investigation [25,39,51,52]. To date, the PRV-host interactions that induces ISG15 upregulation and the impact of the free ISG15 and ISG15 conjugates on PRV replication remained unexplored. Here, we established that although the ISG15 abundance were triggered by PRV infection, they are subsequently abrogated by viral gene expression at high MOI. A possible reason for this finding is that viral gene expression was high enough to prevent ISG15 induction. Our analysis with gE-deleted mutant virus demonstrated that PRV-gE played a central role in inducing free ISG15. Notably, although gE effectively reduced free ISG expression, the level of free ISG15 and ISGylated proteins during PRV infection largely depended on the MOI. Although the antiviral role of ISGylation has been reported in several viruses, studies on free ISG15 interferes with the antiviral functions are limited to a few examples. Our data provide evidence supporting the antiviral roles of free ISG15 during PRV infection using a ISG15-/- cell line. Additionally, we noticed that ISG15 had to accumulate in large amounts before virus replication to carry out its anti-PRV role. To discern the effects of free ISG15 and ISG15 conjugates on PRV replication, WT and ISG15-/- cells was overexpressed with a conjugation-deficient ISG15 mutant ISG15AA, suggesting that free ISG15 significantly inhibited PRV growth. Another powerful evidence is that UbE1L, a specific ISGylation enzyme, was depleted by shRNA in WT cells. These data consistently showed an inverse relationship between the expression of free ISG15 and PRV growth, further indicating that free ISG15 inhibits PRV growth. It is possible that when ISG15 is expressed at high levels before virus replication, no viral proteins are present to counteract the ISG15 antiviral activity (Fig 1). Another possibility is that ISG15 accumulation may promote IFN-I signaling and/or the expressions of ISGs to combat viral infection. It should be noted that the negative correlation between PRV infection and ISG15 expression was also observed from the infected mice (Fig 8B and 8C), pointing to the antiviral role of ISG15 in PRV infection in vivo. We further demonstrated here that complete loss of ISG15 resulted in a reduced IFN-β production by inhibiting IRF3 activation, and impaired IFNα-mediated antiviral response against PRV. Type I IFN signaling is a critical for controlling viral infection. Recent investigations demonstrated that ISG15 acted as a negative regulator of type I IFN signaling exerted antiviral response during viral infection [22,39]. However, our results provided some evidence supporting ISG15 as a positive regulator of IFNα-mediated antiviral response against PRV as following: 1) ISG15 deficiency inhibits IFN-β production and promotes PRV replication; 2) ISG15 deficiency impairs the IFNα-mediated antiviral activity against PRV; 3) ISG15 deficiency prevents the phosphorylation of STAT1/STAT2 and nuclear translocation of STAT1/STAT2 heterodimers; and 4) ISG15 deficiency blocked the ISGF3 formation and several ISGs transcription. Collectively, these data showed that ISG15 deletion reduces the production of the type I IFN and subsequently prevents the activation of STAT1 and STAT2 in response to PRV infection. These findings demonstrate ISG15 as a key positive regulator in IFN-I signaling and confirm its importance in the host defense against PRV infection (Fig 9), which may be more broadly against other viruses as well. We found that ISG15 was involved in two crucial steps in IFN-I signaling, including the activation of STAT1 and STAT2 and the ISGF3 formation (Figs 6 and 7). This may be partly because ISG15 knockout inhibited the IFN-I signaling pathway by blocking the activation of STAT1 and STAT2 (Fig 6A and 6B). Since ISG15 mainly localizes in the cytoplasm (Fig 7D), we found the mechanism for ISG15 to impact this step is through interactions between ISG15 and STAT2. The result that the lack of ISG15 decreases STAT1 and STAT2 phosphorylation with hindering formation of STAT1 and STAT2 heterodimer suggests that ISG15 may be involved in the formation of the ISGF3 heterotrimer. Although the regulation of the ISGF3-mediated transcription of ISGs in the nucleus in not well understood, we demonstrate that ISG15 carries out a critical positive regulator in this process. ISG15 seems to function by enhancing the ISGF3 recruitment to the promoter of ISGs and promoting the transcription of ISGs, this is complex. ISGF3 complex is essential for ISRE activation. Thus, we described that ISG15 may act as a regulator promoting ISGF3 to its ISGs promoters for efficient gene transcription (Fig 9). A similar mode of action is also observed in Bclaf1 that regulated the type I IFN responses and was degraded by alphaherpesvirus US3 [53]. From the above results, we concluded that ISG15 contributes to the IFNα-mediated antiviral response. Supporting this conclusion, ISG15-/- mice are highly susceptible to PRV infection than WT mice, as evidenced by increased mortality rates and viral loads. Moreover, ISG15 knockout mice displayed more severe encephalitis and excessive production of cytokines during PRV infection. The increased susceptibility to PRV induced lethality seemed to correlate with a cytokine storm exhibiting severe encephalitis and pneumonia. Our results contrast with the previous studies that ISG15-deficient patients who display no enhanced susceptibility to viruses in vivo [54]. This reflects the ISG15 function may vary depending on the virus and host species. Overall, our findings confirm the principal role for ISG15 as a positive regulator of type I IFN signaling by facilitating STAT1 and STAT2 activation and nuclear translocation, which provide a viable option for developing therapeutic target for controlling PRV.
C57BL/6N (WT) and ISG15-/- mice were purchased from Cyagen Biosciences, Inc. (Guangzhou, China). Animal experiments were performed in accordance with protocols approved by the National Research Center for Veterinary Medicine (Permit 20180521047).
Porcine kidney epithelial cells (PK15) and mouse embryo fibroblasts cells (MEF) were cultured at 37°C in 5% CO2 in Dulbecco’s modified Eagle medium (DMEM; Gibco) supplemented with 10% fetal bovine serum (FBS; Gibco) and 1% penicillin- streptomycin (DingGuo, Beijing, China). The PRV-QXX virus and gE-deleted PRV strains were preserved in our laboratory. For experiments, PRV was amplified in PK15 cells, and virus titers were determined using a plaque assay, as previously described [40]. The infectivity of each sample was assayed by plaque titration.
According to the protocol of the manufacturer, RNA was extracted from cells using the TRIzol reagent (Takara) and reverse transcribed using the PrimeScript RT reagent Kit (Takara). Quantitative RT-PCR was used to determine gene expression using the SYBR Green Realtime Master Mix (Takara). All values were normalized to the level of β-actin mRNA, and relative expression was calculated using the comparative cycle threshold (2-ΔΔCT) method. The cells were harvested and washed twice with PBS before being lysed with RIPA. After 15 minutes of centrifugation at 13,000 rpm, the supernatant fraction was collected. The BCA Protein Assay Kit was used to assess the protein concentration in supernatants (Beyotime Biotechnology, Shanghai, China). Equivalent quantities of each protein sample were electrophoresed on SDS-PAGE gels and transferred to PVDF membranes (Pall Corporation). The primary antibodies directed against the following proteins were: ISG15, PRV-gE, phospho-STAT1 (Tyr701), STAT1, phospho-STAT2 (Tyr690), STAT2, IRF9 (1:3000 dilution; Cell Signaling). Secondary antibodies conjugated with horseradish peroxidase against rabbit or mouse (1:5000 dilution; Santa Cruz) were used. The ECL Western blotting Analysis System was used to reveal protein bands (Tanon, Shanghai, China). Densitometry was performed with ImageJ software and standardized against β-actin.
PK15 cells were plated into a confocal dish and transfected with HA-STAT1 or myc-STAT2 plasmid. 4% paraformaldehyde was used to fix the monolayer cells, and 0.5% Triton X-100 were used to permeabilized at 4°C with (Solarbio Life Science, Beijing, China). Following a wash with PBS, cells were permeabilized in blocking solution (5% bovine serum albumin in PBS) for 1 h. Fixed cells were treated with a primary antibody specific for PRV-gE followed by an Alexa Fluor 488-conjugated secondary antibody against mouse (Proteintech). 4’, 6-diamidino-2-phenylindole (DAPI) was used to stain the cell nuclei (Solarbio). Fluorescence pictures were acquired by confocal laser scanning microscopy (Nikon).
The nonconjugative ISG15 plasmid pCAGGS-ISG15AA was constructed from pCAGGS-ISG15 using the site-directed mutagenesis kit (Beyotime), with the following primer pair: forward, 5’- TATA TGAATC TGCGCCTGCGGGCGGCCGGGACAGGG-3’, and reverse, 5’- CCCTGTCCCGGCCGCCCGCAGGCGCAGATTCATATA-3’.
Twenty-four hours prior to transfection, PK15 cells were plated in 24-well plates. The cells were pretreated with IFNα for 12 h, and then transfected with control small interfering RNAs (siRNAs), or specific siRNAs against UbE1L using with 1 μL Lipofectamine RNAiMAX reagent (Invitrogen) per well. At 12 hpt, the cells were infected with PRV (MOI = 1). At 24 hpi, the culture media was changed with fresh medium containing 1000 U/mL IFNα, which was maintained throughout the infection duration. At 24 hpi, supernatants were collected for the viral titration, and cells were extracted for Western blotting and RT-qPCR analysis. The siRNAs sequences employed in this study were as follows: UbE1L no. 1: GCACUUCCCACCUGAUAAA; UbE1L no. 2: CAGCC UCACUCUUCAUGAU.
Co-transfection of PK15 and ISG15-/- cells with the identified plasmid and the IFN-β-Luc or ISRE-Luc reporter plasmid (100 ng) plus the internal control pRL-TK reporter plasmid was performed (5 ng). Cells were treated with IFNα (1000 U/mL) for 12 h and were harvested to conduct dual-luciferase reporter assay (Promega). Firefly luciferase activity values were normalized to Renilla luciferase activity, and the relative fold changes in IFN-treated samples compared to IFN-untreated control were calculated.
PK15 and ISG15-/- cells were treated with IFNα for 12 h, and then PK15 cells were transfected with siISG15 or ISG15-/- cells were transfected with ISG15AA. After 12 hpt, the cells were infected with PRV for 24 h, and the cell lysate was cleared by centrifugation at 14,000×g for 5 min at 4°C. Primary antibodies against HA, STAT1 or STAT2 (dilution 1:1000; Proteintech) were added to the supernatants. After three washes with TBS, SDS-PAGE sample buffer was added, and proteins were separated by SDS-PAGE and immunoblotted to determine STAT1 and STAT2 interaction. IB analysis of immunoprecipitations of ISG15-/- cells co-transfected with HA-ISG15AA, HA-ISG15 or Myc-STAT2/STAT1 expression plasmids. After 12 hpt, the cells were treated with IFNα for 24 h. Antibodies against Myc (dilution 1:1000; Proteintech) were added to the supernatants. Immunoprecipitation (IP) with Protein A+G Magnetic Beads (Beyotime) was done following the manufacturer’s instructions. The IP samples with antibody against Myc were subjected to Western blotting with HA/Myc antibody.
The ISG15 knockout mice were generated from the Cyagen Biosciences (Cyagen, China). The seven-week-old male ISG15-/- mice and WT mice were randomly divided into two groups consisting of 15 mice each, respectively. The mice had free access to pelleted food and water during the experimental period. Each mouse was intraperitoneally infected with PRV (5×103 TCID50) or PBS as control. The clinical symptoms, body weight and mortality were monitored daily. The brain and lung tissues were excised to detect the viral copies by absolute quantification real-time PCR, respectively. Blood serum were also collected and kept at 4°C to detect inflammatory factor through specific antibodies by enzyme-linked immunosorbent assay (ELISA). In parallel, the brain tissues were fixed in neutral-buffered formalin for histological analysis. All the animal experiments used in this study were approved by the Animal Ethics Committee of Henan Agricultural University.
GraphPad Prism 8 software was used to conduct statistical comparisons. The difference between groups was determined using Student’s t-tests, and P values less than 0.05 were considered statistically significant (p < 0.05). The standard errors of the mean (SEM) of at least three independent experiments are shown for each data.
Click here for additional data file. Click here for additional data file. |
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PMC9648848 | 36315586 | Jesus Fernandez-Abascal,Lei Wang,Bianca Graziano,Christina K. Johnson,Laura Bianchi | Exon-dependent transcriptional adaptation by exon-junction complex proteins Y14/RNP-4 and MAGOH/MAG-1 in Caenorhabditis elegans | 31-10-2022 | Transcriptional adaptation is a powerful gene regulation mechanism that can increase genetic robustness. Transcriptional adaptation occurs when a gene is mutated and is mediated by the mutant RNA, rather than by protein feedback loops. We show here that transcriptional adaptation occurs in the C. elegans clh family of Cl- channels and that it requires exon-junction complex (EJC) proteins RNP-4, MAG-1, and eiF4AIII. Depending on which exons are deleted in distinct clh-1 alleles, different clh genes are regulated in an EJC-dependent manner. Our results support the idea that different transcriptional adaptation outcomes may be directed by the differential interaction of the EJC with its target mutant RNAs. | Exon-dependent transcriptional adaptation by exon-junction complex proteins Y14/RNP-4 and MAGOH/MAG-1 in Caenorhabditis elegans
Transcriptional adaptation is a powerful gene regulation mechanism that can increase genetic robustness. Transcriptional adaptation occurs when a gene is mutated and is mediated by the mutant RNA, rather than by protein feedback loops. We show here that transcriptional adaptation occurs in the C. elegans clh family of Cl- channels and that it requires exon-junction complex (EJC) proteins RNP-4, MAG-1, and eiF4AIII. Depending on which exons are deleted in distinct clh-1 alleles, different clh genes are regulated in an EJC-dependent manner. Our results support the idea that different transcriptional adaptation outcomes may be directed by the differential interaction of the EJC with its target mutant RNAs.
Transcriptional adaptation is a form of genetic compensation in which the mutation in a gene leads to the change in the expression level and/or pattern of related genes. This phenomenon has been confirmed in zebrafish, mouse cell cultures, and more recently in Caenorhabditis elegans [1–4]. It has also been suggested in other model organisms, including yeast, Arabidopsis, Drosophila, and mouse [5–9]. Transcriptional adaptation does not depend on the loss of protein function but rather on mutant mRNA. At least two models of transcriptional adaptation have been proposed in which the common denominator is the presence of a premature termination codons (PTC) in the mutant mRNA. One model proposed by Ma and colleagues, is based on studies in zebrafish and it involves the interaction of the PTC containing mutant mRNA with the histone modifier COMPASS complex, leading to enhancement of histone H3 Lys4 trimethylation at the transcription start site regions of the adapting genes, which causes their upregulation [4]. This type of mechanism may co-regulate genes in operons. Another model involves the degradation of the PTC containing mRNA via a process termed nonsense-mediated decay (NMD) and the formation of small RNA species that then interact with RNA binding proteins, are transferred to the nucleus, and may regulate gene expression via interaction with histone modifiers [1,2,10]. Serobyan and colleagues recently showed for the first time that transcriptional adaptation occurs also in C. elegans. The authors showed that in this organism, transcriptional adaptation of actin and titin genes requires the function of factors involved in mRNA decay, as well as of Argonaute proteins and Dicer, which are involved in small RNA maturation and transport into the nucleus, thus supporting a model involving PTC mediated NMD [3]. Furthermore, SPK-1 and RSP-6, two serine/arginine proteins involved in mRNA binding and splicing [11,12], and homologous to components of the exon-junction complex, were recently found to also participate in transcriptional adaptation [3]. The EJC has been known to enhance NMD, via recruitment of UPF1 (an RNA decay factor called SMG-2 in C. elegans), when deposited on a mRNA containing a PTC [13]. However, other EJC proteins, including core proteins Y14 (RNP-4 in C. elegans), MAGOH1 (MAG-1 in C. elegans), and eiF4AIII (F33D11.10 in C. elegans) have not been tested for their involvement in transcriptional adaptation [14–19]. Moreover, the involvement of EJC proteins in transcriptional adaptation raises the possibility of different transcriptional adaptation outcomes based on different exon/exon junctions present in distinct mutations of the same gene [9]. The conservation of basic molecular mechanisms from C. elegans to higher organisms, urges the exploitation of this pioneering organism to better understand transcriptional adaptation, a potential modifier of disease severity that could be harnessed for treatment. The clh family of Cl- channels in C. elegans consists of six genes, named clh-1 through clh-6, located on chromosome II (clh-1-3 and clh-5), chromosome V (clh-6), and chromosome X (clh-4). clh genes are homologous to the mammalian CLCN genes, are expressed in various tissues, and participate in several important biological processes [20–24]. For example, CLH-1 regulates pH in the amphid sensory organ of C. elegans, mediates Cl- efflux from amphid glia for GABA regulation of the mechanosensory neuron ASH, and participates in regulating the activity of sensory neurons that modulate the navigation in response to food [25–27]. Furthermore, the knock-out of clh-1 causes wider body and abnormal alae structure, underscoring the function of this Cl- channel in the hypodermal seam cells [20]. Among the six clh genes, clh-1 is the only one for which three different mutants have been isolated [20,25,28], and thus, it represents a useful tool to study transcriptional adaptation. In this study, using real time PCR, RNA interference, phenotypic measurements, quantification of the brood size, and nose touch behavioral assays, we sought to determine whether transcriptional adaptation occurs in the clh family and what factors might be involved. Using the three clh-1 mutants, we report here the following findings for the clh family that may apply to other gene families: 1) transcriptional adaptation is allele-specific, 2) transcriptional adaptation involves the upregulation of some genes and the downregulation of others, 3) the EJC proteins RNP-4, MAG-1, and F33D11.10 (from here on referred to as eIF4AIII) are required for transcriptional adaptation, and 4) functional compensation correlates with downregulation of genes of the same family. Our study shows that different transcriptional adaptation outcomes with variable functional compensations are directed in different mutants of the same gene, adding to our understanding of this important genetic compensation mechanism.
We first acquired three clh-1 knock-out strains (ok658, qa900, and qa901) [20,25,28] and determined whether the mRNA levels for the six clh genes was altered in these mutants (Fig 1). The clh-1(ok658) mutation consists of the deletion of 1029 bp containing exons 3–5 and a thymidine insertion at position 2911_2912 [28] (Fig 1A). In this mutant, a premature stop codon (PTC) is introduced at position 313–315 of the mRNA sequence (the wild type RNA length is 2613 bp) (S1A Fig). The clh-1(qa900) has an in-frame deletion of 1857 bp containing exons 6–9 and part of exon 10 [20], and the clh-1(qa901) has a deletion of 2071 bp containing exons 4–9 and part of exon 10 [20] (Fig 1A). In qa901 a PTC is introduced at position 511–513 of the mRNA sequence (S1A Fig). We found that in these three mutants, the levels of clh-1 mRNA are different (Fig 1B). While in alleles ok658 and qa901 the clh-1 mRNA is reduced as compared to wild type (ratio of each clh-1 mRNA in mutant versus wild type: 0.45 ± 0.05 and 0.15 ± 0.04, respectively), in qa900 mutant it is on average at the same level (1.57 ± 0.33, not statistically different than wild type). These results are consistent with the NMD phenomenon, by which PTCs are recognized as signals to target RNA for degradation [29]. Indeed, the only mutant in which clh-1 mRNA level is like wild type is qa900, which consists of an in-frame deletion. Consistent with the idea that transcriptional adaptation is not activated in mutants lacking a PTC, the mRNA levels of all the other clh genes are unaltered in qa900 (Fig 1C–1G). On the other hand, we found that in both ok658 and qa901 mutants, other clh genes had different mRNA levels as compared to wild type animals, suggesting that transcriptional adaptation is operative in these mutants. Interestingly though, there are differences between the two mutants (Fig 1B–1G and Table 1). clh-2 mRNA level is smaller in qa901 but unaltered in the ok658 mutant (Fig 1C, 0.31 ± 0.04 and 1.04 ± 0.15 versus wild type, respectively), whereas clh-4 mRNA levels are higher in ok658 and lower in qa901 (Fig 1E, 2.10 ± 0.29 and 0.47 ± 0.09 versus wild type, respectively). In both mutant alleles, the expression levels of the other genes, clh3, clh-5, and clh-6, are like in wild type (Fig 1D, 1F–1G). To confirm these data, we performed additional qRT-PCRs using probes spanning other exon boundaries in clh-1, clh-2, and clh-4 mRNAs and we obtained similar results (S1B–S1D Fig). These data show different transcriptional adaptation profiles based on the mutant clh-1 transcript.
Transcriptional adaptation is thought to provide functional compensation via change in expression of related genes (reviewed in [30]). Thus, we wondered whether qa900, the mutant allele in which there is no change in expression of the other clh genes, displayed a phenotype different from ok658 and qa901. Moreover, we asked whether ok658 and qa901, which display different mRNA profiles for clh-2 and clh-4, differed in their phenotypes. clh-1(ok658) mutants are nose touch insensitive, and clh-1(qa900) and clh-1(qa901) have wider bodies [20,27]. We thus compared the nose touch phenotype and body size across all three mutants (Fig 2A and 2B). In addition, we compared brood size since we noticed that clh-1(qa900) produced significantly fewer progenies (Fig 2C). When we compared nose touch avoidance across the three mutants, we found no statistical differences (avoidance index was 0.32 ± 0.045, 0.44 ± 0.052, and 0.44 ± 0.05 for ok658, qa900, and qa901, respectively) (Fig 2A) [27]. Thus, the nose touch insensitive phenotype is shared by the three clh-1 mutants, suggesting that this phenotype is not functionally compensated. On the other hand, we found differences in the other two phenotypes. For body size, we found that while ok658 mutants are similar to wild type in width and length (width, WT = 57.88 ± 0.66 μm, ok658 = 59.31 ± 0.67 μm and length, WT = 1050 ± 10.16 μm, ok658 = 1036 ± 11.09 μm, respectively), qa900 and qa901 mutants are wider and shorter (width, qa900 = 68.31 ± 1.43 μm, qa901 = 65.74 ± 1.36 μm and length, qa900 = 869.9 ± 11.41 μm, qa901 = 952.4 ± 12.95 μm, respectively), as it was previously reported (S2A Fig) [20]. The difference in body proportions is particularly evident in Fig 2B, where we plotted the ratio between width and length. These data show that qa900 mutant has the most severe phenotype having widest and shortest body. Thus, these results suggest that changes in expression of other clh genes in clh-1(ok658) and clh-1(qa901) mutants may result in compensation of the wider and shorter body phenotype. When we analyzed the brood size in the three clh-1 mutants, we once again found that qa900 was the mutant with the most severe phenotype having the smallest brood size among the three mutants (brood size in WT, ok658, qa900, and qa901 was 249.8 ± 12.39, 204.8 ± 18.21, 106.2 ± 2.7, and 261 ± 14.9, respectively) (Fig 2C). Taken together, these data suggest that the body size and the brood size may be phenotypes that are transcriptionally compensated in both ok658 and qa901 mutants.
Transcriptional adaptation is induced by mutations in the genome but not by the knockdown of a gene [1]. To gather further support that the changes in clh-2 and clh-4 mRNA levels seen in ok658 and qa901 mutants might be due to transcriptional adaptation, we performed clh-1 knockdown experiments. While we confirmed knockdown of clh-1 (Fig 3A), we found no differences in the levels of mRNA of the other clh genes (Fig 3A–3F). These data lend further support to the idea that the different mRNA levels for clh-2 and clh-4 observed in ok658 and qa901 are due to transcriptional adaptation. Next, we analyzed body and brood size in clh-1 RNAi worms and found that these phenotypes were significantly different than wild type (Fig 2B and 2C). Parenthetically, clh-1 RNAi also causes nose touch insensitivity [27]. These data further support the idea that the wider and short body, and the smaller brood size are due to uncompensated loss of clh-1 function in C. elegans. To test whether changes in clh-2 and clh-4 gene expression in clh-1 mutants were specific, we analyzed the transcript of titin-related gene sax-3 in the three mutants (Fig 3G). sax-3 mRNA undergoes transcriptional adaptation in mutants of the titin gene unc-89, so we added unc-89 mutant as positive control [3]. As previously reported by Serobyan and colleagues, in unc-89 mutants the adapting gene sax-3 is upregulated (Fig 3G and Table 1), while the mRNA level of unc-89 transcript itself is lower than in WT, consistent with the NMD process (Fig 3H) [3]. We found that neither sax-3 or unc-89 mRNA levels were altered in ok658 and qa901 mutant alleles (Fig 3G and 3H). Interestingly, we found slight downregulation of sax-3 and slight upregulation of unc-89 in qa900 mutant, that might be related to the severely altered body size and proportions in this mutant, given that unc-89 and sax-3 encode for titin-related genes. Taken together, these data support that changes in the expression of clh-2 and clh-4 seen in ok658 and qa901 mutants are not stochastic.
To gather further support for transcriptional adaptation, we looked at the similarity between clh-1 and the other clh genes. Indeed, transcriptional adaptation has been reported to involve more frequently similar genes, even though non-similar genes have been also shown to undergo up or down regulation in PTC-bearing mutants [2]. Using blastn and a word size of 20, we found significant similarity between clh-1 and clh-2, clh-3, clh-4, and clh-5. More specifically, we found that clh-2 contains a continuous stretch of 20 bp that is 100% identical to a stretch of nucleotides in clh-1 exon 13. In addition, clh-2 shares 68% to 85% homology with clh-1 in stretches of nucleotides varying in length between 32 and 228 bp across 785 bp total. We also found significant homology with clh-3 (70% identity across 320 bp), clh-4 (66% identity across 113 bp), and clh-5 (88% identity across 49 bp). These similarities further support the idea that the differences seen in clh-2 and clh-4 mRNA levels in clh-1(ok658) and clh-1(qa901) might be due to transcriptional adaptation. To gather experimental evidence for this conclusion, we quantified the pre-mRNA levels of clh-2 and clh-4 and of clh-4 in the qa901 and ok658 mutants, respectively. We found that the pre-mRNA levels of clh-2 and clh-4 are smaller than in WT in the qa901 mutant, and that the pre-mRNA level of clh-4 is higher than in WT in the ok658 mutant (S1E–S1F Fig). Thus, the changes in steady state RNA levels of clh-2 and clh-4 seen in qa901 and ok658 (Figs 1C and 1E and S1C-) correspond to changes in transcription of these genes, supporting the idea that they are the result of transcriptional adaptation. Next we asked whether ERGO-1, a protein involved in small RNA biogenesis [32–35], and required for transcriptional adaptation in C. elegans titin and actin families [3], was required for changes in clh-2 and clh-4 mRNA levels in clh-1 mutants. First, consistent with the idea that ERGO-1 regulates transcriptional adaptation downstream of mRNA decay, we found that the mutant clh-1 mRNA levels were still downregulated in ergo-1 RNAi (S1G Fig), whose effectiveness was confirmed by qRT-PCR (S1J Fig) [3]. Second, we found different outcomes for clh-2 and clh-4 mRNAs in ergo-1 RNAi. While the mRNA levels of clh-2, which were downregulated in qa901 (Fig 1G), were now upregulated (S1H Fig, upregulation of clh-2 in ergo-1 RNAi was also seen with clh-2 probe spanning 11–12 exons: 2.247 ± 0.29 relative to WT), the mRNA levels of clh-4 for ok658 and qa901 were unchanged as compared to control conditions (S1I Fig). These results support the requirement for small RNA biogenesis for clh-2 downregulation in qa901, but not for the changes in clh-4 mRNA levels in ok658 and qa901. Intriguingly, clh-2 is the only clh gene in which a 20 bp sequence sharing 100% identity with clh-1 is found. Taken together, these results suggest that different transcriptional adaptation mechanisms may be operative in the same mutant to target different adapting genes.
Serobyan and colleagues reported a list of proteins involved in transcriptional adaptation, including splicing factors spk-1 and rsp-6 [3]. However, the core components of the EJC Y14/RNP-4, MAGOH/MAG-1, and eIF4AIII were not included in their study [14,15,17–19,36]. The EJC is involved in transcriptional adaptation via its role in NMD. EJCs are deposited on the mRNA during splicing in the nucleus, remain on mRNAs even after transport to the cytosol, and are then removed from the mRNA by the ribosome during the pioneer round of translation. If a PTC is present upstream of an EJC, then this is not dislodged from the mRNA during translation leading to the recruitment of RNA decay factor UPF1 (smg-2 in C. elegans) that degrades the RNA [13]. To experimentally determine the potential role of EJC proteins in transcriptional adaptation in clh-1 mutants, we performed knockdown by RNAi feeding in wild type and clh-1 mutants, as well as in the unc-89 mutant as a control (Figs 4, S3 and S4 and Table 2). When we analyzed the mRNA levels of the adapting genes clh-2 and clh-4 in clh-1 mutants, and of sax-3 in unc-89 mutant treated with rnp-4 RNAi, we found significant reduction of transcriptional adaptation (Figs 4A–4E, S3G and S3H). Thus, the clh-2 mRNA levels were no longer downregulated in qa901 worms but were like the levels seen in wild type (Fig 4A) and, therefore, higher than the levels seen in qa901 grown under control conditions (Fig 4B). The clh-4 mRNA levels were no longer up- and downregulated in ok658 and qa901 worms, respectively (Fig 4C), thus they were lower and higher than the levels observed in these mutants grown in control conditions (Fig 4D and 4E, respectively), and the sax-3 mRNA levels were no longer upregulated in unc-89 worms (S3G Fig). We also found that the clh genes that do not undergo transcriptional adaptation in both clh-1 mutants remained unaltered when rnp-4 was knocked down (S3A–S3C Fig). Interestingly, the mRNA levels of the mutant clh-1 were still lower than the wild type (S3D Fig). However, comparison with the control conditions reveled that the mutant clh-1 RNA is not as low in qa901, suggesting reduced degradation in this mutant (S3E Fig), a phenomenon not observed in ok658, despite similar rnp-4 knockdown efficiency in the two mutants (S3F Fig). To determine whether the effects of rnp-4 RNAi were specific, we knocked down the gene ZC155.4 which encodes an ortholog of human glycerophosphodiester phosphodiesterase 1 (GDE1). The gene product of ZC155.4 is predicted to participate in lipid metabolic processes and is not expected to be involved in transcriptional adaptation or RNA processing. We found that knock down of ZC155.4 had no effect on the mRNA levels of clh-1, clh-2, and clh-4 in clh-1(ok658) and clh-1(qa901) mutants (S3I–S3K Fig), supporting the specificity of the effects seen in rnp-4 RNAi. The RNAi of the other EJC component, MAG-1 caused also substantial decrease of transcriptional adaptation in both the clh and titin families (Figs 4F–4J and S4D). Thus, clh-2 and clh-4 mRNA levels were no longer downregulated in qa901 (Fig 4F, 4G, 4H and 4J), the upregulation of clh-4 in ok658 worms was significantly reduced (Fig 4H and 4I), and the upregulation of sax-3 in unc-89 mutants was absent (S4D Fig). As seen for rnp-4 RNAi, mag-1 RNAi did not affect the downregulation of clh-1 transcript in ok658 mutant but reduced the downregulation of clh-1 in qa901, suggesting reduced degradation of the clh-1 transcript in this mutant (S4A and S4B Fig). In the case of mag-1 RNAi too, knockdown shows the same effectiveness in the two clh-1 mutants (S4B Fig). Finally, we tested the involvement of the core component of the EJC eiF4AIII and obtained results similar to the ones obtained with RNAi of rnp-4 and mag-1 (Figs 4K–4O and S4E–S4G). Taken together, these findings are consistent with the idea that the process of NMD is EJC-dependent in C. elegans. Although they also reveal that, at least in the case of ok658 mutant, knockdown of components of the EJC does not lead to changes in degradation of the mutant RNA, suggesting an EJC-independent NMD mechanism in this clh-1 mutant [37,38]. Thus in C. elegans EJC-dependent and EJC-independent NMD appear to exist side by side [39].
Serobyan and colleagues reported that smg-2 and smg-4 genes are required for transcriptional adaptation of unc-89 alleles, whereas smg-6 is required for the transcriptional adaptation in act-5 [3]. SMG-2, SMG-4, and SMG-6 are all RNA decay factors that have been implicated in transcriptional adaptation in zebrafish embryos and mouse cell lines, in addition to C. elegans [2,4]. We thus wondered whether transcriptional adaptation in clh-1 mutants required SMG-2, the C. elegans ortholog of the ATP-dependent RNA helicase upstream frameshift 1(UPF1) [40]. We found that the knockdown of smg-2, partially blocked the transcriptional adaptation of the adapting gene clh-2 in qa901 worms (Fig 4P and 4Q) and fully blocked changes in the clh-4 transcript in ok658 and qa901 worms (Fig 4R–4T). As reported by Serobyan and colleagues, smg-2 knockdown blocked the transcriptional adaptation of sax-3 (S4K Fig) and the decay of unc-89 mRNA in unc-89 worms (S4L Fig) [3]. Furthermore, a block of decay of the clh-1 transcript was observed in smg-2 RNAi (S4H and S4I Fig), and the efficiency of the RNAi treatment was confirmed by qRT-PCR (S4J Fig), thus confirming that mutant clh-1 transcripts are degraded via the NMD pathway. To conclude, the RNA decay factor smg-2 is required for degradation of the mutant clh-1 transcripts and for the changes in clh-2 and clh-4 mRNA levels seen in clh-1(ok658) and clh-1(qa901) mutants.
In the knockdown experiments we performed, we grew worms from egg to adult on RNAi plates [41]. To determine whether RNP-4 and MAG-1 are required in development for transcriptional adaptation, we repeated knockdown experiments by growing worms on RNAi plates from the last larval stage L4 to adulthood (24 hours) (S5A–S5L Fig). Under these conditions, we obtained results that were overall similar to the results obtained from animals that were reared from egg to adult on the RNAi plates, albeit the block of clh-4 transcriptional adaptation appeared weaker (compare S5D–S5F and 5J–5L Fig with Fig 4C–4E and 4H–4J). Similarly, smg-2 RNAi in late larvae/young adults reduced transcriptional adaptation, though not to the same extent as in experiments in which animals were reared on RNAi plates from egg to adulthood (S5M–S5N Fig). These results support the idea that RNP-4, MAG-1, and SMG-2 are not essential during development for the transcriptional adaptation observed in the clh family.
The three clh-1 mutants we analyzed here have similar nose touch avoidance phenotype, but different brood size and body’s width/length phenotypes (Fig 2). Interestingly, clh-1(qa900) which does not contain a PTC and in which we did not observe any change in the levels of the other clh genes’ transcripts, displays the most severe phenotypes having the largest width/length ratio and the smallest brood size. We thus wondered whether knockdown of the factors we found to be involved in transcriptional adaptation of the clh family in mutants clh-1(ok658) and clh-1(qa901) would exacerbate the phenotypes in these mutants and render them more similar to clh-1(qa900). Thus, we analyzed body’s size in rnp-4, mag-1, and smg-2 knockdown animals and brood size in rnp-4 and smg-2 knockdown. The extremely low number of progenies in all strains treated with eiF4III RNAi prevented meaningful analysis of brood size under these conditions (S2I Fig). We found that knockdown of rnp-4 and mag-1 exacerbated the width/length ratio in qa901 worms, while it did not have any effect in ok658 (Figs 5A, 5B and S2C–S2F). Interestingly, smg-2 knockdown, which reduced transcriptional adaptation in ok658 and qa901 mutants, but not as effectively as rnp-4 and mag-1 knockdown, did not have any effect on the body size of any of the mutants (Figs 5C, S5G and S5H). The brood size was on average decreased in all the strains in rnp-4 knockdown, as previously reported [19]. However, the effect on the brood size of rnp-4 knockdown was most evident in qa901 mutant which produced a number of progenies as low as qa900 mutant (Fig 5D). Knockdown of smg-2 did not have any effect on the brood size either despite leading to at least partial block of transcriptional adaptation as mentioned above (Fig 5E). The results with smg-2 RNAi suggest that the level of transcriptional adaption under these conditions is still sufficient to induce functional compensation. These results support the idea that in qa901 mutant, knockdown of rnp-4 and mag-1 causes the worsening of the phenotypes, suggesting that in this mutant transcriptional adaptation leads to functional compensation. It is interesting to note that in qa901, both clh-2 and clh-4 genes are downregulated. On the contrary, even though we observe block of upregulation of clh-4 in ok658 mutant treated with rnp-4 and mag-1 RNAi, the phenotypes remain unaffected, suggesting that other genes compensate the phenotypes in ok658, perhaps in an rnp-4 and mag-1 independent manner.
With the expansion of genetic tools that engineer mutations in genes, there is a growing interest in understanding mechanisms of genetic compensation. Indeed, across species, genetic mutations often do not result in any apparent phenotype. C. elegans is a genetically amenable organism that can be used for rapidly advancing our understanding of the mechanisms of genetic compensation, including transcriptional adaptation. The work that we present here adds to our understanding of transcriptional adaptation in C. elegans, so far described only in another manuscript [3]. We report here that the EJC proteins RNP-4, MAG-1, and eiF4AIII are needed for transcriptional adaptation in the clh and titin families and show that the transcriptional adaptation outcome of the adapting genes depends on the specific PTC-bearing mutant alleles. More specifically, transcriptional adaptation in the clh family can result in the downregulation or the upregulation of the adapting genes, it is, at least in part, dependent on the RNA biogenesis factor ERGO-1 [3], while the RNA decay factor SMG-2 appears commonly required, at least for transcriptional adaptation in the 2 clh-1 mutants we analyzed [13,33–35]. Our data support that the introduction of a premature stop codon (PTC) is a key factor in promoting transcriptional adaptation in C. elegans. Indeed, we show that qa900 mutant, consisting in an in-frame deletion (Figs 1A and S1), does not lead to change in the expression of other clh genes. On the contrary, ok658 and qa901 alleles introduce PTC and lead to changes in expression level of clh-2 and clh-4 (Fig 1), in line with what was shown by Serobyan and colleagues for the actin and titin families in C. elegans [3]. Our smg-2 RNAi data support the idea that the PTC-bearing mRNAs undergo degradation via the NMD mechanism [42] and thus, that the lower levels of mutant clh-1 detected in these mutants are not likely due to reduced transcription of clh-1. Interestingly though, this is as far as the commonality of transcription adaptation mechanism between the two PTC-bearing clh-1 mutants goes. Our analysis of the requirement for EJC proteins and for RNA biogenesis factor ERGO-1 for transcriptional adaptation in ok658 and qa901 reveals differences between the two mutants. While changes in clh-2 and clh-4 expression levels in both ok658 and qa901 mutants are blocked when EJC proteins are knocked down (Table 2), the outcome of this treatment on the PTC-bearing clh-1 mRNA level is different. While in ok658 it is still degraded, in qa901 it is not (S3E, S4B and S4F Figs). This result suggests that in ok658 the EJC complex is required for transcriptional adaptation (perhaps indirectly via other genes) [2], but it is not required for NMD of the mutant clh-1 mRNA, consistent with an EJC-independent NMD in this mutant as previously described in C. elegans and in other systems [37,39,43,44]. This model would still be consistent with the fact that smg-2 RNAi reduces transcriptional adaptation in both ok658 and qa901 mutants, given that at least in human cell lines UPF1, the mammalian homolog of SMG-2, is needed for both EJC-dependent and EJC-independent NMD [39]. Future studies in which the entire transcriptome is compared between ok658 and qa901 mutants may be needed to shed more light on the difference between mechanisms of transcriptional adaptation in these mutants. Importantly, in qa901, where we find requirement for ERGO-1, we also observe worsening of the phenotype when EJC proteins are knocked down, consistent with the idea that in this mutant transcriptional adaptation leads to at least partial functional compensation. Indeed, under rnp-4 and mag-1 RNAi conditions, qa901 body and brood size are similar to qa900 mutant. Interestingly though, in this mutant both clh-2 and clh-4 mRNA are downregulated. While the study of transcriptional adaptation has been focusing primarily on understanding how adapted genes become upregulated, the downregulation of genes has been observed before [2,3]. For example, in act-5(dt2019) mutants, while act-3 becomes upregulated, act-4 becomes downregulated [3]. On a larger scale, El-Brolosy and colleagues reported the downregulation, in addition to the upregulation, of hundreds of genes in three mouse knock-out cell lines [2]. One attractive model is that small RNAs produced by the degradation of mutant clh-1 directly silence clh-2 and clh-4 mRNA [45]. Alternatively, small RNA, once bound to RNA binding proteins, may act on the promoter regions of these genes (reviewed in [46]). Intriguingly, clh-1 shares some identity with clh-4, and even more so, with clh-2 (100% identity over a stretch of 20 nucleotides). Our data also show that the downregulation of clh-2 and/or of clh-4 lead to functional compensation. Although the involvement of other genes cannot be excluded, how might the downregulation of a gene lead to functional compensation? This is not clear, but effects mediated by antisense transcripts or the proteins themselves, especially if the proteins have opposite effects on cellular physiology, can be envisioned [2]. In neither ok658 nor qa901 the nose touch avoidance phenotype is compensated. Similarly, mutations in the C. elegans Na+/K+-ATPase α-subunits eat-6 and catp-1 cause upregulation of the homologous gene catp-2. However, the upregulation of catp-2 does not compensate for the nose touch insensitive phenotype of eat-6 and catp-1 mutants [47]. Other examples of failed functional compensation can be found in zebrafish. For example, vegfaa (Vascular endothelial growth factor A) mutants, there is an upregulation of the related gene vegfab, but these animals still show vascular hypoplasia [48]. Failed functional compensation in transcriptional adaptation may be associated with phenotypes, such as nose touch avoidance or vascularization, that are still compatible with survival and reproduction. Finally, we must caution on the fact that in our work we have not analyzed a full locus deletion of clh-1; instead, we have compared our results obtained in PTC-bearing ok658 and qa901 mutants with the in-frame deletion qa900 where clh-1 RNA is still present. On the contrary Serobyan and colleagues using an RNA-less unc-89 mutant were able to show that transcriptional adaptation indeed requires the presence of the mutant mRNA [3]. Thus, we cannot exclude the possibility that other mechanisms such as loss of CLH-1 function are at play here. In summary, we have shown here that the EJC plays a key role in transcriptional adaptation in C. elegans. Furthermore, we report that transcriptional adaptation can lead to either the up or down regulation of related genes, depending on the mutant allele, and that functional compensation is variable not only depending on the mutation but also depending on the phenotype. Finally, we have confirmed that transcriptional adaptation requires the presence of PTC-bearing mRNA. The data presented here urge consideration of this genetic mechanism of compensation whenever working with C. elegans knock-out strains. The good practice of the worm community of analyzing knock-out, knockdown, and rescue strains must be maintained to safeguard from misinterpretation of phenotypes. Furthermore, the genetic amenability of C. elegans makes it an excellent model to advance the study the molecular underpinnings of this important, yet still poorly understood, phenomenon.
Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Laura Bianchi ([email protected]).
Experiments were performed using healthy 1 day old adult hermaphrodites. Nematodes were grown on standard grow medium (NGM) seeded with Escherichia coli (OP50 strain) and kept at 20°C. N2 Bristol was the wild type strain.
N2 strain from CGC was used as wild type [49]. The clh-1 mutants, strain RB1052 trpa-1(ok999), and strain VC40193 unc-89(gk509355) were also purchased from CGC [20,28,50]. Strains XA900 and XA901 were originally outcrossed at least 4 times [20]. Strain BLC588 clh-1(ok658) was obtained after outcrossing 3 times the RB833 strain. The unc-89 strain used in this study (BLC532) was obtained after outcrossing 3 times the VC40193 strain. The full list of the strains used in this study is reported in Table 3.
Gravid adults were rinsed off plates using 2 ml of M9 buffer (22.1 mM KH2PO4, 42.3 mM Na2PO4, 85.6 mM NaCl) and then transferred to tubes for centrifugation at 4300 rpm for 5 minutes. Pelleted worms were resuspended in 400 μl of bleach solution (22.7% bleach, 0.1 M NaOH). When ~ 90% of the eggs were released, the reaction was stopped with 10 ml of M9 buffer. Eggs were centrifuged at 4300 rpm for 5 minutes and washed with M9 buffer twice. The pelleted eggs were then resuspended in 100 μl M9 buffer and inoculated onto seeded NGM plates.
One day old adult hermaphrodites were rinsed off plates using 2 ml of M9 buffer and transferred into tubes containing 12 ml M9 buffer. Tubes were centrifuged for 3 minutes at 2400 rpm prior to the pellet being washed 4 times with M9. The pellet was then resuspended in 1 ml TRIzol reagent (ThermoFisher) and exposed to 6 cycles of liquid N2 for 30 seconds and bath of 37°C for 2 minutes. The solution was then transferred into fresh tubes and mixed with 200 μl chloroform. After 5 minutes incubation on ice, the tubes were centrifuged for 15 minutes at 15000 rpm at 4°C. The top transparent layer was then transferred to a fresh tube containing 800 μl of isopropanol and centrifuged for 10 minutes at 15000 rpm at 4°C. The pellet was then resuspended in 75% ethanol solution and centrifuged again for 5 minutes at 15000 rpm at 4°C. Finally, the pellet was resuspended in water and heated at 62°C for 10 minutes. The RNA concentration was measured using a spectrophotometer and only samples with OD260/280 between 1.8 and 2 were used for further analysis. One μg of RNA per sample was used for reverse transcription with the High-Capacity RNA-to-cDNA kit (Applied Biosystems) according to manufacturer’s instructions. For PCR amplification, 25 ng of cDNA were used with FAM dye labeled probes (Table 4, ThermoFisher) and TaqMan Universal Master Mix II in a CXF Connect Real-Time PCR detection system (Bio-Rad) following manufacturer’s instructions. For the rnp-4 gene, the SYBR Green PCR method was used due to lack of commercial TaqMan rnp-4 FAM dye labeled specific probe. Briefly, SYBR Green qPCR experiments were performed using PowerUp SYBR Green PCR Master Mix (Applied Biosystems, USA), following the manufacturer’s instructions. cDNA (25 ng) and 50 nM of the paired-primer mix were used for each reaction. The melting curve was performed and analyzed to make sure there were no nonspecific PCR products. To measure pre-mRNA levels with SYBR chemistry, extracted RNA samples were processed to remove genomic DNA before reverse transcription using a DNase kit (Qiagen, Netherlands) and following manufacturer’s instructions. The pre-RNA clh-2 and clh-4, the rnp-4 and pmp-3 primers’ sequences used for the SYBR Green method are shown in Table 4. The gene pmp-3 was used as an endogenous calibrator for both methods of qPCR. For the SYBR Green method, pmp-3 primers were designed at the same location as pmp-3 FAM dye labeled probe. The pmp-3 primers are also in Table 4. The relative mRNA levels were calculated using the 2-ΔΔCt method [51,52]. Wild type was used as the reference sample, taken as 1-fold expression level, is indicated on each figure legend.
To measure body length and width, we immobilized synchronized 1 day old adults in 2% agarose (in M9 buffer) pads using 100 mM sodium azide. We used a Evos FL Auto 2 Imaging System (Invitrogen) microscope to acquire images with an 40x objective (Olympus). Acquisition was done with the Evos FL Auto software. To determine the length, the distance from the tip of the nose to the end of the tail was measured in animals that were laying straight on the agar pad. The width was determined by measuring the distance from vulva opening to back of the worm. Fiji (ImageJ) was used in both cases for data analysis [53]. Measures and ratio width/length were plotted on Prism 8 for Windows (Version 8.4.2).
Nose touch assays were performed as previously described [27]. In brief, healthy 1 day old adults were placed in a NGM plate containing a thin layer of OP50 and allow to crawl for 30 minutes. An eyelash was placed perpendicular to a forward moving animal so the worm would touch it with the nose while crawling forward. A response was recorded as positive if the worm showed an aversive response (reversal or head withdraw) or as negative if the worm kept moving forward over, under or along the eyelash. Each worm was tested 5 times with an interval of at least 30 seconds between touches. The average response of each worm was calculated and used for data curation (see S1 Table). The experiments were performed blind to genotype.
The quantification of the brood size was performed as previously described [54]. Individual worms were picked at L1 stage into separate plates containing empty OP50 or HT115 E. coli transformed with the target RNAi construct. For L4 to young adult assays, worms were picked at L4 stage. After reaching day one adulthood, worms were transferred into fresh plates for 5 consecutive days. Progenies from each plate were counted at late larva to adult stage.
A 770 bp exon-rich sequence from the genomic rnp-4 gene was amplified by PCR using the following primers: forward (5’CTTAAGCTTAGAGATGGAGGATGTGGTGGC) and reverse (5’GTAGCTAGCTCAGCGCTTTCCAGAAGTCT). A 1128 bp exon-rich sequence from the genomic clh-1 gene was amplified by PCR using the following primers: forward (5’GACTCAGGCTTAGGCTTAGG) and reverse (5’CTCCAACCACGGCATAAAGTCC. A 995 bp exon-rich sequence from the genomic eiF4AIII gene was amplified by PCR using the following primers: forward (5’CGTCGTAATCTTCGTACCCGAG) and reverse (5’CTCCGTTGGATAGTATTTGGGTCTTAG). The PCR products were then separately cloned into a L4440 vector containing T7 polymerase promoters to read the sequence in both sense and antisense. The vectors were transformed into HT115 E. coli that were then used to inoculate NGM plates containing IPTG. The HT115 E. coli strains expressing the L4440 vector containing a 584 bp exon-rich sequence from the genomic mag-1 gene, a 651 bp exon-rich sequence from the genomic smg-2 gene, a 1155 bp exon-rich sequence from the genomic ZC144.5 gene, or a ≅ 1 kb exon-rich sequence from the genomic ergo-1 gene were part of the Ahringer library [55] and were a gift from Kevin Collins. C. elegans eggs were seeded on the RNAi plates and allowed to grow for 2.5 days to adulthood prior to RNA extraction [45]. We observed reduced brood size in rnp-4 RNAi plates, as previously reported, supporting RNAi efficiency in our hands [19], as well as almost complete sterility associated with eiF4AIII RNAi treatment.
For qRT-PCR, the values obtained with the 2-ΔΔCt method, which avoid a false depiction of the variation, were used for statistical analysis between the target samples and their own reference sample (wild type or control) using unpaired t-test [56,57]. For phenotypic comparisons, ANOVA followed by Tukey’s was used. The statistics used for each graph are reported in the figure legends. The software Prism 8 for windows, version 8.4.2. was used.
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PMC9648850 | 36315598 | Maeve McLaughlin,David M. Hershey,Leila M. Reyes Ruiz,Aretha Fiebig,Sean Crosson | A cryptic transcription factor regulates Caulobacter adhesin development | 31-10-2022 | Alphaproteobacteria commonly produce an adhesin that is anchored to the exterior of the envelope at one cell pole. In Caulobacter crescentus this adhesin, known as the holdfast, facilitates attachment to solid surfaces and cell partitioning to air-liquid interfaces. An ensemble of two-component signal transduction (TCS) proteins controls C. crescentus holdfast biogenesis by indirectly regulating expression of HfiA, a potent inhibitor of holdfast synthesis. We performed a genetic selection to discover direct hfiA regulators that function downstream of the adhesion TCS system and identified rtrC, a hypothetical gene. rtrC transcription is directly activated by the adhesion TCS regulator, SpdR. Though its primary structure bears no resemblance to any defined protein family, RtrC binds and regulates dozens of sites on the C. crescentus chromosome via a pseudo-palindromic sequence. Among these binding sites is the hfiA promoter, where RtrC functions to directly repress transcription and thereby activate holdfast development. Either RtrC or SpdR can directly activate transcription of a second hfiA repressor, rtrB. Thus, environmental regulation of hfiA transcription by the adhesion TCS system is subject to control by an OR-gated type I coherent feedforward loop; these regulatory motifs are known to buffer gene expression against fluctuations in regulating signals. We have further assessed the functional role of rtrC in holdfast-dependent processes, including surface adherence to a cellulosic substrate and formation of pellicle biofilms at air-liquid interfaces. Strains harboring insertional mutations in rtrC have a diminished adhesion profile in a competitive cheesecloth binding assay and a reduced capacity to colonize pellicle biofilms in select media conditions. Our results add to an emerging understanding of the regulatory topology and molecular components of a complex bacterial cell adhesion control system. | A cryptic transcription factor regulates Caulobacter adhesin development
Alphaproteobacteria commonly produce an adhesin that is anchored to the exterior of the envelope at one cell pole. In Caulobacter crescentus this adhesin, known as the holdfast, facilitates attachment to solid surfaces and cell partitioning to air-liquid interfaces. An ensemble of two-component signal transduction (TCS) proteins controls C. crescentus holdfast biogenesis by indirectly regulating expression of HfiA, a potent inhibitor of holdfast synthesis. We performed a genetic selection to discover direct hfiA regulators that function downstream of the adhesion TCS system and identified rtrC, a hypothetical gene. rtrC transcription is directly activated by the adhesion TCS regulator, SpdR. Though its primary structure bears no resemblance to any defined protein family, RtrC binds and regulates dozens of sites on the C. crescentus chromosome via a pseudo-palindromic sequence. Among these binding sites is the hfiA promoter, where RtrC functions to directly repress transcription and thereby activate holdfast development. Either RtrC or SpdR can directly activate transcription of a second hfiA repressor, rtrB. Thus, environmental regulation of hfiA transcription by the adhesion TCS system is subject to control by an OR-gated type I coherent feedforward loop; these regulatory motifs are known to buffer gene expression against fluctuations in regulating signals. We have further assessed the functional role of rtrC in holdfast-dependent processes, including surface adherence to a cellulosic substrate and formation of pellicle biofilms at air-liquid interfaces. Strains harboring insertional mutations in rtrC have a diminished adhesion profile in a competitive cheesecloth binding assay and a reduced capacity to colonize pellicle biofilms in select media conditions. Our results add to an emerging understanding of the regulatory topology and molecular components of a complex bacterial cell adhesion control system.
The ability of microbial cells to adhere to surfaces and form biofilms is often a key determinant of fitness in both clinical and non-clinical contexts [1–3]. Colonization of substrates can support energy production [4], protect cells from toxic compounds [5,6], and shield cells from grazing protist predators [7]. However, competition for resources in a multicellular biofilm can also slow growth; thus, there are evolutionary tradeoffs between surface attached and planktonic lifestyles [8]. Given that the fitness benefit of surface attachment varies as a function of environmental conditions, it follows that the cellular decision to adhere to a substrate is highly regulated. Gram-negative bacteria of the genus Caulobacter are common in aquatic and soil ecosystems [9] and are dominant members of mixed biofilm communities in freshwater [10]. Caulobacter spp. often produce a secreted polar adhesin known as the holdfast, which enables high-affinity attachment to surfaces [11] and robust biofilm formation [12]. In the model Caulobacter species, C. crescentus, holdfast development is regulated at many levels. The transcription of holdfast synthesis genes exhibits periodic changes across the cell cycle, consistent with the developmental regulation of holdfast synthesis [13,14]. In addition, the small protein, HfiA, is a potent post-translational inhibitor of holdfast synthesis that itself is controlled by cell cycle and environmental signals [15–17]. Holdfast biogenesis is also influenced by mechanical cues [18–20], while the second messenger cyclic-di-GMP affects both synthesis [19] and physical properties of [21] the holdfast. Additionally, an elaborate regulatory pathway comprised of multiple two-component signaling (TCS) proteins and one-component regulators controls holdfast development and surface attachment [16]. We have previously shown that a C. crescentus strain expressing a non-phosphorylatable allele of the lovK sensor histidine kinase (lovKH180A) overproduces holdfast and, consequently, has an enhanced adhesion phenotype in a biofilm assay. The lovKH180A adhesion phenotype requires the presence of spdS-spdR two-component system genes and the hybrid histidine kinase skaH gene [16]. Two XRE-family transcription factors, RtrA and RtrB, function downstream of the TCS regulators to promote holdfast synthesis by directly repressing transcription of the holdfast inhibitor, hfiA (Fig 1A). Though rtrA and rtrB clearly contribute to holdfast regulation downstream of the adhesion TCS proteins, we hypothesized that there were additional regulators of C. crescentus holdfast biosynthesis in this pathway. Our hypothesis is based on the observation that deletion of both rtrA and rtrB does not completely abrogate holdfast synthesis when the TCS pathway is constitutively activated [16]. To search for these postulated downstream regulators, we used a transposon sequencing approach to select for insertions that attenuate the hyper-holdfast phenotype of a lovKH180A mutant. Our selection uncovered a gene encoding a hypothetical protein that we have named RtrC, which functions as both a transcriptional activator and repressor in C. crescentus. RtrC binds a pseudo-palindromic DNA motif in vivo and in vitro and activates holdfast synthesis downstream of the lovK-spdSR-skaH TCS ensemble by directly repressing transcription of the holdfast inhibitor, hfiA. RtrC, along with the response regulator SpdR, and the transcription factor RtrB form an OR-gated type I coherent feedforward loop (C1-FFL) that regulates hfiA transcription. C1-FFL motifs are known to buffer gene expression against transient loss of regulating signals, which often occurs in fluctuating natural environments. Beyond hfiA, RtrC can also directly control the transcription of dozens of other genes in C. crescentus via its pseudo-palindromic binding site, including genes that impact flagellar motility, cyclic-di-GMP signaling, and aerobic respiration.
An ensemble of two-component signal transduction (TCS) proteins in C. crescentus, including LovK and SpdR, can control holdfast synthesis by indirectly regulating transcription of hfiA. Two XRE-family transcription factors, RtrA and RtrB, function downstream of this TCS system to directly repress hfiA and thereby activate holdfast synthesis [16] (Fig 1A). However, deleting rtrA, rtrB, or both (as shown in [16]) has only modest effects on holdfast synthesis when the TCS system is constitutively activated (Fig 2B). We therefore reasoned that there are additional downstream regulators in this pathway that can activate C. crescentus holdfast synthesis. To identify genes downstream of lovK that regulate holdfast synthesis in both an spdR-dependent and spdR-independent manner, we constructed a randomly barcoded transposon mutant library in a lovK mutant background (lovKH180A) in which holdfast synthesis is constitutively activated. This barcoded library was cultivated and serially passaged in the presence of cheesecloth, a process that titrates adhesive cells from liquid medium as recently described [22]. Non-adhesive mutants become enriched in the media supernatant surrounding the cheesecloth, which is reflected as a positive fitness score when the total barcoded population is quantified (Fig 1B). Using this approach, we aimed to identify transposon insertions that ablated the hyper-holdfast phenotype of a mutant in which the adhesion pathway is constitutively active. We expected that performing this genetic selection in a hyper-holdfast lovKH180A background would not only uncover previously identified loss-of-adhesion mutants [22] but would also identify new regulators that function to activate holdfast synthesis downstream of LovK. As expected, strains harboring transposon insertions in all the known adhesion TCS genes (e.g. lovK, spdS, spdR and skaH) had increased abundance in the supernatant (i.e. decreased adhesion to cheesecloth, and positive fitness scores) when grown in the presence of cheesecloth. Insertions in select polar development regulators, and in holdfast synthesis and anchoring genes also resulted in the expected positive fitness scores (Fig 1C and S1 Table). Strains with insertions in gene locus CCNA_00551, which encodes a predicted standalone 146-residue hypothetical protein, had strongly positive fitness scores after cheesecloth selection. In fact, strains with insertions in CCNA_00551 were enriched in the supernatant to a greater extent than TCS adhesion mutants or rtrA and rtrB mutants (Fig 1C and S1 Table). Consistent with these Tn-seq data, in-frame deletion of either spdR or CCNA_00551 from the chromosome abrogated the hyper-holdfast phenotype of lovKH180A (Fig 2A). Expression of CCNA_00551 is directly activated by the DNA-binding response regulator, SpdR [16,23], which implicated CCNA_00551 in the adhesion TCS pathway. Following the convention of previously named adhesion factors that function downstream of SpdR [16], we henceforth refer to CCNA_00551 as rtrC. SpdR functions downstream of LovK [16] (Fig 1A) and expression of a phosphomimetic allele of SpdR (SpdRD64E) provides an alternative genetic approach to constitutively activate the C. crescentus adhesion TCS system. We predicted that deletion of rtrC would also abrogate the hyperadhesive phenotype of a spdRD64E strain. Consistent with this prediction and with the Tn-seq data, we observed that the fraction of cells with visibly stained holdfasts was reduced in a spdRD64E ΔrtrC strain compared to the spdRD64E parent (Fig 2B). There was no significant difference in the percentage of cells with visibly stained holdfasts between spdRD64E ΔrtrA ΔrtrB ΔrtrC and spdRD64E ΔrtrC (Fig 2B). This provides evidence that RtrC is the primary downstream determinant of hyperadhesion when the TCS adhesion pathway is constitutively active. Indeed, overexpression of rtrC alone enhanced the fraction of cells with stained holdfasts more than overexpression of either rtrA or rtrB (Fig 2C).
A search of protein domain family databases in InterPro [24] and the Conserved Domain Database [25] failed to identify conserved domains in RtrC. However, a primary and secondary structure profile matching approach [26] indicated that RtrC resembled classic transcription factors. To explore this possibility, we implemented AlphaFold [27] to predict the tertiary structure of RtrC. This approach predicted a fold that contained five α-helices (α1 –α5) and two β-strands (β1 –β2) that form an antiparallel β hairpin (Fig 3A). We compared this structure to the Protein Data Bank (PDB) using Dali [28], which revealed that the predicted structure of RtrC was most similar to MepR (PDB: 3ECO), a MarR-family transcriptional regulator from Staphylococcus aureus containing a winged helix-turn-helix motif [29]. Based on the structural alignments and 3D superposition with MepR, α1 and α5 of RtrC likely form a dimerization domain, while α2, α3, α4, β1, and β2 form a winged helix-turn-helix (Fig 3). Considering these structural predictions, we hypothesized that rtrC encoded a transcription factor that functions downstream of the C. crescentus TCS adhesion regulatory system.
The transcription factors RtrA and RtrB are known to activate holdfast synthesis and adhesion by repressing transcription of the holdfast inhibitor, hfiA [16]. Given the correlated phenotypes of rtrA, rtrB, and rtrC mutants and the prediction that RtrC is a transcription factor (Figs 2 and 3), we hypothesized that RtrC functioned as a transcriptional repressor of hfiA. To test this model, we measured changes in expression from a fluorescent hfiA transcriptional reporter upon overexpression of rtrC. As expected, overexpression of rtrA and rtrB reduced signal from the PhfiA fluorescent reporter by 80% and 30%, respectively. Overexpression of rtrC resulted in a 95% reduction in hfiA expression (Fig 2D).
We next sought to directly test the predicted DNA-binding function of RtrC. We performed chromatin immunoprecipitation sequencing (ChIP-seq) using a 3xFLAG-tagged rtrC allele and identified 113 statistically significant peaks across the genome (S2 Table). As expected, we observed a significant peak within the hfiA promoter region (Fig 4A). Peaks were highly enriched near globally defined transcription start sites (TSS) [30–32] when compared to a set of randomly generated peaks (Fig 4B); this TSS-proximal enrichment pattern is characteristic of proteins that directly bind DNA to regulate gene expression. To identify putative binding motifs in the ChIP-seq peaks, we analyzed the peak sequences using the XSTREME algorithm within the MEME Suite [33]. This revealed a pseudo-palindromic motif in 112 of the 113 rtrC peaks (E-value: 2.3e-12) that likely corresponded to an RtrC binding site (Fig 4C). To test if RtrC bound to this predicted binding site, we performed electrophoretic mobility shift assays (EMSA) with purified RtrC. Increasing concentrations of RtrC shifted a labeled DNA probe, containing a 27 bp sequence from the hfiA promoter centered on the predicted RtrC binding motif (Figs 4D and S1). RtrC bound to this pseudo-palindrome in the hfiA promoter with high affinity (kd of 45 ± 9 nM) (S2 Fig). Addition of excess unlabeled specific DNA probe competed with labeled probe bound to RtrC, while unlabeled non-specific probe did not compete for RtrC binding (Fig 4D). These data provide evidence that RtrC directly represses hfiA transcription by specifically binding to a pseudo-palindromic motif in the hfiA promoter.
To further characterize the function of RtrC as a transcriptional regulator, we used RNA sequencing (RNA-seq) to measure changes in transcript levels upon rtrC overexpression (rtrC++). RNA-seq was performed with an rtrC overexpression strain rather than a rtrC deletion strain because rtrC expression is low under standard logarithmic growth conditions [30]. By combining RNA-seq and ChIP-seq datasets, we identified genes that are directly controlled by RtrC. Direct targets were defined as genes that a) were differentially regulated in rtrC++ relative to an empty vector control, b) contained an RtrC-enriched peak by ChIP-seq, and c) contained an RtrC binding motif in their promoter region [32]. Of the directly regulated genes, 63% were activated and 37% were repressed by RtrC (Fig 5A and S3 Table). Consistent with transcriptional reporter analysis (Fig 2D), hfiA transcript levels were ~5-fold lower in rtrC++ compared to the vector control (S3 Table). To confirm the RNA-seq results, we constructed several fluorescent transcriptional reporters for genes identified as direct targets of RtrC. Consistent with the RNA-seq data, rtrC overexpression significantly increased reporter signal for CCNA_00629 (2.6-fold) and CCNA_00538 (2.0-fold) and decreased reporter signal for CCNA_00388 (6.7-fold) compared to an empty vector control (Fig 5B). RtrC bound to the rtrC promoter in vivo as demonstrated by ChIP-seq, and signal from a rtrC transcriptional reporter was 19-fold lower when rtrC was overexpressed (Fig 5B). From this, we conclude that RtrC is a negative autoregulator. We also measured signal from transcriptional reporters for several genes that contained RtrC motifs in their promoters but did not meet the statistical threshold for differential regulation by rtrC overexpression in the RNA-seq dataset. We evaluated these additional reporters in complex medium to better match conditions in which we identified RtrC-binding peaks. However, for most reporters (11/16) we still observed no significant transcriptional response to rtrC overexpression (S3 Fig). rtrC overexpression significantly enhanced transcription from the remaining 5 reporters: CCNA_03585 (2.0-fold), CCNA_02901 (1.6-fold), dgrB (5.8-fold), CCNA_01140 (1.6-fold), and CCNA_02976 (1.4-fold) (Fig 5B). Together, these ChIP-seq, RNA-seq and reporter data provide evidence that RtrC can function as both a direct transcriptional activator and repressor.
We hypothesized that the activity of RtrC as an activator or repressor depends on its binding position within a promoter relative to the transcription start site (TSS). To assess whether position correlated with regulatory activity, we analyzed the location of RtrC binding motifs within the promoters of genes that were up- or downregulated based on RNA-seq and transcriptional reporter data. Promoters directly repressed by RtrC typically had predicted motifs that overlapped the -10/-35 region of the promoter. In contrast, genes activated by RtrC had binding motifs that were located upstream of the -10/-35 region (Fig 5C). These data provide evidence that the regulatory activity of RtrC is related to the position of the RtrC binding site in a promoter. The results of this analysis are consistent with a well-described trend in which DNA-binding regulators that function as repressors bind at or near the transcription start site, while activators typically bind upstream of the -10/-35 region to promote transcription [34].
Transcript levels of rtrB were 12-fold higher in the rtrC++ background relative to a vector control, placing it among the most highly activated direct targets of RtrC (S3 Table). As noted above, SpdR activates transcription of both rtrB and rtrC [16,23]. This suggested that these three proteins form a coherent type I feedforward loop (FFL) because the sign of direct regulation (i.e. activation of rtrB by SpdR) is the same as the sign of the indirect regulation (i.e. activation of rtrB by SpdR through RtrC) (Fig 6A). The regulatory properties of this predicted coherent type I FFL depend on whether C. crescentus uses AND-gated logic, in which both SpdR and RtrC are required to activate rtrB expression, or OR-gated logic, in which either SpdR or RtrC can activate rtrB expression [35]. To test FFL gating, we deleted spdR and rtrC from the chromosome and measured fluorescence from a rtrB transcriptional reporter upon expression of spdRD64E and/or rtrC from inducible promoters. Expression of either rtrC or spdRD64E alone increased transcription from the rtrB reporter by ~5-fold, while expression of both rtrC and spdRD64E increased transcription by ~6-fold (Fig 6B). spdR deletion significantly reduced transcription from a PrtrB reporter in stationary phase (S4 Fig). As expected, deletion of rtrC alone did not affect transcription from PrtrB as spdR is still present on the chromosome (S4 Fig). We conclude that either SpdR or RtrC can activate rtrB expression and are therefore competent to form an OR-gated coherent type I FFL in C. crescentus.
SpdR affects gene regulation during stationary phase [23,36], and was previously reported to bind rtrC promoter DNA [23]. Consistent with these observations, transcription from a rtrC reporter increased 13-fold during stationary phase in complex medium in a spdR-dependent manner (S5 Fig). The regulation of rtrC transcription is strongly medium dependent as stationary phase activation of rtrC was not observed in M2-xylose defined medium (S5 Fig). These results led us to assess the effect of rtrC gene deletion on holdfast synthesis in log and stationary phase cultures in complex medium. The fraction of cells with holdfasts in complex medium during early log phase was not significantly different in strains with in-frame deletions of spdR, rtrA, rtrB, rtrC, or in a strain missing all three rtr regulators (S6A Fig). Stained holdfast were greatly reduced in stationary phase, but this effect did not require spdR. Holdfast counts are low in stationary phase, and an rtrB deletion mutant had even fewer holdfasts than wild type in stationary phase; deletion of either rtrA or rtrC had no effect on holdfast counts under these conditions (S6B Fig). While ΔrtrC holdfast counts were not significantly different from wild type in standard complex medium cultures, analysis of transposon sequencing data revealed that strains harboring insertions in rtrC are highly enriched in the supernatant after repeated passaging in media containing cheesecloth (Fig 7A, data from [22]). Thus, there is evidence that loss of rtrC function results in diminished adherence to a solid cellulosic substrate after repeated passage. Importantly, disruption of other genes in the TCS adhesion regulation pathway resulted in a similar temporal adhesion profile as rtrC::Tn mutants in this serial passage experiment (Figs 7A and 7D and S7A). In addition to its critical role in adhesion to solid surfaces, the holdfast is also required for C. crescentus to form pellicle biofilms at air-liquid interfaces [37]. We therefore hypothesized that strains with disruptions in genes that can promote holdfast formation, including rtrC, would have reduced abundance in the pellicle micro-environment. To test this model, we grew the same barcoded transposon mutant library used in the cheesecloth adhesion experiments [22,38] in either defined or complex medium under static growth conditions, which promotes pellicle formation. C. crescentus strains were sampled from the air-liquid interface at successive stages of pellicle development. Strain barcodes were then amplified, sequenced, counted, and fitness scores were calculated for each gene. Positive fitness scores indicate mutant strain enrichment in the pellicle fraction and negative scores reflect underrepresentation of mutants in the pellicle. As expected, strains harboring disruptions of genes required for holdfast synthesis (e.g. hfsA) were highly underrepresented in pellicles in both defined and complex media (Figs 7B, 7C, 7E and 7F and S7B and S7C and S4 Table). Transposon insertions in rtrC resulted in only a minor reduction in strain abundance in the pellicle in complex medium, similar to strains with insertions in the TCS adhesion regulators skaH and spdR. Strains harboring insertions in lovK were slightly enriched in the pellicle (Fig 7B and 7E). We conclude that in complex medium, the adhesion signaling pathway only weakly contributes to holdfast-dependent pellicle formation. However, in M2-xylose defined medium rtrC::Tn mutants have reduced abundance in pellicles, again similar to skaH and spdR mutants (Fig 7C and 7F), and the effect of lovK disruption is more pronounced and is consistent with lovK playing a repressive role in adhesion TCS signaling under this condition. This result echoes the repressive effect that lovK has in regulation of the general stress response (GSR) [39], and may be related to our observation that disruption of the core GSR regulators, phyR and ecfG, attenuates the hyperadhesive phenotype of lovKH180A (Fig 1C). Taken together, these results are consistent with a model in which the adhesion TCS regulatory system is active in static growth in defined xylose medium. RtrC, a downstream component of the TCS adhesion pathway that directly represses hfiA, plays a role pellicle development in defined medium.
We designed a forward genetic selection to search for novel holdfast regulators and identified RtrC. This formerly hypothetical protein functions downstream of an ensemble of TCS regulatory genes to activate surface adhesion. RtrC binds and regulates multiple sites on the C. crescentus chromosome, including the hfiA promoter where it represses hfiA transcription and thereby activates holdfast synthesis.
A comparison of the predicted three-dimensional structure of RtrC to experimental structures available in the PDB suggested structural similarity to MepR and several other MarR family transcriptional regulators (Fig 3C). Members of this transcription factor family often bind as dimers to pseudo-palindromic DNA sequences [40–42]. MarR family transcriptional regulators are known to function as both activators and repressors, depending on the position of binding within regulated promoters. Similarly, we observed that the activity of RtrC as an activator or repressor was correlated with the position of the RtrC motif within the promoter; this positional effect on transcriptional regulation is a well-described phenomenon [43]. Our data thus provide evidence that RtrC (like MarR) functions as a classic transcription factor. The sequence of RtrC is not broadly distributed; it is largely restricted to the Caulobacterales and Rhodospirillales where it is annotated as a hypothetical protein. The genomic neighborhood surrounding rtrC is highly conserved across diverse Caulobacter species (S8 Fig) suggesting rtrC is ancestral in the genus.
Holdfast-dependent surface attachment in C. crescentus is permanent and therefore highly regulated. The small protein, HfiA, is central to holdfast control. It represses holdfast biogenesis by directly interacting with the glycosyltransferase HfsJ, an enzyme required for synthesis of holdfast polysaccharide [15]. hfiA expression is influenced by multiple cell cycle regulators, TCS sensory/signaling systems, a transcriptional regulator of stalk biogenesis, and c-di-GMP [15–17,44]. We have shown that RtrC functions immediately downstream of the stationary phase response regulator, SpdR, to directly bind the hfiA promoter and repress its transcription. SpdR can therefore regulate expression of at least three distinct direct repressors of hfiA transcription–rtrA, rtrB, and rtrC (Fig 8). Why, then, does the spdR response regulator have so many outlets to directly modulate hfiA transcription? We do not know whether the activities of RtrA, RtrB, or RtrC as transcription factors are allosterically regulated by small molecules, chemical modifications, or protein-protein interactions. If these transcription factors are subject to allosteric regulation, it may be the case that this suite of proteins serves to integrate multiple environmental or cellular signals. In such a model, primary signals that regulate the transcriptional activity of SpdR may enhance expression of RtrA, RtrB, or RtrC, which could then influence the scale of adhesion to substrates in response to secondary physical or chemical cues. Another possible explanation for multiple adhesion transcription factors downstream of SpdR is redundancy. Transcription factor redundancy may buffer the network against transient changes in signaling and gene regulation, ensuring that the decision to synthesize a holdfast (or not) is less subject to environmental fluctuations.
RtrC-dependent regulation of transcription was observed for dozens of genes, suggesting that RtrC influences physiological processes beyond holdfast development. For instance, the spdR-rtrC axis activates transcription of the cox genes (S3 Table). These genes encode an aa3-type cytochrome oxidase, which is one of four distinct aerobic terminal oxidase complexes in C. crescentus [45]. An aa3-type oxidase in Pseudomonas aeruginosa is reported to provide a survival advantage for cells under starvation conditions [46]. The physiological impact of cox regulation by RtrC remains untested. RtrC directly regulates expression of several genes involved in c-di-GMP (CdG) signaling including a CdG receptor (dgrB), a PAS-containing EAL phosphodiesterase (CCNA_01140), and a GGDEF-EAL protein (CCNA_00089). Deletion of CCNA_00089 enhances C. crescentus surface attachment [47] and dgrB is reported to directly bind CdG and repress motility [48]. Considering that rtrC overexpression represses CCNA_00089 expression (S3 Table) and activates dgrB (S3 Table and Fig 5B), it is possible that rtrC influences adhesion and/or motility through CCNA_00089, dgrB and hfiA. RtrC also activates expression of genes with predicted roles in chemotaxis, including two methyl-accepting chemotaxis proteins (CCNA_00538 and CCNA_02901) and a cheY receiver domain protein (CCNA_03585). Additionally, RtrC activates transcription of an alternative chemotaxis cluster (CCNA_00628 and CCNA_00629-CCNA_00634), which has been reported to influence hfiA transcription and C. crescentus surface adherence [49]. Given these results, it seems likely that rtrC will influence C. crescentus motility and/or chemotaxis under certain conditions. We identified RtrC motifs in the promoters of 61 operons that were not differentially regulated in our RNA-seq data. It may be the case that RtrC binding has no effect on the regulation of gene expression at certain sites, or that regulation from particular sites requires additional factors that were not present or expressed under the conditions we assayed. Our data provide evidence that RtrC-dependent gene expression can change as a function of growth medium: CCNA_00629, CCNA_00538, and CCNA_00388 were regulated by RtrC in both defined medium and complex medium as shown by our RNA-seq data and confirmed by transcriptional reporter analysis (S3 Table and Fig 5B). In contrast, CCNA_03585, CCNA_02901, dgrB, CCNA_01140, and CCNA_02976 were only regulated by RtrC in complex medium (Fig 5B), while CCNA_00089 was only regulated in defined medium (S3 Table and S3 Fig). We do not understand the mechanism(s) underlying media-dependent regulation of components of the RtrC regulon. Published transcriptomic data provide evidence that carbon limitation [50], cell cycle [51], and stringent response signaling [52] all significantly affect rtrC transcription, indicating that there are a range of environmental conditions (and developmental states) in which rtrC could impact gene expression.
The DNA-binding response regulator, SpdR, is regulated in a growth phase and media-dependent fashion [23,36], and systems homologous to C. crescentus SpdS-SpdR are reported to respond to cellular redox state and to flux through the electron transport chain via modulation of disulfide bond formation [53], modification of a reactive cysteine [54], or by binding of oxidized quinones [55,56]. C. crescentus SpdS contains both the reactive cysteine and quinone-interacting residues observed in related bacteria, suggesting that SpdS may be regulated in a similar manner. The activity of SpdR as a transcriptional regulator is also affected by the sensor kinases LovK and SkaH [16]. Thus, multiple environmental signals apparently feed into SpdR-dependent gene regulation. Our data provide evidence that spdR and rtrC form a type I coherent feedforward loop (C1-FFL) with the XRE-family transcription factor rtrB. Experimental and theoretical studies of C1-FFLs indicate that these regulatory motifs function as sign-sensitive delay elements [35,57]. AND-gated C1-FFLs exhibit a delay in the ON step of output expression, which can allow circuits to function as persistence detectors [35,58]. Conversely, OR-gated C1-FFLs delay the OFF step of output expression, which can buffer the circuit against the transient loss of activating signals [35,57]. Expression of either spdR or rtrC was sufficient to activate transcription from a PrtrB reporter, indicating that the spdR-rtrC-rtrB C1-FFL is competent to function as an OR-gated system (Fig 6). Though the exact environmental signals that regulate the adhesion TCS pathway remain undefined, the architecture of the SpdR-RtrC-RtrB circuit suggests that RtrC can reinforce rtrB expression in particular environments where the levels of activating signals for SpdR are fluctuating or noisy.
It is clear that RtrC expression is impacted by multiple environmental cues. Tn-seq studies show that rtrC mutants are enriched in the supernatant of complex medium after serial passaging in the presence of cheesecloth (Figs 7A and S7A) [22]. C. crescentus was repeatedly cycled between different growth states over the course of this five day experiment (i.e. stationary to logarithmic phase) as cells were diluted into fresh media each day. Considering that SpdR strongly activates RtrC expression during stationary phase (S5 Fig), it seems likely that the adhesion profile of rtrC mutants is influenced by growth phase-dependent changes in rtrC expression. Importantly, disruptions in the adhesion TCS system (specifically spdS, spdR, and skaH) resulted in similar temporal enrichment profiles in the supernatant. This provides evidence that the multi-protein signaling system functioning upstream of rtrC similarly influences adhesion under this serial passage condition. The pellicle provides an interesting and ecologically relevant state to further assess the function of rtrC and its upstream regulators. In C. crescentus, pellicle formation at the air-liquid interface requires holdfast production [37]. Hyperadhesive mutants have accelerated pellicle development and holdfast null mutants are unable to stably partition to this microenvironment [37]. As expected, we observed that disruption of holdfast synthesis genes led to highly reduced fitness within the pellicle fraction (Figs 7B and 7C and S7B and S7C and S4 Table). In addition, our data provide evidence that disruption of rtrC reduces the ability of C. crescentus to inhabit this micro-environment. We hypothesize that rtrC mutants have reduced fitness in the pellicle fraction because of differences in holdfast development; this is based on our observations that RtrC regulates holdfast formation through hfiA. However, it is possible that reduced fitness of rtrC mutants in the pellicle fraction is due to other changes in metabolic state or growth rate affected by RtrC. Interestingly, the impact of rtrC disruption on strain fitness in the pellicle was more significant in defined medium than complex medium (Fig 7B, 7C, 7E and 7F and S4 Table). We observed similar pellicle fitness trends for lovK, spdR and skaH mutants in defined versus complex media, providing evidence that the adhesion TCS pathway upstream of rtrC plays a larger regulatory role in the pellicle in defined medium than in complex medium. Though rtrC expression was not activated by spdR in defined medium in a standard continuously shaken culture (S5 Fig), our results suggest that SpdR and the adhesion TCS pathway is active in statically grown pellicles in defined medium. This study expands our understanding of a transcriptional network functioning downstream of a suite of TCS proteins that affect surface adherence in Caulobacter. The DNA-binding response regulator SpdR regulates expression of at least three transcription factor genes (rtrA, rtrB, and rtrC) that directly repress the holdfast inhibitor, hfiA. Of these three transcription factors, RtrC is the most potent repressor of hfiA. However, it is clear from the ChIP-seq and transcriptomic data presented in this study that the regulatory function of RtrC likely extends well beyond hfiA and holdfast synthesis (Fig 8). Efforts focused on deciphering the regulatory cues that impact signaling through the adhesion TCS system, and comparative analyses of the SpdR, RtrA, RtrB, and RtrC regulons will provide a more complete understanding of the regulatory logic that underpins the highly complex process of holdfast adhesin development, surface adherence, and pellicle formation in Caulobacter.
Escherichia coli was grown in Lysogeny broth (LB) or LB agar (1.5% w/v) at 37°C [59]. Medium was supplemented with the following antibiotics when necessary: kanamycin 50 μg ml-1, chloramphenicol 20 μg ml-1, oxytetracycline 12 μg ml-1, and carbenicillin 100 μg ml-1. Caulobacter crescentus was grown in peptone-yeast extract (PYE) broth (0.2% (w/v) peptone, 0.1% (w/v) yeast extract, 1 mM MgSO4, 0.5 mM CaCl2), PYE agar (1.5% w/v), or M2 defined medium supplemented with xylose (0.15% w/v) as the carbon source (M2X) [60] at 30°C. Solid medium was supplemented with the following antibiotics where necessary: kanamycin 25 μg ml-1, chloramphenicol 1 μg ml-1, and oxytetracycline 2 μg ml-1. Liquid medium was supplemented with the following antibiotics where necessary: chloramphenicol 1 μg ml-1, and oxytetracycline 2 μg ml-1.
Construction and mapping of the barcoded Tn-himar library was performed following protocols originally described by Wetmore and colleagues [61]. A 25 ml culture of the E. coli APA_752 barcoded transposon donor pool (obtained from Adam Deutschbauer Lab) was grown to mid-log phase in LB broth supplemented with kanamycin and 300 μM diaminopimelic acid (DAP). A second 25 ml culture of C. crescentus lovKH180A was grown to mid-log phase in PYE. Cells from both cultures were harvested by centrifugation, washed twice with PYE containing 300 μM DAP, mixed and spotted together for conjugation on a PYE agar plate containing 300 μM DAP. After incubating the plate overnight at room temperature, the cells were scraped from the plate, resuspended in PYE medium, spread onto 20, 150 mm PYE agar plates containing kanamycin and incubated at 30°C for three days. Colonies from each plate were scraped into PYE medium and used to inoculate a 25 ml PYE culture containing 5 μg ml-1 kanamycin. The culture was grown for three doublings, glycerol was added to 20% final concentration, and 1 ml aliquots were frozen at -80°C. To map the sites of transposon insertion, we again followed the protocols of Wetmore et al. [61]. Briefly, genomic DNA was purified from three 1 ml aliquots of each library. The DNA was sheared and ~300 bp fragments were selected before end repair. A Y-adapter (Mod2_TS_Univ, Mod2_TruSeq) was ligated and used as a template for transposon junction amplification with the primers Nspacer_BarSeq_pHIMAR and either P7_mod_TS_index1 or P7_mod_TS_index2. 150-bp single end reads were collected on an Illumina HiSeq 2500 in rapid run mode, and the genomic insertion positions were mapped and correlated to a unique barcode using BLAT [62] and MapTnSeq.pl to generate a mapping file with DesignRandomPool.pl. Using this protocol, we identified 232903 unique barcoded insertions at 60940 different locations on the chromosome. The median number of barcoded strains per protein-encoding gene (that tolerated Tn insertion) was 34; the mean was 49.6. Median number of sequencing reads per hit protein-encoding gene was 4064; mean was 6183.5. All code used for this mapping and analysis is available at https://bitbucket.org/berkeleylab/feba/.
Adhesion profiling followed the protocol originally outlined in Hershey et al. [22]. 1 ml aliquots of the barcoded transposon library were cultured, collected by centrifugation, and resuspended in 1 ml of M2X medium. 300 μl of this barcoded mutant pool was inoculated into a well of a 12-well microtiter plate containing 1.5 ml M2X defined medium with 6–8 ~1 x 1 cm layers of cheesecloth. These microtiter plates were incubated for 24 hours at 30°C with shaking at 155 rpm after which 150 μl of the culture was passaged by inoculating into a well with 1.65 ml fresh M2X containing cheesecloth. Cells from an additional 500 μl of medium from each well was harvested by centrifugation and stored at -20°C for barcode sequencing (BarSeq) analysis. Each passaging experiment was performed in triplicate, and passaging was performed sequentially for a total of five rounds of selection. Identical cultures grown in a plate without cheesecloth were used as a nonselective reference condition. Cell pellets were used as PCR templates to amplify the barcodes in each sample using indexed primers [61]. Amplified products were purified and pooled for multiplexed sequencing. 50 bp single end reads were collected on an Illumina HiSeq4000. The Perl and R scripts MultiCodes.pl, combineBarSeq.pl and FEBA.R were used to determine fitness scores for each gene by comparing the log2 ratios of barcode counts in each sample over the counts from a nonselective growth in M2X without cheesecloth. To evaluate mutant phenotypes in each selection, the replicates were used to calculate a mean fitness score for each gene after each passage. Mean fitness (a proxy for adhesion to cheesecloth) was assessed across passages for each gene.
Plasmids were cloned using standard molecular biology techniques and the primers listed in S5 Table. To construct pPTM051, CCNA_03380 (-21 to +15 bp relative to the start of the gene) was fused to mNeonGreen and cloned into pMT805 lacking the xylose-inducible promoter [63]. To construct pPTM056, site directed mutagenesis was used to introduce a silent mutation in the chloramphenicol acetyltransferase gene of pPTM051 to remove an EcoRI site. A cumate-inducible, integrating plasmid was constructed by fusing a backbone with a chloramphenicol resistance marker derived from pMT681 [63], the xylose integration site derived from pMT595 [63], and the cumate-responsive repressor and promoter derived from pQF through Gibson Assembly [64]. To construct pPTM057, the xylose integration site, cumate repressor, and cumate-inducible promoter of pPTM052 were fused to a backbone with a kanamycin resistance marker derived from pMT426 [63]. For reporter plasmids, inserts were cloned into the replicating plasmid pPTM056. For overexpression constructs, inserts were cloned into pPTM057 or pMT604 that integrate at the xylose locus and contain either a cumate- (PQ5) or xylose-inducible (Pxyl) promoter, respectively [63]. For 3xFLAG-tagged RtrC overexpression, inserts were cloned into the replicating plasmid pQF [64]. Deletion inserts were constructed by overlap PCR with regions up- and downstream of the target gene and cloned into the pNPTS138 plasmid. Clones were confirmed with Sanger sequencing. Plasmids were transformed into C. crescentus by either electroporation or triparental mating [60]. Transformants generated by electroporation were selected on PYE agar supplemented with the appropriate antibiotic. Strains constructed by triparental mating were selected on PYE agar supplemented with the appropriate antibiotic and nalidixic acid to counterselect against E. coli. Gene deletions and allele replacements were constructed using a standard two-step recombination/counter-selection method, using sacB as the counterselection marker. Briefly, pNPTS138-derived plasmids were transformed into C. crescentus and primary integrants were selected on PYE/kanamycin plates. Primary integrants were incubated overnight in PYE broth without selection. Cultures were plated on PYE agar plates supplemented with 3% (w/v) sucrose to select for recombinants that had lost the plasmid. Mutants were confirmed by PCR amplification of the gene of interest from sucrose resistant, kanamycin sensitive clones.
Strains were inoculated in triplicate in M2X or PYE, containing 50 μM cumate when appropriate, and grown overnight at 30°C. Strains were subcultured in M2X or PYE, containing 50 μM cumate when appropriate, and grown for 3–8 hours at 30°C. Cultures were diluted to 0.0000057–0.00045 OD660 and incubated at 30°C until reaching 0.05–0.1 OD660,. For stationary phase cells, cultures were diluted to 0.05 OD660 and incubated at 30°C for 24 hours. Alexa594-conjugated wheat germ agglutinin (WGA) (ThermoFisher) was added to the cultures with a final concentration of 2.5 μg ml-1. Cultures were shaken at 30°C for 10 min at 200 rpm. Then, 1.5 ml early log phase culture or 0.75 ml stationary phase culture was centrifuged at 12,000 x g for 2 min and supernatant was removed. Pellets from early log phase in M2X and PYE were resuspended in 35 μl M2X or 100 μl H2O, respectively. Pellets from stationary phase in PYE were resuspended in 400 μl H2O. Cells were spotted on 1% (w/v) agarose pads in H2O and imaged with a Leica DMI6000 B microscope. WGA staining was visualized with Leica TXR ET (No. 11504207, EX: 540–580, DC: 595, EM: 607–683) filter. Cells with and without holdfasts were enumerated using the image analysis suite, FIJI. Statistical analysis was carried out in GraphPad 9.3.1.
The structure of CCNA_00551 was predicted with AlphaFold [27] through Google Colab using the ChimeraX interface [65]. The predicted structure from AlphaFold was submitted to the Dali server [28] for structural comparison to the Protein Data Bank (PDB).
Strains were incubated in triplicate at 30°C overnight in 10 ml PYE supplemented with 2 μg/ml oxytetracycline. Then, 5 ml overnight culture was diluted into 46 ml PYE supplemented with 2 μg/ml oxytetracycline, grown at 30°C for 2 hours. Cumate was added to a final concentration of 50 μM and cultures were grown at 30°C for 6 hours. Cultures were crosslinked with 1% (w/v) formaldehyde for 10 min, then crosslinking was quenched by addition of 125 mM glycine for 5 min. Cells were centrifuged at 7196 x g for 5 min at 4°C, supernatant was removed, and pellets were washed in 25 ml 1x cold PBS pH 7.5 three times. Pellets were resuspended in 1 ml [10 mM Tris pH 8 at 4°C, 1 mM EDTA, protease inhibitor tablet, 1 mg ml-1 lysozyme] and incubated at 37°C for 30 min. Sodium dodecyl sulfate (SDS) was added to a final concentration of 0.1% (w/v) and DNA was sheared to 300–500 bp fragments by sonication for 10 cycles (20 sec on/off). Debris was centrifuged at 15,000 x g for 10 min at 4°C, supernatant was transferred, and Triton X-100 was added to a final concentration of 1% (v/v). Samples were pre-cleared through incubation with 30 μl SureBeads Protein A magnetic beads for 30 min at room temp. Supernatant was transferred and 5% lysate was removed for use as input DNA. For pulldown, 100 μl Pierce anti-FLAG magnetic agarose beads (25% slurry) were equilibrated overnight at 4°C in binding buffer [10 mM Tris pH 8 at 4°C, 1 mM EDTA, 0.1% (w/v) SDS, 1% (v/v) Triton X-100] supplemented with 1% (w/v) bovine serum albumin (BSA). Pre-equilibrated beads were washed four times in binding buffer, then incubated with the remaining lysate for 3 hours at room temperature. Beads were washed with low-salt buffer [50 mM HEPES pH 7.5, 1% (v/v) Triton X-100, 150 mM NaCl], high-salt buffer [50 mM HEPES pH 7.5, 1% (v/v) Triton X-100, 500 mM NaCl], and LiCl buffer [10 mM Tris pH 8 at 4°C, 1 mM EDTA, 1% (w/v) Triton X-100, 0.5% (v/v) IGEPAL CA-630, 150 mM LiCl]. To elute protein-DNA complexes, beads were incubated for 30 min at room temperature with 100 μl elution buffer [10 mM Tris pH 8 at 4°C, 1 mM EDTA, 1% (w/v) SDS, 100 ng μl-1 3xFLAG peptide] twice. Elutions were supplemented with NaCl and RNase A to a final concentration of 300 mM and 100 μg ml-1, respectively, and incubated at 37°C for 30 min. Then, samples were supplemented with Proteinase K to a final concentration of 200 μg ml-1 and incubate overnight at 65°C to reverse crosslinks. Input and elutions were purified with the Zymo ChIP DNA Clean & Concentrator kit and libraries were prepared and sequenced at the Microbial Genome Sequencing Center (Pittsburgh, PA). Raw chromatin immunoprecipitation sequencing data are available in the NCBI GEO database under series accession GSE201499.
Paired-end reads were mapped to the C. crescentus NA1000 reference genome (GenBank accession number CP001340) with Bowtie2 on Galaxy. Peak calling was performed with the Genrich tool (https://github.com/jsh58/Genrich) on Galaxy; peaks are presented in S2 Table. Briefly, PCR duplicates were removed from mapped reads, replicates were pooled, input reads were used as the control dataset, and peak were called using the default peak calling option [Maximum q-value: 0.05, Minimum area under the curve (AUC): 20, Minimum peak length: 0, Maximum distance between significant sites: 100]. An average AUC > 2500 was used as the cutoff for significant peaks. Distance between called peaks and the nearest transcription start sites (TSS) (modified from [32]) was analyzed using ChIPpeakAnno [66]. For genes/operons that did not have an annotated TSS, the +1 residue of the gene (or start of the operon) was designated as the TSS.
For motif discovery, sequences of enriched ChIP-seq peaks were submitted to the XSTREME module of MEME suite [33]. For the XSTREME parameters, shuffled input files were used as the control sequences for the background model, checked for motifs between 6 and 30 bp in length that had zero or one occurrence per sequence.
Strains were incubated in quadruplicate at 30°C overnight in M2X broth supplemented with 50 μM cumate. Overnight replicate cultures were diluted into fresh M2X/50 μM cumate to 0.025 OD660 and incubated at 30°C for 8 hours. Cultures were diluted into 10 ml M2X/50 μM cumate to 0.001–0.003 OD660 and incubated at 30°C until reaching 0.3–0.4 OD660. Upon reaching the desired OD660, 6 ml culture was pelleted at 15,000 x g for 1 minute, supernatant was removed, and pellets were resuspended in 1 ml TRIzol and stored at -80°C. Samples were heated at 65°C for 10 min. Then, 200 μl chloroform was added, samples were vortexed, and incubated at room temperature for 5 min. Samples were centrifuged at 17,000 x g for 15 min at 4°C, then the upper aqueous phase was transferred to a fresh tube, an equal volume of 100% isopropanol was added, and samples were stored at -80°C overnight. Samples were centrifuged at 17,000 x g for 30 min at 4°C, then supernatant was removed. Samples were washed with cold 70% ethanol. Samples were centrifuged at 17,000 x g for 5 min at 4°C, supernatant was removed, and the pellet was allowed to dry. Pellets were resuspended in 100 μl RNase-free water and incubated at 60°C for 10 min. Samples were treated with TURBO DNase and cleaned up with RNeasy Mini Kit (Qiagen). Library preparation and sequencing was performed at the Microbial Genome Sequencing center with the Illumina Stranded RNA library preparation and RiboZero Plus rRNA depletion (Pittsburgh, PA). Reads were mapped to the C. crescentus NA1000 reference genome (GenBank accession number CP001340) using CLC Genomics Workbench 20 (Qiagen). Differential gene expression was determined with the CLC Genomics Workbench RNA-seq Analysis Tool (|fold-change| ≥ 1.5 and FDR p-value ≤ 0.001). Raw RNA sequencing data are available in the NCBI GEO database under series accession GSE201499.
The Bioconductor package was used to identify overlap between RtrC-regulated genes defined by RNA-seq and RtrC binding sites defined by ChIP-seq [66]. Promoters for genes were designated as the sequence -400 to +100 around the TSS [32]. Overlap between promoters and RtrC motifs identified from XSTREME was analyzed using ChIPpeakAnno within Bioconductor. Genes were defined as direct targets of RtrC if their transcript levels were differentially regulated in the RNA-seq analysis and had an RtrC motif within a promoter for their operon. To analyze RtrC motif distribution in directly regulated promoters, promoters were grouped based on the effect of RtrC on gene expression (i.e. upregulated vs. downregulated). The number of predicted RtrC motifs at each position with these promoters was calculated and plotted. If an operon contained more than one promoter, then each promoter for that operon that contained an RtrC motif was analyzed.
Strains were incubated in triplicate at 30°C overnight in PYE or M2X broth supplemented with 1 μg ml-1 chloramphenicol and 50 μM cumate. Overnight cultures were diluted to 0.05 OD660 in the appropriate broth and incubated at 30°C for 24 hours. For S4 and S5 Figs, log phase (0.05–0.3 OD660) cultures were diluted to 0.025 OD660 and incubated at 30°C for 24–48 hours. Then, 200 μl culture was transferred to a black Costar 96 well plate with clear bottom (Corning). Absorbance at 660 nm and fluorescence (excitation = 497 ± 10 nm; emission = 523 ± 10 nm) were measured in a Tecan Spark 20M plate reader. Fluorescence was normalized to absorbance. For Fig 6, strains were incubated in triplicate at 30°C overnight in PYE broth supplemented with 1 μg ml-1 chloramphenicol. Overnight cultures were diluted to 0.025 OD660 in PYE broth supplemented with 1 μg ml-1 chloramphenicol, 50 μM cumate, and 0.15% (w/v) xylose. Cultures were incubated at 30°C for 24 hours, then 100 μl overnight was diluted with 100 μl PYE and transferred to a black Costar 96 well plate with clear bottom (Corning). Fluorescence and absorbance were measured as indicated above in a Tecan Spark 20M plate reader. Fluorescence was normalized to absorbance. Statistical analysis was carried out in GraphPad 9.3.1.
For heterologous expression of RtrC, plasmids were transformed into the BL21 Rosetta (DE3)/pLysS background. Strains were inoculated into 20 ml LB broth supplemented with 100 μg ml-1 carbenicillin and incubated overnight at 37°C. Overnight cultures were diluted into 1000 ml LB supplemented with carbenicillin and grown for 3–4 hours at 37°C. Protein expression was induced by 0.5 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) and incubation at 37°C for 3–4.5 hours. Cells were pelleted at 11,000 x g for 7 min at 4°C, pellets were resuspended in 25 ml lysis buffer [25 mM Tris pH 8 at 4°C, 500 mM NaCl, 10 mM imidazole], and stored at -80°C. Samples were thawed, supplemented with PMSF and benzonase to a final concentration of 1 mM and 50 Units ml-1, respectively. Samples were sonicated with a Branson Digital Sonifier at 20% output in 20” intervals until sufficiently lysed and clarified by centrifugation at 39,000 x g for 15 min at 4°C. Clarified lysates were batch incubated with 4 ml Ni-NTA Superflow Resin (50% slurry) that had been pre-equilibrated in lysis buffer for 60 min at 4°C. Column was then washed with 25 ml lysis buffer, high salt buffer [25 mM Tris pH 8 at 4°C, 1 M NaCl, 30 mM imidazole], and low salt buffer [25 mM Tris pH 8 at 4°C, 500 mM NaCl, 30 mM imidazole]. For elution, column was batch incubated with 25 ml elution buffer [25 mM Tris pH 8 at 4°C, 500 mM NaCl, 300 mM imidazole] for 60 min at 4°C. Elution was supplemented with ULP1 protease to cleave the His6-SUMO tag and dialyzed in 1 L dialysis buffer [25 mM Tris pH 8 at 4°C, 500 mM NaCl] overnight at 4°C. Dialyzed sample was batch incubated with 4 ml Ni-NTA Superflow Resin (50% slurry) that had been pre-equilibrated in dialysis buffer for 60 min at 4°C. Flowthrough that contained untagged RtrC was collected and concentrated on an Amicon Ultra-15 concentrator (3 kDa cutoff) at 4,000 x g at 4°C. Samples were stored at 4°C until needed.
To prepare labeled DNA fragments, an Alexa488-labeled universal forward primer and an hfiA specific reverse primer listed in S5 Table were annealed in a thermocycler in as follows: 94°C for 5 min, then ramp down to 18°C at 0.1°C s-1. Overhangs were filled in with DNA polymerase I, Large Klenow fragment at 25°C for 60 min. DNA fragments were then treated with Mung Bean Nuclease for 120 min at 30°C to remove any remaining overhangs. DNA fragments were purified with the GeneJet PCR purification kit, eluted in 10 mM TE/NaCl [Tris pH 8 at 4°C, 1 mM EDTA, 50 mM NaCl], and diluted to 0.5 μM in TE/NaCl. Unlabeled DNA fragments were prepared by annealing primers listed in S5 Table with protocol listed above. For non-specific chase, the sequence of the hfiA specific probe was shuffled. RtrC was incubated with 6.25 nM labeled DNA in binding buffer at 20°C for 30 min in the dark and subsequently cooled to 4°C on ice. For EMSA to analyze binding curves, DNA binding buffer consisted of 32.5 mM Tris pH 8 at 4°C, 200 mM NaCl, 1 mM EDTA, 30% (v/v) glycerol, 1 mM DTT, 10 μg ml-1 BSA, and 50 ng μl-1 poly(dI-dC). For EMSAs with unlabeled chases, DNA binding buffer consisted of 30 mM Tris pH 8 at 4°C, 150 mM NaCl, 1 mM EDTA, 30% (v/v) glycerol, 1 mM DTT, 10 μg ml-1 BSA, and 50 ng μl-1 poly(dI-dC). For non-specific and specific chases, reactions were supplemented with 2.5 μM unlabeled DNA. Then, 15 μl reaction was loaded on to a degassed polyacrylamide gel [10% (v/v/) acrylamide (37.5:1 acrylamide:bis-acrylamide), 0.5x Tris-Borate-EDTA buffer (TBE: 45 mM Tris, 45 mM borate, 1 mM EDTA)] and run at 100 V for 40 min at 4°C in 0.5x TBE buffer. Gels were imaged on a ChemiDoc MP imaging system [Light: blue epi illumination, Filter: 530/28, Exposure: 30 sec] and bands were quantified with FIJI. For calculating kd, percent bound probe at each protein concentration was calculated as (1 –[intensity free probe at x nM protein]/[intensity of free probe at 0 nM protein]). Binding curve was derived from One site–specific binding analysis using GraphPad 9.3.1.
We grew a barcoded Tn-himar mutant library previously described and characterized [38] in static culture and harvested cells from the air-liquid interface using an approach described in [37]. For the experiment in complex (PYE) medium, a 1 ml aliquot of the library was diluted into 100 ml of PYE in a 250 ml flask and outgrown shaking at 30°C overnight. Five aliquots of 200 μl of the starter culture were saved as a reference sample. Five beakers, each containing 400 ml PYE, were inoculated to a starting density of OD660 = 0.005–0.006. These beakers were incubated at room temperature without shaking and samples from the air-liquid interface were collected using the large end of sterile 1 ml pipet tips [37]. The interfacial liquid and cells collected in the pipet tip were transferred to a 1.7 ml centrifuge tube containing 1.5 ml of sterile water. Contact with the sterile water allowed efficient transfer of the interfacial sample to the tube. At early time points (less than 2 days), two sample plugs were collected from the interface of each replicate beaker. At later time points (2+ days), one sample plug contained sufficient numbers of cells for analysis. After transfer, cells were collected by centrifugation (3 min at 21,000g), the supernatant was removed, and the cell pellet was stored at -20°C. The experiment in defined M2X medium, was conducted similarly, except that the starter culture was grown in M2X medium and samples were collected on a slower and longer time course because pellicles develop more slowly in defined medium [37]. To assess barcode abundances, we followed the approach developed and described by Wetmore and colleagues [61]. Briefly, each cell pellet was resuspended in 10–20 μl water. Barcodes were amplified using Q5 polymerase (New England Biolabs) in 20 μl reaction volumes containing 1X Q5 reaction buffer, 1X GC enhancer, 0.8 U Q5 polymerase, 0.2 mM dNTP, 0.5 μM of each primer and 1 μl of resuspended cells. Each reaction contained a universal forward primer, Barseq_P1, and a unique indexed reverse primer (Barseq_P2_ITxxx, where the xxx identifies the index number) described in [61]. Reactions were cycled as follows: 98°C for 4 min, 25 cycles of 98°C for 30 s, 55°C for 30 s, and 72°C for 30, 72°C for 5 min, 4°C hold. Amplified barcodes were pooled and 50-bp single-end reads were collected on an Illumina HiSeq4000 with Illumina TruSeq primers at the University of Chicago Genomics Facility. Pellicle barcode amplicon sequence data have been deposited in the NCBI Sequence Read Archive under BioProject accession PRJNA877623. Sequence data used to map the Tn insertion sites to the Caulobacter crescentus genome are available under BioProject accession PRJNA429486, SRA accession SRX3549727. Barcode sequences were analyzed using the fitness calculation protocol of Wetmore and colleagues [61]. Briefly, the barcodes in each sample were counted and assembled using MultiCodes.pl and combineBarSeq.pl. From this table of barcodes, FEBA.R was used to determine fitness by comparing the log2 ratios of barcode counts in each sample over the counts in the starter culture reference samples. Fitness scores corresponding to the genes of interest in this study were manually extracted.
Strains were incubated in quadruplicate at 30°C overnight in PYE broth supplemented with 50 μM cumate. Overnight cultures were diluted to 0.05 OD660 in PYE/50 μM cumate, then incubated at 30°C for 24 hours. Cultures were diluted to 0.5 OD660 in PYE broth, 0.75 μl diluted culture was pipetted into PYE plate supplemented with 50 μM cumate, incubated at 30°C for 3 days. Plates were imaged on a ChemiDoc MP imaging system and swarm size was measured with FIJI. Statistical analysis was carried out in GraphPad 9.3.1.
RtrC protein sequence was compared to the NCBI Refseq database with PSI-BLAST, using the default settings and excluding uncultured/environmental samples. Accession ID for proteins with > 95% query coverage and > 65% percent identity were extracted and submitted to the WebFLaGs server for neighborhood analysis (http://www.webflags.se/) [67].
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PMC9648851 | 36315596 | Ty A. Bottorff,Heungwon Park,Adam P. Geballe,Arvind Rasi Subramaniam | Translational buffering by ribosome stalling in upstream open reading frames | 31-10-2022 | Upstream open reading frames (uORFs) are present in over half of all human mRNAs. uORFs can potently regulate the translation of downstream open reading frames through several mechanisms: siphoning away scanning ribosomes, regulating re-initiation, and allowing interactions between scanning and elongating ribosomes. However, the consequences of these different mechanisms for the regulation of protein expression remain incompletely understood. Here, we performed systematic measurements on the uORF-containing 5′ UTR of the cytomegaloviral UL4 mRNA to test alternative models of uORF-mediated regulation in human cells. We find that a terminal diproline-dependent elongating ribosome stall in the UL4 uORF prevents decreases in main ORF protein expression when ribosome loading onto the mRNA is reduced. This uORF-mediated buffering is insensitive to the location of the ribosome stall along the uORF. Computational kinetic modeling based on our measurements suggests that scanning ribosomes dissociate rather than queue when they collide with stalled elongating ribosomes within the UL4 uORF. We identify several human uORFs that repress main ORF protein expression via a similar terminal diproline motif. We propose that ribosome stalls in uORFs provide a general mechanism for buffering against reductions in main ORF translation during stress and developmental transitions. | Translational buffering by ribosome stalling in upstream open reading frames
Upstream open reading frames (uORFs) are present in over half of all human mRNAs. uORFs can potently regulate the translation of downstream open reading frames through several mechanisms: siphoning away scanning ribosomes, regulating re-initiation, and allowing interactions between scanning and elongating ribosomes. However, the consequences of these different mechanisms for the regulation of protein expression remain incompletely understood. Here, we performed systematic measurements on the uORF-containing 5′ UTR of the cytomegaloviral UL4 mRNA to test alternative models of uORF-mediated regulation in human cells. We find that a terminal diproline-dependent elongating ribosome stall in the UL4 uORF prevents decreases in main ORF protein expression when ribosome loading onto the mRNA is reduced. This uORF-mediated buffering is insensitive to the location of the ribosome stall along the uORF. Computational kinetic modeling based on our measurements suggests that scanning ribosomes dissociate rather than queue when they collide with stalled elongating ribosomes within the UL4 uORF. We identify several human uORFs that repress main ORF protein expression via a similar terminal diproline motif. We propose that ribosome stalls in uORFs provide a general mechanism for buffering against reductions in main ORF translation during stress and developmental transitions.
About half of human mRNAs have at least one upstream open reading frame (uORF) in their 5′ untranslated region [1–3]. Ribosome profiling studies estimate that at least twenty percent of these uORFs are actively translated [4,5]. uORFs can regulate gene expression via the biological activity of the uORF peptide, but they also often cis-regulate translation of the downstream main ORF [6,7]. Despite having poor initiation sequence contexts, many eukaryotic uORFs repress main ORF translation [1,3,4,7–11]. uORF mutations are implicated in several human diseases via changes to main ORF translation [12,13]. For example, uORF mutations in oncogenes and tumor suppressors can act as driver mutations in cancer [14,15]. uORFs can regulate translation via a variety of molecular mechanisms. uORFs can constitutively repress translation by siphoning away scanning ribosomes from initiating at downstream main ORFs. Multiple uORFs can interact together to regulate the re-initiation frequency at the main ORF. For example, uORFs in the S. cerevisiae GCN4 mRNA and the homologous human ATF4 mRNA render main ORF translation sensitive to cellular levels of the eIF2α-GTP-tRNAMet ternary complex [16,17]. Although the initiation rate usually limits translation [18–20], inefficient elongation or termination on uORFs can also regulate protein expression by preventing scanning ribosomes from reaching the main ORF [21–26]. Inefficient elongation can be driven by the nascent uORF peptide [27,28], poorly translated codons in the uORF [29,30], or small molecules such as amino acids or polyamines [23,24]. Further, interactions between scanning and elongating ribosomes on uORFs may cause dissociation of scanning ribosomes or enhanced initiation at start codons [23,31,32]. Despite the plethora of proposed uORF regulatory mechanisms, their implications for the regulation of protein expression are not clear. For example, are some uORF regulatory mechanisms more effective than others at repressing protein expression across a wide range of biochemical parameters? How do uORFs alter the response of main ORF translation to changes in cellular and environmental conditions? Answering these questions requires a joint accounting of how the different steps of translation, such as initiation, scanning, and elongation, together influence the overall rates of uORF and main ORF translation. Since it is not straightforward to monitor the rates of individual steps of translation [33], indirect measurements of protein expression are often necessary to infer the underlying mechanism of uORF-mediated regulation. Such inference requires rigorous kinetic models of uORF regulation that make testable experimental predictions for the effects of genetic mutations on protein expression. Computational kinetic modeling has been widely used to study mechanisms of translational control [34]. Quantitative modeling of uORF translation has been used to support the regulated re-initiation model for the GCN4 mRNA [35–37]. A computational model predicted that elongating ribosomes can dislodge leading scanning ribosomes on uORFs and confer stress resistance to protein expression [38]. However, these models have not been compared against alternative models of uORF regulation that predict queuing or dissociation of scanning ribosomes upon collision with paused elongating ribosomes [21,23]. A critical barrier to such comparison has been the lack of a computational framework for the specification and simulation of different kinetic models of uORF-mediated translational regulation. Such a computational framework is necessary for the identification of unique experimental signatures of each proposed model and for their comparison with experimental measurements. Even though simulation code has been made available in many computational studies of mRNA translation [18,38], it is often highly tailored for specific models and cannot be easily modified to consider alternative regulatory mechanisms. Here, we use experimental measurements on the well-studied uORF-containing 5′ UTR of the human cytomegaloviral UL4 mRNA to test different kinetic models of uORF-mediated translational control [21]. The second uORF (henceforth uORF2) in the UL4 5′ UTR contains a terminal diproline motif that stalls 80S ribosomes by disrupting peptidyl transferase center activity [27,28]. For systematic model comparisons, we rely on a recent computational framework that allows easy specification and efficient simulation of arbitrary kinetic models of translational control [39]. Using this experimentally-integrated modeling approach, we find that the presence of 80S stalls in uORF2 of UL4 5′ UTR confers resistance (henceforth called buffering) of main ORF translation to reduced ribosome loading on the mRNA. Modeling suggests that collisions of scanning ribosomes with the stalled 80S ribosome confer this buffering behavior. Experimental variation of the distance between the uORF2 start codon and the elongating ribosome stall supports a kinetic model in which scanning ribosomes dissociate rather than queue upon colliding with the 80S stall. We also identify several human uORFs that have repressive terminal diproline motifs similar to the UL4 uORF2 80S stall. We propose that ribosome stalls in uORFs enable buffering of main ORF protein expression against reduced ribosome loading across cellular and environmental transitions. Together, our results illustrate the value of experimentally-integrated kinetic modeling for the comparison of different uORF regulatory mechanisms and the identification of novel experimental signatures from complex molecular interactions.
We surveyed five previously proposed models of uORF regulation of main ORF translation (Fig 1). We tested these models using a combination of computational modeling and experimental reporter assays. In the constitutive repression model [9] (Fig 1A), uORFs siphon away scanning ribosomes from the main ORF since re-initiation is usually infrequent [40–43]. In the 80S-hit dissociation model [38] (Fig 1B), elongating ribosomes that hit downstream scanning ribosomes cause the 3′ scanning ribosomes to dissociate from the mRNA. In the queuing-mediated enhanced repression model [23] (Fig 1C), a stalled elongating ribosome within the uORF allows upstream scanning ribosomes to queue in the 5′ region. This queuing can bias scanning ribosomes to initiate translation at the uORF start codon rather than leaky scan past it. In the collision-mediated 40S dissociation model [31,32] (Fig 1D), scanning ribosomes instead dissociate if they collide with a 3′ stalled elongating ribosome. Lastly, in the regulated re-initiation model [16,44,45] (Fig 1E), for example in the GCN4 (S. cerevisiae homolog of human ATF4) mRNA, translation of the first uORF is followed by re-initiation at either a second downstream uORF or the main ORF depending on the stress status of the cell. After termination at the first uORF stop codon, scanning ribosomes must reacquire a new eIF2α-GTP-tRNAMet ternary complex (TC) before re-initiating at a downstream start codon. The time to reacquire a new TC correlates with the proportion of phosphorylated eIF2α. Therefore, when cells are not stressed and the proportion of phosphorylated eIF2α is lower, translation of the first uORF is followed by re-initiation at the second downstream uORF start codon. Alternatively, when cells are stressed and the proportion of phosphorylated eIF2α is higher, translation of the first uORF is instead followed by re-initiation at the main ORF start codon.
To differentiate between proposed models of uORF regulation (Fig 1), we used the well-studied human cytomegaloviral UL4 uORF2 [31] as an experimental model (Fig 2A). uORF2 represses main ORF translation via an elongating ribosome stall that is dependent on the uORF2 peptide sequence [21] (Fig 2A, irrelevant uORFs boxed in white, key uORF2 boxed in green). The two C-terminal proline residues, regardless of codon usage, in uORF2 are necessary for the elongating ribosome stall [32]. These residues are poor substrates for nucleophilic attack to generate a peptide bond and also reorient the ribosomal peptidyl transferase center to reduce termination activity [28]. Termination activity is further reduced through interactions between the uORF2 nascent peptide and the GGQ motif within eRF1 [46]. Even though the A-site of the uORF2-stalled ribosome is occupied by a stop codon, we refer to it as an elongating ribosome stall since they are functionally equivalent for the purposes of this study. This terminology is also inclusive of elongation stalls within other uORFs [22,23,47–51]. The 5′ leader region preceding the UL4 coding sequence contains two other uORFs besides uORF2. uORF1 slightly reduces uORF2 repressiveness by siphoning scanning ribosomes away from uORF2, and uORF3 is irrelevant for repression [31]. We inserted the uORF2-containing UL4 leader sequence into a dual-luciferase reporter system (Fig 2B) in which nanoluciferase (NLuc) signal provides a readout of uORF2 repressiveness and firefly luciferase (FLuc) signal serves as normalization for transfection efficiency. This experimental platform can detect differences in luciferase activity over a 1000-fold range (S1 Fig). We confirmed that uORF2 repressiveness depends on its translation and the terminal diproline-dependent elongating ribosome stall (Fig 2C). Near-cognate start codons within uORF2 do not contribute to the uORF2 repressiveness (S1 Fig). We used this UL4-based luciferase reporter to quantitatively dissect the kinetics of uORF2-mediated translational regulation. We complemented our experimental measurements with computational kinetic modeling of proposed models of uORF regulation (Fig 1). We aimed to find unique modeling predictions that would allow us to experimentally distinguish between the different models of uORF regulation. We specified the kinetics of each of the proposed models of uORF regulation using PySB, a framework for compact specification of rule-based models [58]. We then expanded the model into the BioNetGen modeling language syntax [59] and inferred a reaction dependency graph for efficient simulation [39]. Next, we stochastically simulated the models using an agent-based Gillespie algorithm implemented in NFSim [60]. The molecules and reactions within the kinetic model are shown in Fig 3A and 3B, respectively, and are described in detail in the Materials and Methods section. We experimentally tested predictions from this computational modeling and used the results to refine our model specifications. This iterative cycle of experimental testing and computational modeling constituted our platform for differentiating between proposed uORF regulatory models. To derive estimates for unknown parameters (`This work’ in Table 1), we first calibrated our computational models to our reporter measurements on wild-type or mutant uORF2 (Fig 2C). We did not fit the constitutive repression and regulated re-initiation models (Fig 1A and 1E) to our reporter measurements (Fig 2C) since these models cannot account for the critical role of the UL4 uORF2 elongating ribosome stall in regulating main ORF translation in single uORF-containing mRNAs. We used previously generated estimates for kinetic parameters not directly inferred in our work (Table 1). Simulations of the queuing-mediated enhanced repression (Fig 1C) and collision-mediated 40S dissociation (Fig 1D) models readily recapitulate measurements of NLuc protein output from wild-type and mutant UL4 reporters (Fig 2D, triangles and squares). The 80S-hit dissociation model (Fig 1B), modified to include an elongating ribosome stall within the uORF, also recapitulates the reporter measurements (Fig 2D, circles). However, this modified 80S-hit dissociation model requires the difference between the stronger Kozak and wild-type uORF initiation fractions to be quite large (80% vs. 2% compared to 50% vs. 10% for 2 other models mentioned above, Table 1). The derived ribosome loading rates (~0.02/s for all three of these models (Fig 1B–1D) are in line with literature estimates [52–54]. The re-initiation fractions derived here (50–70%, Table 1) are within the range of measured values across mRNAs with different sequence features [40–43]. A complete description of the derivation of model parameters can be found in the Materials and Methods section.
Following calibration of our computational models to recapitulate experimental data, we used these models to predict how translation would be perturbed upon varying other kinetic parameters. While many kinetic parameters could be varied to help distinguish between proposed models of uORF regulation (Fig 1), we honed in on the rate of ribosome loading onto the mRNA for two key reasons. Firstly, this rate is reduced endogenously in response to a variety of cellular and environmental signals. Amino acid deprivation, ribosome collisions, dsRNA viral infection, unfolded proteins, and heme deprivation are sensed by one of the four eIF2α kinases (GCN2, PKR, PERK, and HRI) to reduce TC concentration [61–64]. A reduction in the concentration of eIF2α-containing TCs reduces the rate of ribosome loading. Viral infection also leads to reduced ribosome loading via interferon-induced proteins with tetratricopeptide repeats (IFITs) [65]. Cellular stress also reduces ribosome loading via inhibition of mTOR and sequestration of eIF4E by hypophosphorylated 4EBP [66]. Secondly, translated repressive uORFs are enriched in transcripts buffered against reduced ribosome loading [67–70]. Therefore, we were particularly interested in varying this ribosome loading rate to investigate if and how uORFs provide this buffering across various proposed models. For each of the five surveyed models of uORF regulation (Fig 1), we investigated what uORF parameter combinations, if any, allow buffering against reduced ribosome loading rates. We use the term ‘buffer’ to describe the observation of main ORF protein output decreasing less than expected, or even increasing, with reduced ribosome loading in comparison to the constitutive repression model (Fig 1A). The constitutive repression model (Fig 1A) has no buffering (Fig 4A) since its repression is independent of the ribosome loading rate. Buffering requires an interaction between ribosome loading and the degree of translational repression. We use buffering as an overarching term that encompasses both resistance and preferred translation. Resistance refers to a decrease in main ORF protein output to a lower extent than in the constitutive repression model when ribosome loading is reduced. Preferred translation refers to increased main ORF protein output when ribosome loading is reduced. The 80S-hit dissociation model (Fig 1B) displays buffering (Fig 4B, left panel, yellow-green line) in agreement with previous work [38]. This behavior arises because the number of 5′ elongating ribosomes that collide with scanning ribosomes correlates with the ribosome loading rate. However, buffering requires strong uORF initiation, minimal re-initiation, and a long uORF (Fig 4B, left panel, yellow-green line, S2A Fig) as observed previously [38]. These observations can be rationalized as follows. Strong uORF initiation generates sufficient elongating ribosomes that hit and knock off 3′ scanning ribosomes. Minimal re-initiation prevents the many uORF-translating ribosomes from also translating the main ORF. Longer uORFs offer more time for elongating ribosomes to catch up, hit, and knock off 3′ scanning ribosomes. However, most eukaryotic uORFs only weakly initiate translation and are short [1,3,4,8–11,31]. UL4 uORF2 is 22 codons long, and we estimate re-initiation to be frequent (Table 1). Accordingly, buffering is no longer observed (S2B Fig) in the 80S-hit dissociation model when parameters (Table 1) derived from control UL4 variants (Fig 2C) are used. The queuing-mediated enhanced repression model [23] (Fig 1C) displays buffering behavior (Fig 4C, left panel, purple line) since the number of scanning ribosomes that initiate translation at the uORF is dependent on the rate of ribosome loading. In this model, reduced ribosome loading decreases the average queue length of ribosomes behind the elongation stall and, thus, also the fraction of ribosomes that initiate at the uORF2 start codon (S2C Fig, left). Unlike the 80S-hit dissociation model (Fig 1B), weakly initiating uORFs, such as UL4 uORF2, still confer buffering in the queuing-mediated enhanced repression model (Fig 4C, left panel, purple line). In the queuing-mediated enhanced repression model, enhanced uORF initiation and, therefore, buffering are sensitive to the distance between the uORF start codon and the elongating ribosome stall (dstall) (S2C Fig and Fig 4C, left vs. right panels). This sensitivity arises because dstall determines if the P-site of a queued scanning ribosome is correctly positioned at the uORF start codon to productively increase uORF initiation (S2C Fig, left). In the idealized case of homogeneously sized ribosomes (30 nt footprints [56,71]) and strict 5′-3′ scanning, dstall must be an integer multiple of 30 nt for buffering to occur. This strong dependence of buffering on dstall is relaxed when backward scanning [41,72–74] occurs with a high rate (S2D Fig, middle). To simplify our modeling interpretations, we considered UL4 uORF2, that is 22 codons long, to be 21 codons so that a queue behind the terminating ribosome stall positions a scanning ribosome’s P-site exactly at the start codon. The collision-mediated 40S dissociation model (Fig 1D) displays buffering (Fig 4D, right panel, purple line) because the number of scanning ribosomes that collide with 3′ stalled ribosomes depends on the rate of ribosome loading. Buffering in this model requires the collision-induced 40S dissociation rate to be somewhat fast (Fig 4D, right vs. left panels, S2E Fig, teal and yellow-green lines). If this rate is too low (for example, 0 in Fig 4D, left panel), this model reduces to the queuing-mediated enhanced repression model (Fig 1C). With an appreciable dissociation rate, the collision-mediated 40S dissociation model is not sensitive to the distance between the stall and the start codon (S2F Fig, purple lines). As in the queuing model (Fig 1C), weakly initiating uORFs, such as UL4 uORF2, can still confer buffering (Fig 4D, right panel, purple line) in the collision-mediated 40S dissociation model (Fig 1D). This effect arises because, unlike in the 80S-hit dissociation model (Fig 1B), the elongation stall is now rate-limiting for main ORF translation. Therefore, an elongation stall in the collision-mediated 40S dissociation model or in the queuing-mediated enhanced repression model with permissive dstall spacing imparts buffering. In the regulated re-initiation model (Fig 1E), buffering is observed (Fig 4E, right panel, yellow-green line) because termination at the first uORF stop codon is followed by re-initiation at either the second downstream uORF or the main ORF depending on the ternary complex concentration. Buffering in the regulated re-initiation model (Fig 1E) depends on the initiation efficiency and continued scanning fraction (fraction of terminating but non-recycling ribosomes) of the two uORFs (Fig 4E). Continued scanning following termination at the first uORF must be frequent while continued scanning following termination at the second downstream uORF must be rare. Higher ternary complex concentrations bias towards initiation at the second downstream uORF (S3A Fig). Reductions in ternary complex concentrations bias towards main ORF initiation; therefore main ORF translation can increase with decreased ribosome loading. As such, our computational results provide the first systematic dissection of different mechanisms of uORF-mediated regulation (Fig 1) and enable their comparison with experimental measurements below.
We next tested whether the computational predictions of uORF-mediated buffering (Fig 4) can be observed experimentally with UL4 uORF2. To this end, we experimentally varied the rate of ribosome loading and measured effects on main ORF protein output using our reporter system (Fig 2B). Since the no-stall uORF2 variants have similar protein expression to the no-start uORF2 variants (Fig 2C), luciferase signal from the no-stall uORF2 variants provides a readout of the ribosome loading rate. If buffering were absent, then we would expect NLuc translation to be reduced equally between the no-stall and wild-type variants when ribosome loading is reduced. We used three strategies to vary the rate of ribosome loading. We first used stem-loops near the 5′ cap to reduce the rate of 43S-cap binding without affecting mRNA stability (Fig 5A) [75,76]. We varied the degree to which ribosome loading is reduced by altering the GC content of the stem-loops; generally, higher GC content stem-loops are more stable and therefore cause greater reductions in ribosome loading. We observe that NLuc signal decreases less with reduced ribosome loading for the wild-type UL4 reporter in comparison to the no-stall UL4 variant (Fig 5A, left panel, yellow vs. gray circles). Therefore, NLuc protein output is resistant to stem-loop-mediated reduction in ribosome loading. When the wild-type data are normalized by the no-stall data, NLuc translation negatively correlates with ribosome loading, indicative of buffering against reduced ribosome loading by wild-type uORF2 (Fig 5A, right panel). We also reduced ribosome loading with the drug thapsigargin, which induces the integrated stress response (ISR) by triggering ER stress (Fig 5B) [79]. We added a PEST tag [78] to increase the turnover of the NLuc protein to more accurately measure changes in main ORF translation during drug treatment. NLuc protein output from the wild-type UL4 reporter decreases less in comparison to the no-stall control upon thapsigargin treatment (Fig 5B, left panel, yellow vs. gray circles), indicative of resistance. Again, when the wild-type data are normalized by the no-stall data, we observe that NLuc translation negatively correlates with ribosome loading, indicative of buffering against reduced ribosome loading by wild-type uORF2 (Fig 5B, right panel). Finally, we added a short, synthetic uORF, 5′ to the UL4 uORF2, to siphon scanning ribosomes away from uORF2 (Fig 5C). We varied the degree of ribosome siphoning by varying the Kozak context of the synthetic uORF, which in turn determines the rate of ribosome loading onto the uORF2-NLuc portion of the mRNA. Here, we observe that more NLuc is produced from the wild-type UL4 reporter as scanning ribosomes are increasingly siphoned off by improving the Kozak context of the synthetic uORF (Fig 5C, left panel, yellow circles). While resistance is observed with the other strategies of reduced ribosome loading (Fig 5A and 5B, left panels), preferred translation is observed here (Fig 5C, left panel), perhaps because ribosome loading is reduced at the scanning step instead of at the cap-binding step. Similar to the other two strategies, when the wild-type data are normalized by the no-stall data, NLuc translation negatively correlates with ribosome loading, indicative of buffering against reduced ribosome loading by wild-type uORF2 (Fig 5C, right panel).
Given our experimental data demonstrating uORF2-mediated buffering of UL4 reporters (Fig 5), we narrowed our focus from the five surveyed models (Fig 1) to the two (Fig 1C and 1D) most relevant for UL4 uORF2: the queuing-mediated enhanced repression (Fig 1C) and collision-mediated 40S dissociation (Fig 1D) models. These two models are computationally predicted to confer buffering in an elongating ribosome stall-dependent manner without needing multiple uORFs (Fig 4C and 4D). To differentiate between these models, we turned to our computational modeling prediction that, only in the queuing-mediated enhanced repression model (Fig 1C), main ORF protein output is sensitive to the distance between the uORF start codon and the elongating ribosome stall (Fig 4C). Our computational modeling of the queuing-mediated enhanced repression model (Fig 6A, yellow-green line) predicts two broadly spaced clusters of main ORF protein output. Protein output from the main ORF is repressed when the start codon-stall distance is an integer multiple of the ribosome size. Protein output from the main ORF is high when the start codon-stall distance is not an integer multiple of the ribosome size. In contrast, the collision-mediated 40S dissociation model (Fig 1D) predicts a much lower effect of dstall on uORF repressiveness (Fig 6A, left panel, purple line). The residual effect of dstall on uORF repressiveness (Fig 6A, left panel, purple line) in the collision-mediated 40S dissociation model (Fig 1D) arises because the dissociation rate is low enough to allow rare queuing. Backward scanning is predicted to diminish the periodicity in main ORF translation with varying dstall lengths in both models (Fig 6A). However, backward scanning occurring as fast as forward scanning (~ 5 nucleotides/s) is required to abolish the periodicity in the queuing model (Fig 6A, right panel, yellow-green line). While there are estimates of how far ribosomes can backward scan [72–74], we are not aware of any backward scanning rate estimates. It is unlikely that the rate of backward scanning approaches the rate of forward scanning (5 nucleotides/s here) given the 5′-3′ directionality of scanning. Slower backward scanning (~ 3.75 nucleotides/s) is sufficient to abolish periodicity in the collision-mediated 40S dissociation model (Fig 6A, middle panel, purple line). This effect is not surprising given that the presence of periodicity in the latter model arises from rare queuing behavior. Therefore, our computational predictions of greater periodicity in main ORF translation across varied dstall in the queuing model hold even with backward scanning. We then experimentally varied the distance between the start codon and the stall of the UL4 uORF by adding codons to the 5′ end of uORF2. With EYFP donor sequences, we observe less than 2-fold changes in translational regulation (Fig 6B, top 7 rows) with no systematic trend with variations in uORF2 length, which is inconsistent with computational modeling predictions of the queuing-mediated enhanced repression model (Fig 6A, left panel). We observe similar results with a different donor sequence (S4 Fig, top 7 rows), confirming the generality of the observed repression with changes in uORF2 length. With both donor sequences, the longest uORF mutants are less repressive, but this effect may be due to decreased elongating ribosome stall strength. In these cases, the longer nascent peptides can extend out of the exit tunnel and can be bound by additional factors [27,28] or cotranslationally fold to exert a pulling force [80] to relieve the stall. Thus, in summary, varying the length of the UL4 uORF2 stall does not match computational predictions for sensitivity of main ORF repression to dstall in the queuing-mediated enhanced repression model (Fig 1C) and better supports the collision-mediated 40S dissociation model (Fig 1D). In the queuing-mediated enhanced repression model (Fig 1C), buffering is uniquely predicted to be sensitive to the distance between the uORF start codon and the elongating ribosome stall (Fig 4C). We, therefore, asked whether or not buffering would still be experimentally observed with a disruption in this distance. Using our synthetic uORF method of reducing ribosome loading (Fig 6C), we observe that a 6 nt longer dstall uORF still buffers against reduced ribosome loading (Fig 6C, top two rows compared to bottom two rows). Since backward scanning of 15–17 nt has been observed [72–74], one would expect that buffering would still be predicted in the queuing model even with an increase in dstall of 6 nt. However, our computational modeling predicts that even very fast backward scanning does not restore buffering when dstall is disrupted by 6 nt (S2D Fig, right). Thus, our experimental data does not match computational predictions of buffering sensitivity to dstall in the queuing-mediated enhanced repression model (Fig 1C) but is consistent with the collision-mediated 40S dissociation model (Fig 1D).
Given that the elongating ribosome stall in the human cytomegaloviral UL4 uORF2 is dependent on a terminal diproline motif, we asked whether there are other human uORFs similarly ending in diproline motifs that are also repressive. We searched for such uORFs in three databases: a comprehensive database of ORFs in induced pluripotent stem cells and human foreskin fibroblasts with 1,517 uORFs [6], a database integrated from de novo transcriptome assembly and ribosome profiling with 3,577 uORFs [81], and a database of proteins less than 100 residues in size derived from literature mining, ribosome profiling, and mass spectrometry with 1,080 uORFs [82]. We identified several human transcripts with terminal diproline-containing uORFs: C1orf43, C15orf59, TOR1AIP1, PPP1R37, and ABCB9. We replaced UL4 uORF2 in our reporter (Fig 2B) with these human uORFs. We mutated the terminal proline codon to alanine codon as well as the start codon of these human uORFs and measured the effects of these mutations on NLuc protein output relative to the wild-type uORFs. While many of the tested uORFs are repressive (Fig 7, yellow vs. blue), unlike the human cytomegaloviral uORF2, these human uORFs still repress NLuc protein output without their terminal diproline motif (Fig 7, gray vs. blue), indicating additional contributions to translational repression from other residues in the nascent peptide and due to siphoning of scanning ribosomes at the start codon.
In this study, we use a combination of computational modeling and experimental reporter measurements to dissect the kinetics of uORF-mediated translational regulation of the UL4 mRNA of human cytomegalovirus. We find that the elongating ribosome stall in UL4 uORF2 buffers against reductions in main ORF protein output due to reduced ribosome loading (Fig 4). Using an experimentally-integrated modeling approach, we differentiate between models of regulation that can explain this observation. Our computational framework allows easy specification and efficient simulation of several previously proposed kinetic models of uORF regulation (Fig 1). While uORFs are enriched in stress-resistant transcripts, not all uORFs provide buffering [67]. We can predict which models of uORF regulation allow buffering and which parameters are key for buffering in each model (Fig 4). To our knowledge, our work is the first systematic investigation of what uORF metrics impart buffering in each kinetic model of uORF regulation. uORFs are generally thought to simply siphon away scanning ribosomes from main ORFs, but this simple behavior in the constitutive repression model (Fig 1A) is not predicted to provide buffering (Fig 4C) [67–70]. Instead, we find that 5′ UTRs containing one (or some combination) of the following enable buffering of main ORF translation: scanning ribosome dissociation due to 80S hits from the 5′ end (Fig 1B), a single uORF with an elongating ribosome stall (Fig 1C and 1D), or multiple uORFs acting through the regulated re-initiation model (Fig 1E). Long, well-initiating uORFs that do not re-initiate well allow buffering (Fig 4B, left panel, yellow-green line, S2 Fig panel A, yellow-green line) in the 80S-hit model (Fig 1B), but these requirements are at odds with the typically short and poorly initiating nature of known uORFs [1,3,4,8–11]. Indeed, when we use parameters specific to UL4 uORF2 for the 80S-hit model (Table 1), namely that uORF2 initiates poorly, re-initiates well, and is not very long, buffering is no longer predicted (S2B Fig). Computational predictions from the regulated re-initiation model (Fig 1E) agree (Figs 4E and S3A) with previous work [35,36] showing that buffering requires: 1) two well-translated uORFs and 2) frequent and rare continued scanning after termination at uORFs 1 and 2, respectively. Since 30% of human transcripts contain multiple uORFs, some of these might enable buffering by the regulated re-initiation model. However, about 25% of human transcripts only have one uORF [2] and cannot provide buffering under this model. We narrowed our focus to the two models (Fig 1C and 1D) that are most pertinent to UL4 uORF2. Both the queuing-mediated enhanced repression (Fig 1C) and collision-mediated 40S dissociation (Fig 1D) models are predicted to allow buffering (Fig 4C and 4D) with weakly initiating uORFs and elongating ribosome stalls. Both of these models require only a single uORF for buffering (Fig 4C and 4D). Computational modeling not only predicts this buffering behavior but also allows us to differentiate between these two models. We predict that the queuing-mediated enhanced repression model (Fig 1C) is uniquely sensitive to the distance between the uORF start codon and elongating ribosome stall (Fig 6A, yellow-green line, Fig 4C, purple lines, S2F Fig, purple lines). We experimentally vary this distance and do not find any systematic changes in either main ORF protein output (Fig 6B) or buffering (Fig 6C). Based on our results, we propose that scanning ribosomes dissociate rather than queue when encountering a 3′ stalled elongating ribosome on uORF2 of UL4 mRNA. Scanning ribosomes have been predicted to dissociate upon encountering stable secondary structures on the mRNA [83]. Collisions between scanning ribosomes and their subsequent dissociation have also been proposed in a model of initiation quality control [84]. This dissociation could serve to maintain the free pool of 40S ribosomal subunits while still allowing regulation of main ORF translation. Collisions between scanning and elongating ribosomes and subsequent quality control are not well understood; what we describe as scanning ribosome dissociation here may be rescue by a quality control pathway. Although our data from UL4 uORF2 does not support the queuing-mediated enhanced repression model (Fig 1C) [23], this model might describe translation kinetics on other mRNAs. Translation from near-cognate start codons is resistant to cycloheximide, perhaps due to queuing-mediated enhanced initiation, but sensitive to reductions in ribosome loading [85]. Loss of eIF5A, which helps paused ribosomes continue elongation, increases 5′ UTR translation on human mRNAs with pause sites proximal to the start codon, perhaps also through queuing-mediated enhanced initiation [86]. There is also evidence of queuing-enhanced uORF initiation in the 23 nt long Neurospora crassa arginine attenuator peptide [87] as well as in transcripts with secondary structure near and 3′ to start codons [88]. Additional sequence elements in the mRNA might determine whether scanning ribosome collisions result in queuing or dissociation. Small subunit profiling data [89] from human uORFs that have conserved amino acid-dependent elongating ribosome stalls do not show evidence of scanning ribosome queues (S5A Fig), consistent with the collision-mediated 40S-dissociation model. However, subtle queues might not be observed given low read counts arising from insufficient capture of small ribosomal subunits in these experiments. In our modeling, we assume homogenous footprint lengths of 30 nt for both scanning and elongating ribosomes. Even though heterogeneously sized footprints have been observed for small ribosomal subunits [89–91] and elongating ribosomes [92,93], our modeling of homogenous footprint length is appropriate for the following reasons. Firstly, with respect to the small ribosomal subunit footprints, crosslinking of associated eIFs is thought to be the main source of length heterogeneity [89,90], and homogenous 30 nt footprints are observed in the absence of crosslinking [90]. Secondly, in the context of the strong, minutes-long UL4 uORF2 elongating ribosome stall [57], collided ribosomes, if they do not dissociate, will wait for long periods of time in a queue relative to normal scanning or elongating ribosomes, during which associated eIFs likely dissociate [90]. Thirdly, a sizable fraction of mRNAs exhibit cap-tethered translation in which eIFs must dissociate from ribosomes before new cap-binding events, and therefore collisions, can occur [90]. Elongating ribosome footprint heterogeneity is much less drastic than that observed for scanning ribosomes and likely arises from different conformational states such as empty or occupied A sites [92,93]. While different elongating ribosome footprints arise from differences in mRNA accessibility to nucleases, it is unclear whether the distance between two collided ribosomes changes across different ribosome conformations. In addition to the UL4 viral uORF studied here, several human uORFs are known to contain an elongating ribosome stall [22,23,26,47]. Apart from terminal diprolines, other motifs such as Arg-X-Lys at E-P-A sites [94] or specific dipeptides such as Gly-Ile, Asp-Ile, Gly-Asp [95] can also cause elongation stalls. There are a variety of other mechanisms that may reduce the rate of elongation, such as mRNA stem-loops and G-quadruplexes [96], low tRNA availability [97,98], or interactions between the nascent peptide and the ribosome [99,100]. uORFs are often short [3] and may therefore be better poised to stall ribosomes since the nascent peptides might not be accessible to co-translational factors that pull the nascent peptide out of the ribosome [101,102]. Thus, a key role for elongating ribosome stalls in uORFs might be to enable buffering. While few uORF stalls have been mechanistically characterized [1], other elongating ribosome stall-containing uORFs, such as the ones in MTR [47] and AMD1 [103] mRNAs, might enable buffering; the elongating ribosome stall-containing uORFs in AZIN1 [23], PPP1R15A (GADD34) [104], and DDIT3 (CHOP) [22] have already been shown to enable buffering. Conversely, uORFs in several single uORF transcripts known to buffer against stress, such as SLC35A4, C19orf48, and IFRD1 [67], might act through elongating ribosome stalls. The computational models considered here can be readily extended to incorporate more complex mechanisms of translational control. For example, in our models, initiation proceeds via a cap-severed mechanism in which multiple scanning ribosomes can be present in the 5′ UTR at the same time. If we were to model cap-tethered initiation, strong uORF elongating ribosome stalls would eventually sever this connection, similar to how the cap-eIF-ribosome connection is severed during the usually longer translation of main ORFs [90,105,106]. It will also be interesting to consider the effect of cellular stress-reduced elongation rates [107] and increased re-initiation [108], both of which might regulate uORF-mediated buffering, as well as elongating ribosome dissociation through known quality control pathways [39,84,109–114]. Translation heterogeneity among isogenic mRNAs has been observed in several single-molecule translation studies [33,52–54,115]. This heterogeneity may arise from variability in intrasite RNA modifications [116], RNA binding protein occupancy, or RNA localization. We do not capture these sources of heterogeneity in our modeling since the observables in our simulations are averaged over long simulated time scales and used to predict only bulk experimental measurements. However, the models studied here can readily be extended through compartmentalized and state-dependent reaction rates [59] to account for the different sources of heterogeneity observed in single-molecule studies.
The parent cloning vector was created as follows. A commercial vector (Promega pGL3) with ampicillin resistance was used to clone NLuc and FLuc. NLuc expression is driven by a CMV promoter. FLuc expression is driven in the opposite direction within the plasmid and serves as an internal transfection control. The human cytomegaloviral UL4 5′ UTR was PCR amplified from HCMV genomic DNA. To create mutant 5′ UTR versions of the parent pGL3-FLuc-NLuc vector, the vector was digested with KpnI/EcoRI unless otherwise noted. 1 or 2 PCR-amplified fragments with 20–30 bp homology arms were then cloned using isothermal assembly [117]. The stem-loop [76] 5′ UTR mutants were cloned as follows. The stem-loops were ordered as oligonucleotides with overhangs for ligation into ClaI and NotI sites. The oligonucleotides were annealed and used in PCR reactions to add CMV homology arms. An AAVS1 parent vector was digested with ClaI and NotI. These stem-loops were then inserted into the ClaI/NotI restriction digested parent vector by isothermal assembly [117]. The stem-loops were then PCR amplified off of this plasmid and inserted into the pGL3-Fluc-UL4-5′-UTR-NLuc parent vector described above. The several tested human uORFs were PCR amplified from human genomic DNA and inserted into a PstI/EcoRI digested parent. The inserted sequences were confirmed by Sanger sequencing. Kozak context and stall codon mutations were introduced in the PCR primers used for amplifying inserts before isothermal assembly. Standard molecular biology procedures were used for all other plasmid cloning steps [118]. S1 Table lists the plasmids described in this study. Key plasmid maps are available at https://github.com/rasilab/bottorff_2022 as SnapGene.dna files. Plasmids will be sent upon request.
HEK293T cells were cultured in Dulbecco′s modified Eagle medium (DMEM 1X, with 4.5 g/L D-glucose, + L-glutamine,—sodium pyruvate, Gibco 11965–092) and passaged using 0.25% trypsin in EDTA (Gibco 25200–056).
Plasmid constructs were PEI or Lipofectamine 3000 (Invitrogen, L3000-008) transiently transfected into HEK293T cells for 12-16h in 96 well plates. After the 12-16h transfection, the ~110 μL media was removed and replaced with 20 μL media per well. The Promega dual-luciferase kit was used. Cells were lysed with 20 μL ONE-Glo EX Luciferase Assay Reagent per well for three minutes to measure firefly (Photinus pyralis) luciferase activity. Then, 20 μL NanoDLR Stop & Glo Reagent was added per well for 10 minutes to quench the FLuc signal and provide the furimazine substrate needed to measure NLuc luciferase activity. FLuc activity serves as an internal control for transfection efficiency, and NLuc activity provides a readout of 5′ UTR regulation of NLuc translation.
We specify our kinetic models using the PySB interface [58] to the BioNetGen modeling language [59] (Fig 3). The Python script is parsed by BioNetGen into a.bngl file and converted into an xml file for use as input to the agent-based stochastic simulator NFsim [60].
Our kinetic models of eukaryotic translational control describe the interactions between 3 molecule types: mRNA, ribosome (composed of separate large and small subunits), and ternary complex. Here, we describe these molecules’ components, states, and binding sites (Fig 3A). mRNA molecules have the following components: 5′ end and codon sites (ci). The mRNA 5′ end can either be free of (clear) or occupied with a ribosome (blocked). The mRNA 5′ end must be clear for a 43S to bind, which leaves the 5′ end blocked until the ribosome scans (or elongates) sufficiently 3′ downstream. The mRNA codon sites serve as binding sites for the ribosome A site. Small ribosomal subunits have the following components: inter-subunit binding interface (isbi), ternary complex contact (tc), 5′ side (t for trailing), 3′ side (l for leading), and A site (a). The inter-subunit binding interface site allows interactions between small and large ribosomal subunits; large ribosomal subunits also have the inter-subunit binding interface (isbi) components. The 5′ and 3′ side sites serve as binding sites for other ribosomes during collisions (5′ or 3′ side). The A site serves as a binding site for the mRNA. Both scanning and elongating ribosomes have mRNA footprints of 10 codons in our simulations based on mammalian ribosome profiling data [56,71]. Ternary complex molecules have a single component ssusite that serves as a binding site for the small ribosomal subunit.
We describe here each type of kinetic reaction in our models of eukaryotic translational control (Fig 3B). We use a syntax similar to that of BioNetGen [59] to illustrate the kinetic reactions. We scale TC and ribosome subunit numbers (100 each) to the single mRNA present in the simulation. Simulation of a single mRNA over several rounds of translation is sufficient to infer steady state translation dynamics.
Small ribosomal subunits must bind TCs to form pre-initiation complexes (PICs, 43Ss) before loading onto mRNAs. We assume that PIC formation is irreversible. PIC formation is not rate-limiting in our simulations; we set the rate of 43S-cap binding (kcap bind) to be rate-limiting and to a total rate (independent of [43S]) to match the overall initiation rate to that of cellular estimates. Therefore, we arbitrarily set the second-order PIC formation rate (40S-TC binding rate, kssu tc bind) to 0.01 * TC−1 * SSU−1 such that 100 40S-TC binding events occur per second, which is much higher than the 43S-cap binding rate.
We model ribosome footprints at 30 nt following mammalian ribosome profiling data [56,71]. Therefore, PIC loading can occur when the 5′ most 30 nucleotides (nt) of the mRNA are not bound to any ribosome. The rate at which PICs load onto the 5′ end of the mRNA, kcap bind, is varied over a 100-fold range from the maximum ribosome loading rate, 0.125/s, based on single-molecule estimations in human cells [52]. PICs can load onto the mRNA when a ribosome footprint-sized region at the 5′ mRNA end is free of ribosomes. PIC loading results in the 5′ end being blocked until this ribosome scans or elongates past a ribosome footprint from the 5′ cap. We assume that PIC loading is irreversible.
The scanning rate is 5 nucleotides/s following estimates in a mammalian cell-free translation system [55] and a previous computational study [38]. Small ribosomal subunit A sites must be positioned exactly over start codons to initiate translation. The uORF start codon is 25 nt from the 5′ cap. We vary the rate at which this start codon selection occurs at the uORF in our modeling. Start codon selection releases the TC bound to the small ribosomal subunit. We assume that TC is regenerated instantaneously. The start codon selection rate divided by the sum of this start codon selection rate, the scanning rate, and the backward scanning rate equals the baseline initiating fraction. This calculation of the baseline initiating fraction will underestimate the initiating fraction in the case of correctly positioned 3′ ribosome queues (as in the queuing-mediated enhanced repression model). We assume that start codon selection is irreversible.
Elongation results in the ribosome A site moving from codon ci to codon ci+1. The rate of elongation is set to 5 codons/s following single-molecule method and ribosome profiling estimates in mammalian cells of 3–18 codons/s [52–54,56,119]. Elongation may only proceed if there is no occluding 3′ ribosome; in other words, elongation may only proceed from codon ci to codon ci+1 if the next 3′ ribosome′s A site is bound to a codon no more 5′ than ci+11. The elongation rate at the stall within the uORF is set to 0.001/s [57].
Termination results in the dissociation of the large ribosomal subunit, but the small ribosomal subunit may continue scanning and subsequently re-initiate if a new TC is acquired before the next start codon is encountered. The termination rate is set to 1/s given that ribosome density tends to be higher at stop codons than within ORFs [56,92]. The recycling rate of terminated small ribosomal subunits after uORF translation is varied to model the effect of varied continued scanning after uORFs on the regulation of main ORF translation. The scanning rate divided by the sum of the scanning rate and this recycling rate equals the continued scanning fraction.
A collision between two ribosomes requires them to be separated by exactly one ribosome footprint in distance on the mRNA and results in binding between the 5′ side of the leading (3′ most) ribosome and the 3′ side of the trailing (5′ most) ribosome. Abortive (premature) termination of ribosomes results in their dissociation from the mRNA and any collided ribosomes they are bound to. Different models have different non-zero dissociation rates. For instance in the 80S-hit model, the following rates are equal and non-zero: kscan term 5 hit 80s, kscan term both hit 80s 80s, kscan term both hit 80s 40s. These rates relate to the dissociation of scanning ribosomes upon collisions with a 5′ elongating ribosome. Both hit refers to collisions with ribosomes on both sides. In the collision-mediated 40S dissociation model, the following rates are equal and non-zero: kscan term 3 hit 40s, kscan term 3 hit 80s, kscan term both hit 40s 40s, kscan term both hit 40s 80s, kscan term both hit 80s 40s, kscan term both hit 80s 80s. These rates relate to the dissociation of scanning ribosomes upon collisions with a 3′ scanning or elongating ribosome. The in vivo abortive termination rates of scanning ribosomes are not known. Small ribosomal subunits that make it to the 3′ end of the mRNA through leaky scanning of all (u)ORFs always dissociate.
We derive the kcap bind rates by spline interpolation of computationally modeled protein output fit to experimental data (Fig 2C). We minimized the root mean square error between modeled protein output across variations in these parameters and the experimental data.
We import uORF lists from several databases [6,81,82]. The SmProt database [82] includes 3162 uORFs from ribosome profiling data, which we filter down first to 1080 uORFs after filtering for aligned matches, available Kozak context, near-cognate start codons, and non-duplicates. Two of these uORFs end in diproline motifs, including C1orf43. Another database is a set of high confidence ORFs derived from ribosome profiling of human-induced pluripotent stem cells (iPSCs) or foreskin fibroblast cells (HFFs) and was downloaded from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720255/bin/NIHMS741295-supplement-3.csv [6]. This database includes 1517 high confidence (ORF-RATER score > 0.8) uORFs from either iPSCs or HFFs, which we filter down to 3 that end in diproline motifs, including ABCB9, C1orf43, and TOR1AIP1. The third database derives from HEK293T, HeLa, and K562 cells using ribosome profiling and was downloaded from https://static-content.springer.com/esm/art%3A10.1038%2Fs41589-019-0425-0/MediaObjects/41589_2019_425_MOESM3_ESM.xlsx [81]. This database includes 3577 uORFs which we filter down to 3 that end in diproline motifs and that are less than 60 codons in length for ease of cloning, including ABCB9,C15orf59, and PPP1R37.
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PMC9648852 | 36315578 | Smitha Srinivasachar Badarinarayan,Daniel Sauter | Not all viruses cause disease: HERV-K(HML-2) in healthy human tissues | 31-10-2022 | Human endogenous retroviruses (HERVs) make up a significant part of our genome. While their expression is frequently associated with disease, a new study in PLOS Biology found that HERV-K(HML-2) is expressed in more than 50 healthy tissues. | Not all viruses cause disease: HERV-K(HML-2) in healthy human tissues
Human endogenous retroviruses (HERVs) make up a significant part of our genome. While their expression is frequently associated with disease, a new study in PLOS Biology found that HERV-K(HML-2) is expressed in more than 50 healthy tissues.
Viruses are everywhere. They infect virtually all organisms on this planet. They can be found in the air we breathe, the flowers in our garden, and the depths of the ocean. Some of them can even be found in our DNA. These so-called human endogenous retroviruses (HERVs) represent remnants of once-infectious viruses that became fixed in our genome. While some of them still have the potential to produce virus-like particles, HERVs have lost their ability to generate infectious virions that are transmitted horizontally. Instead, they persist in our DNA as viral fossils that are passed on vertically from generation to generation. Viruses are generally perceived as nasty pathogens, whose transmission and spread must be prevented. Even the infectious ancestors of endogenous retroviruses may have been pathogenic, and expression of HERVs such as HERV-K(HML-2) has been associated with cancer, neurological disorders, and other diseases. However, several HERVs have also been co-opted during evolution and exert important physiological functions. The prime examples are 2 virus-derived glycoproteins (Syncytin-1 and Syncytin-2) that mediate cell–cell fusion during placenta development [1]. Without these viral proteins, normal pregnancy would not be possible. Other HERVs regulate cellular gene expression. For example, alcohol digestion is modulated by a virus-derived cis-regulatory element enhancing expression of the alcohol dehydrogenase 1C gene (ADH1C) [2]. Some HERVs even contribute to antiviral immune responses, e.g., by regulating antiviral gene expression [3,4]. To better understand the role of HERVs in human health and disease, a systematic comparison of their activity in healthy versus diseased tissues is required. Most previous studies, however, have focussed on HERV activation in the context of disease, and there is limited information on HERV expression in non-diseased tissues, particularly at the level of individual HERV loci. In this issue, Burn and colleagues closed this gap by systematically analysing HERV transcription in more than 50 healthy tissues from almost 1,000 donors [5] (Fig 1). The authors took advantage of RNA-sequencing datasets available via the Genotype Tissue and Expression (GTEx) Project [6]. For their analyses, Burn and colleagues focused on HERV-K(HML2). This ERV subclade represents the youngest HERV group and includes proviruses that are unique to humans. Some HERV-K(HML-2) proviruses are even polymorphic in the human population and may contribute to inter-individual variation [7]. Like all HERVs, HERV-K(HML-2) insertions have accumulated numerous deletions and mutations. Still, some HERV-K(HML-2) elements have retained intact open reading frames (ORFs) that may encode for functional proteins. Intriguingly, HERV-K(HML-2) expression was detectable in all tissues examined. Particularly, high expression levels were observed in the brain (cerebellum and pituitary), thyroid, and reproductive tissues (testes and ovaries) (Fig 1). The high levels of HERV-K(HML-2) transcripts in reproductive tissues may be a consequence of their ancestor’s ability to infect germ cells. To establish themselves in the human genome, retroviruses must enter the germ line. Thus, HERV-K(HML-2) has evolved from an exogenous retrovirus that infected germ cells and most likely exploited cellular transcription factors that are highly active in reproductive tissues. These properties may allow efficient LTR-mediated HERV-K(HML-2) transcription in testes and ovaries today. It is tempting to speculate that expression of HERV-K(HML-2) in other tissues may also be a relic of the tissue tropism of the ancestral exogenous retrovirus. Alternatively, selection processes may have changed the expression patterns of HERV-K(HML-2) after their fixation in the host genome. Importantly, Burn and colleagues did not only analyse total HERV-K(HML-2) transcription, but also expression of individual HERV-K(HML-2) loci. They detected 37 HERV-K(HML-2) proviruses that are expressed in at least 1 tissue. Five of them (1q21.3, 1q22, 3q12.3, 12q24.33, and 19q13.12b) were broadly transcribed in almost every tissue (Fig 1). Intriguingly, some of the HERV-K(HML-2) loci expressed in healthy tissues also contain intact ORFs (Fig 1). For example, gag-derived proteins may be produced by the broadly expressed HERV-K(HML-2) locus 3q12.3 and/or 12q14.1, which is specifically expressed in kidneys. Although stability and potential functions of these Gag proteins remain to be determined, HERV-derived Gag proteins may negatively interfere with the assembly of exogenous retroviruses [8]. In addition to Gag, a functional Env protein expressed from HERV-K(HML-2) 7p22.1a may be present in several healthy tissues, including blood. This particular Env protein has previously been shown to retain its fusogenic activity [9] and may thus induce syncytia formation in different tissues, provided that its receptor is also present. Notably, HERV-K(HML-2) 7p22.1a Env was also suggested to reduce HIV-1 infection [10]. Besides gag and env genes, intact HERV-K(HML-2) rec ORFs were also shown to be expressed in various tissues, confirming and expanding findings of an earlier study [11]. HERV-K(HML-2) Rec can be considered a functional homolog of HIV Rev. Both proteins interact with RNA structures and mediate the export of unspliced or incompletely spliced (viral) mRNA from the nucleus to the cytoplasm. While the presence of rec transcripts in several healthy tissues suggests that Rec proteins may have been co-opted by the human organism, the exact function of Rec proteins in human cells remains to be determined. In summary, the transcriptome analyses by Burn and colleagues help to identify active HERV loci that are neither cause nor consequence of disease, but may have neutral or even beneficial effects. Their study provides a valuable dataset of healthy controls that can be used to better assess the relevance of HERV activation in aberrant cells. Notably, the identification of HERV-K(HML-2) ORFs strongly suggests that virus-derived proteins are normal components of the human proteome in many tissues. Together with proteomic analyses, the findings by Burn and colleagues will therefore also help to assess the suitability of HERV proteins as biomarkers and their specificity for certain cancer entities and other aberrant cells. It should be borne in mind that the HERV-K(HML-2) group analysed in the present study is only one of many HERV subclades. It remains to be determined whether other HERV members exhibit similarly broad expression in healthy cells and tissues. Moreover, HERVs can also be active without being transcribed, e.g., by acting as enhancers or suppressors of gene expression. Thus, the number of HERVs that are active in our cells is most likely higher than we think, and many physiological HERV functions remain to be discovered. |
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PMC9648853 | 36315601 | Cynthia M. McMillen,Devin A. Boyles,Stefan G. Kostadinov,Ryan M. Hoehl,Madeline M. Schwarz,Joseph R. Albe,Matthew J. Demers,Amy L. Hartman | Congenital Rift Valley fever in Sprague Dawley rats is associated with diffuse infection and pathology of the placenta | 31-10-2022 | Rift Valley fever (RVF) is a disease of animals and humans associated with abortions in ruminants and late-gestation miscarriages in women. Here, we use a rat model of congenital RVF to identify tropisms, pathologies, and immune responses in the placenta during vertical transmission. Infection of late-gestation pregnant rats resulted in vertical transmission to the placenta and widespread infection throughout the decidua, basal zone, and labyrinth zone. Some pups from infected dams appeared normal while others had gross signs of teratogenicity including death. Histopathological lesions were detected in placenta from pups regardless of teratogenicity, while teratogenic pups had widespread hemorrhage throughout multiple placenta layers. Teratogenic events were associated with significant increases in placental pro-inflammatory cytokines, type I interferons, and chemokines. RVFV displays a high degree of tropism for all placental tissue layers and the degree of hemorrhage and inflammatory mediator production is highest in placenta from pups with adverse outcomes. Given the potential for RVFV to emerge in new locations and the recent evidence of emerging viruses, like Zika and SARS-CoV-2, to undergo vertical transmission, this study provides essential understanding regarding the mechanisms by which RVFV crosses the placenta barrier. | Congenital Rift Valley fever in Sprague Dawley rats is associated with diffuse infection and pathology of the placenta
Rift Valley fever (RVF) is a disease of animals and humans associated with abortions in ruminants and late-gestation miscarriages in women. Here, we use a rat model of congenital RVF to identify tropisms, pathologies, and immune responses in the placenta during vertical transmission. Infection of late-gestation pregnant rats resulted in vertical transmission to the placenta and widespread infection throughout the decidua, basal zone, and labyrinth zone. Some pups from infected dams appeared normal while others had gross signs of teratogenicity including death. Histopathological lesions were detected in placenta from pups regardless of teratogenicity, while teratogenic pups had widespread hemorrhage throughout multiple placenta layers. Teratogenic events were associated with significant increases in placental pro-inflammatory cytokines, type I interferons, and chemokines. RVFV displays a high degree of tropism for all placental tissue layers and the degree of hemorrhage and inflammatory mediator production is highest in placenta from pups with adverse outcomes. Given the potential for RVFV to emerge in new locations and the recent evidence of emerging viruses, like Zika and SARS-CoV-2, to undergo vertical transmission, this study provides essential understanding regarding the mechanisms by which RVFV crosses the placenta barrier.
Rift Valley fever (RVF) is a zoonotic disease of ruminants and humans caused by infection with Rift Valley fever virus (RVFV). RVFV is a negative-stranded, tri-segmented arbovirus of the Bunyavirales order (family: Phenuiviridae, genus: Phlebovirus) that is endemic to regions in Africa and the Arabian Peninsula. Outbreaks peak during the rainy seasons due to increases in competent mosquito vector populations (Aedes & Culex spp.) [1,2]. Rainy seasons can wreak havoc on agricultural industries due to heightened transmission of RVFV to cows, sheep, and goats leading to high rates of fetal abortions (10–40%, 10–60%, and 25–90%, respectively) and neonatal death. Susceptibility to death after RVFV infection occurs in an age-dependent manner; neonates are significantly more susceptible than adult animals [3]. Newborn and older lamb mortality can reach approximately 90% and 30%, respectively [4]. Cause of death in adult, juvenile, and fetal livestock is due to severe hepatic necrosis resulting from high levels of viral replication within the liver [5,6]. Live-attenuated vaccines are used in endemic regions to reduce the burden of disease in ruminants and the subsequent spread to people [7,8]. Still-borne lambs and calves born to mothers infected with RVFV show signs of arthrogryposis, muscle atrophy, hydranencephaly, and other nervous and musculoskeletal defects [5,6,9]. Pathologies such as raised caruncles and mineralized endometrium have also been documented in the uterus of naturally [10] and experimentally [11] infected sheep. Most reports of congenital RVF have documented gross physical abnormalities; few studies conducted detailed histological analysis of placenta in ruminants [12–14]. Vaccination of pregnant livestock with live-attenuated strains of RVFV can result in fetal teratogenicity from the vaccine strain itself [15]; therefore, vaccinations are restricted to non-pregnant animals. A major effort is underway to develop improved agricultural and human RVFV vaccines, including human clinical trials for the live-attenuated MP-12 vaccine. For these reasons it is important to understand the mechanisms of congenital disease in both livestock and humans. Like ruminants, humans can become infected with RVFV through mosquito bite. Alternatively, farmers, butchers, and veterinarians are more likely to contract RVF from contact with contaminated tissues and virus-containing aerosols during outbreaks in livestock [16]. Human RVF is generally acute, resulting in intense fever, malaise, dizziness, and headache. Severe cases (<1%) can result in hepatic disease, hemorrhage, or late onset encephalitis, all of which can be fatal [17]. Minimal epidemiological data is available concerning the risk of vertical transmission and teratogenicity in pregnant women. Vertical transmission during the third trimester of human pregnancy was documented in two RVFV-infected women with classical signs of RVF (headache, fever, dizziness) [18,19]. These reports were limited to symptomology, serology, and gross observations of hepatic disease in both the infant and mother. A larger-scale epidemiological study performed in 2011 was the first to find a significant association between acute RVFV infection in women during pregnancy and late-term spontaneous abortion or stillbirths (odds ratio [OR], 7.4) [20]. Infection of second trimester [21] and full-term [12] human placental explants confirmed susceptibility of human placental villi to RVFV infection. These recent clinical findings highlight the importance of understanding adverse outcomes resulting from RVFV infection during pregnancy. To provide a reliable animal model to study vertical transmission, we developed a rat model of congenital RVF using Sprague Dawley (SD) rats infected with RVFV during late gestation (embryonic day 14; E14) [21]. Vertical transmission of RVFV was observed in the placentas and fetuses of immunocompetent pregnant rats. While some infected dams succumbed to lethal RVF disease, other RVFV-infected dams without observable signs of illness delivered still-borne pups with gross physical abnormalities (i.e., stunted development, necrosis, and fetal hydrops). RVFV infection led to higher pup mortality (2.5x) in surviving dams, and viral RNA could be detected in the placenta as early as 2 days post infection (dpi). This model recapitulates many outcomes observed during natural ruminant infection [5,6] and provides an important tool to study congenital RVF and screen vaccines for teratogenic effects. Despite abortion storms being a distinguishing outcome during RVF outbreaks, little is known about the mechanism of vertical transmission and the host immune response to infection during pregnancy. Here, we identify cellular targets of RVFV at the maternal-fetal interface and evaluate the innate immune responses to infection using the SD rat model. Furthermore, we compared the uterus and placentas from RVFV infected dams that either delivered pups with normal physical appearance to those that delivered still-borne pups with apparent physical abnormalities to identify pathologies associated with more severe outcomes and teratogenicity.
Infection of late-gestation SD rats with virulent RVFV can serve as a tractable model of congenital RVF [21]. For the current study, we used this model to identify cellular targets of RVFV within the placentas of infected dams and to determine whether teratogenicity is associated with specific pathologies and/or immune responses to infection. On embryonic day 14 (E14) of a 22-day gestational period, we subcutaneously inoculated timed-pregnant SD rats with wild-type RVFV (strain ZH501) in the hind flank (dose range 75–1.5x105 pfu) (Fig 1A). Inoculation on E14 recapitulates a late-gestation infection because the rat placenta fully forms at approximately E11-12 [22] and serves as a model for the late miscarriages (second or third trimester) observed in women infected with RVFV [20]. Throughout the study, RVFV-infected dams and uninfected (no infection (NI) controls) were monitored for clinical signs of disease and euthanized if criteria were met. Uninfected controls were euthanized at 5dpi (E19; n = 3) or 6dpi (E20; n = 2). For dams that survived infection, pup delivery occurred at E22 and term placentas were collected, if available. The study concluded when surviving dams were euthanized at 18 or 22dpi.
Rift Valley fever in adult non-pregnant SD rats is generally associated with extensive infection, severe hemorrhaging, and necrosis of the liver, leading to fulminant hepatic disease and a fatal outcome in 18/32 (56.2%) of rats. In pregnant rats, we surprisingly found no association between infection dose and survival (S1A Fig). Regardless of inoculation dose, RVFV infection resulted in around half (57%) of the pregnant dams succumbing to disease with clinical signs (hypothermia, ataxia, labored breathing) requiring euthanasia between 2-6dpi (S1A Fig). Fetuses from dams that were euthanized at 6dpi (E20) had gross signs of teratogenicity, such as fetal hydrops, growth restriction and hemorrhage. For dams that were euthanized from 2-5dpi (prior to 6 dpi), when the pups were E16-E19, physical abnormalities were not observed. It is unclear whether gross physical changes do not appear until later in gestation, only after longer exposures to the virus, or whether gross signs of teratogenicity are simply unable to be distinguished by the naked eye because the fetuses are too small and/or underdeveloped prior to E20. Dams that succumbed to infection between 2-6dpi developed viremia and high levels of viral RNA within the uterus and ovaries (Fig 1B). Exceptionally high levels of viral RNA (avg. 1.8 x 108 pfu/mL equivalent) were detected in placentas collected from dams at E20 or E22 (Fig 1C). Of the approximately 43% of dams that survived infection after receiving doses spanning between 75 and 1.5 x 105 pfu, none displayed clinical signs of illness. Despite this, 21% of the surviving dams without clinical signs of disease delivered deceased pups. In dams that survived to the end of the study (18–22 dpi), some residual viral RNA was still detectable in the uterus and ovaries in a few (3 of 15 and 1 of 15, respectively) of the rats (Fig 1B). The uterus and placenta of infected dams displayed considerable signs of pathology and RVFV viral staining, based on H&E and RNA in situ hybridization (ISH) (S1B and S1C Fig). The ovaries did not show signs of pathology nor evidence of viral infection via ISH despite detection of vRNA by RT-PCR.
We performed ISH to identify cell types and structures within the placenta that are infected by RVFV upon vertical transmission (Fig 2A). The rat placenta is a fetal-derived organ that is divided into two main structures, the labyrinth zone and basal zone. The labyrinth and basal zones consist of specialized trophoblast cells. The labyrinth zone contains intertwined maternal and fetal blood that is separated by cyto- and syncytiotrophoblasts. The labyrinth zone supplies the primary means of nutrient and oxygen exchange between the mother and fetus. The basal zone secretes important pregnancy regulating hormones and consists of spongiotrophoblasts, trophoblast giant cells and glycogen giant cells. At the maternal-fetal interface, the maternal-derived decidua is a specialized structure of the uterus and site of embryo implantation. The decidua, basal zone, and labyrinth zone are the main layers of the placenta and the primary maternal-fetal interface. These layers are the focus of our studies below. The yolk sac is a gestational membrane that provides supplementary means of nutrient and oxygen to the fetus. Broadly speaking, RVFV RNA was detected in yolk sac epithelial cells (Fig 2B, panel i) in addition to stromal cells of the decidua and trophoblast giant cells of the basal zone (Fig 2B, panel ii). RVFV vRNA was also detected in the cytotrophoblasts surrounding the maternal vasculature (Fig 2B, panel iii). Trophoblast infection within the labyrinth zone appears to be diffuse, targeting both cyto- and syncytiotrophoblasts (Fig 2B, panel iv). Immunofluorescent staining for RVFV glycoprotein Gn resembles the staining observed via ISH analyses (S2B Fig). Placentas from uninfected dams were negative for RVFV vRNA (S2A Fig) and Gn (S2C Fig). Thus, we were able to detect RVFV infection in the 3 main layers of the rat placenta (decidua, basal zone, and labyrinth zone).
Placentas collected from E16-E20 were evaluated to identify the placenta structures targeted by RVFV infection. RNA ISH analyses showed RVFV vRNA in the decidua, basal zone, and labyrinth zone as early as 2dpi (E16; Fig 3). The most substantial vRNA staining in all 3 regions was found at 6 dpi (E20). To identify placenta pathologies associated with RVFV vertical transmission in SD rats, we analyzed H&E-stained placenta tissue sections from infected and uninfected dams at different stages of infection (Fig 4). Overall, among all placenta examined, RVFV infection was associated with significant levels of hemorrhage and necrosis, both of which were detected as early as 3 dpi (E17), compared to placenta from uninfected control animals. Hemorrhage severity increased over time (Fig 4A and 4B). When comparing the 3 main placental sections (decidua, basal zone, and labyrinth zone), hemorrhage was detected in all sections, with the labyrinth zone having the most abundant signs of congestion (Fig 4C). Approximately 90% of the RVFV infected placenta sections collected from E16-E20 contained hemorrhage in the labyrinth zone (Fig 4C). Cellular necrosis, on the other hand, occurred most frequently in the maternal decidua (98%), followed by the basal and labyrinth zones (47% and 10%, respectively; Fig 4C). In the decidua, decidualized uterine epithelial cells showed signs of necrosis, while trophoblast giant cells were the primary necrotic cell type in the basal zone (Fig 4A). Overall, the placenta displayed minimal signs of inflammation, with leukocytes present primarily in the decidua (27%) (Fig 4C). Inflammation occurred much less frequently in the basal zone (2%) and was not observed in the labyrinth zone.
To determine whether viral burden and certain pathologies within the placenta contribute to teratogenicity, we directly compared the placentas (E19-E22) from infected dams that delivered pups with normal physical appearance (herein “normal”) and those that delivered pups with visible signs of teratogenicity (i.e. fetal hydrops, smaller size, hemorrhage; herein “teratogenic”). Teratogenicity was associated with more severe (score >2) and diffuse vRNA staining of the myometrium/decidua and basal zone, compared to placentas derived from pups with normal appearance (Fig 5A and 5B). Viral staining within the labyrinth zone was not significantly different between placentas from normal and teratogenic pups (Fig 5B). Both normal and teratogenic cohorts had significant levels of necrosis and hemorrhage compared to placentas from uninfected controls (Fig 6A and 6B). In contrast, inflammation was rarely detected in any placentas collected from E18-E22 regardless of the presence of teratogenicity. Surprisingly, there were no differences in inflammation, necrosis, nor hemorrhage pathology scores between teratogenic and normal pups (Fig 6B). However, a major difference between teratogenic and normal placentas was that hemorrhage was more diffuse across multiple placental layers from teratogenic pups (57.2%), whereas hemorrhage was limited to the labyrinth zone in placentas from pups with normal appearance (7.5%; Fig 6C). Conversely, necrosis was found in multiple placental layers in placentas from normal pups (47.5%) while necrosis was more limited to the decidua in placenta from teratogenic pups (14.3%; Fig 6C).
To better understand the inflammatory responses during RVFV infection in pregnant rats, cytokine and chemokine gene and protein expression were evaluated in the placentas of infected and uninfected dams collected at E20-E22. Infected placentas were further divided to identify differences between placentas derived from pups with normal physical appearance and those from pups with observable signs of teratogenicity. Overall, RVFV infection was associated with increased levels of IFNλ3 mRNA in infected teratogenic placentas compared to uninfected controls (Fig 7A). Differences in IFNλ3 mRNA expression was not found between normal and teratogenic placentas from infected dams. IFNβ mRNA was slightly elevated in placentas from teratogenic pups compared to placentas from normal pups and uninfected controls (Fig 7A). When further comparing the protein levels of inflammatory mediators in infected placentas from either normal or teratogenic pups, IL1α, MCP-1/CCL2, MIP-1 α/CCL3, RANTES/CCL5, and GRO/KC/CXCL1 were significantly elevated in the placentas from teratogenic pups but not in placentas from pups with normal appearance, compared to uninfected controls (Fig 7B). Furthermore, the same cytokines and chemokines were significantly elevated in teratogenic placentas compared to normal placentas. For example, RANTES/CCL5 was approximately 10x higher in teratogenic placentas than infected placentas from pups with normal appearance. IL-18 protein expression was also elevated (p = 0.145 & 0.113, respectively) in teratogenic placentas compared to infected but normal pup placentas (Fig 7B). Despite high levels of chemokines found within the placenta, we saw minimal to no inflammation in histology sections (Fig 6).
Besides the placenta, TORCH (toxoplasmosis, other, rubella, cytomegalovirus, and herpes virus) pathogens can also cause detrimental effects to the maternal uterus. Here, we examined uterine tissue and found that the uterus of dams that succumbed to infection between E16-20 had mild-to-moderate levels of inflammation (p<0.001) compared to uninfected controls (Fig 8A), whereas similar levels of hemorrhage was observed between the groups. Necrosis was not detected in the tissue sections collected from either cohort. In line with the higher levels of inflammation, GRO/KC/CXCL1 and MCP-1/CCL2 were found at higher levels in the uterus of RVFV infected dams compared to uninfected controls (Fig 8B). Inflammation remained significantly elevated in the uterus of post-partum RVFV infected dams that were euthanized at 18-22dpi compared to uninfected controls (p < 0.05; S3A Fig). Uninfected and infected dams had similar levels of hemorrhage in their uterus which could be due to the natural delivery process. In post-partum dams, only RANTES/CCL5 was elevated in the uteruses from infected dams that delivered pups with normal physical appearance compared to uninfected controls (S3B Fig). No differences in cytokine, chemokine, and growth factor expression were noted between cohorts that delivered pups with normal physical appearance and those that delivered pups with signs of teratogenicity.
Our findings demonstrate that RVFV infects the majority of maternal and fetal structures and cell types within the rat placenta, highlighting the highly pan-tropic nature of this virus. This is the first study to identify the cellular targets of RVFV in a rodent model of congenital RVF. Mice infected with RVFV by intrauterine injections 3–4 days prior to term (approx. 21 days) had been utilized once prior [23]; vertical transmission occurred, but gross-physical abnormalities and still-births were not observed in this model. Histological analyses were not performed on the embryos and placentas were not collected. Based on our results, RVFV targets similar cell types of the placenta in rodents as humans and ruminants. These findings further support the use of this model to understand the course of vertical transmission and teratogenicity of RVFV in naturally infected species. For instance, ex vivo infection of second trimester [21] and full-term human placentas [12] showed infection of cytotrophoblasts and syncytiotrophoblasts of chorionic villi, which correlates with infection of similar cells in the labyrinth zone (cytotrophoblasts and syncytiotrophoblasts) and basal zone (giant cells) of rats. Immortalized human trophoblast cell lines (A3 and Jar), are also permissive to RVFV infection [24]. Despite general similarities in cellular constituents, there are distinct differences in the structure of placentas in rats and humans which should be considered when using rats as a model to study human disease. From a broad perspective, humans and rodents have similar placenta structures given they both have discoid hemochorial placentas [25], however the number of cells separating the maternal and fetal blood are different between species. Humans have a hemomonochorial placenta, meaning there is only one layer of syncytiotrophoblasts separating blood, whereas rodents have a hemotrichorial placenta which have two layers of cytotrophoblasts and a single layer of syncytiotrophoblasts separating maternal and fetal blood. At the maternal-fetal interface, the human placenta forms branch like structures consisting of villous trophoblasts and invasive extravillous trophoblasts located at the end of the branches that anchor the placenta to the uterine wall [26]. This branched structure is bathed in maternal blood, providing quick nutrient exchange. Rats, on the other hand, have a labyrinthine structure consisting of intertwined maternal blood and fetal vasculature [26]. Rats also have highly invasive cells, trophoblast giant cells, that embed themselves into the uterine tissue and line the junctional zone between the placenta and uterus [27,28]. These structural differences, and others not discussed, could affect pathogenic outcomes between species and limit the use as rats as a surrogate for human or ruminant disease [29,30]. Comparable placenta cell-types are also targeted by RVFV in sheep and rats. In placentas collected from sheep infected in 2010 during a natural outbreak in South Africa, RVFV antigen staining was predominantly found in the fetal villus trophoblasts, and infection of multinucleated syncytiotrophoblasts residing in the maternal caruncle, the sheep uterus, was present [14]. Infection of endothelial cells within the vasculature of the chorioallantoic membrane and sheep uterus also occurred. Another recent study by Oymans [12] has provided additional insight into the cellular targets of RVFV. Recombinant RVFV strain 35/74 inoculated in pregnant sheep at one-third gestation (embryonic day 55; E55) or mid-gestation (E78) underwent vertical transmission to the placenta and fetus as early as 4 dpi for both cohorts. RVFV appeared to establish infection within the sheep placenta by two methods: 1) by direct infection of fetal trophoblasts in the hemophagous zone, a region of the placenta where the maternal blood is in direct contact with the fetal cells, or 2) by first infecting maternal epithelium within the villi prior to spreading to the fetal trophoblasts. Due to the diffuse labyrinthine nature of placenta infection in our rats, we were unable to deduce the route of established infection within the placenta. Cells at all points of entry (i.e., decidual cells, trophoblast giant cells of the basal zone, endothelial cells and cyto- and syncytiotrophoblasts of the labyrinth zone) are permissive to infection, thus it is likely that multiple routes of invasion into the placenta could occur in rats as well. Future studies looking at placentas collected as early as 12hpi could shed light as to which cells are first targeted by RVFV. The ruminant placenta has a vastly different structure than human placentas [31,32]. Ruminants have cotyledonary placentas consisting of mini-placentomes spanning the fetal membrane. Their synepitheliochorial cellular structure provides additional separation between maternal and fetal blood, which is a key difference in nutrient exchange between humans, rodents, and ruminants. Ruminant and rats contain trophoblast giant cells that aid in uterine implantation [32]. Structural differences and instances of conserved cellularity within placentas should be considered when using rodents as a model for ruminant disease. Despite the outlined structural differences, pathology within the rat placenta found here displayed similarities to what is observed in ruminants. In our studies, necrosis was seen in the proximal regions of the maternal-fetal interface, most abundantly in the decidua and to a lesser extent, the basal zone. Necrosis was elevated even at early stages of infection. In the study by Oymans [12], necrosis occurred primarily in the maternal villus epithelium of RVFV infected sheep. Minimal necrosis was observed in fetal trophoblasts and endothelial cells showed no signs of necrosis despite infection. Odendaal [14], however, saw evidence of endothelial necrosis in naturally infected sheep. The main pathological outcomes observed in RVFV-infected rat placentas were necrosis (primarily in decidua) and hemorrhage (primarily in labyrinth zone). There was surprisingly little inflammation in any placental structure. Zika virus (ZIKV) infection of pregnant IFNAR1 knockout mice via footpad injection can result in vertical transmission involving severe necrosis of the decidua and basal zone [33,34]; however, decidual necrosis in ZIKV-infected rodents and pregnant women [35] is not associated with adverse fetal outcomes such as death or microcephaly. Vertical transmission of human immunodeficiency virus in placentas from 1st and 2nd trimester abortions is also correlated with necrosis of the decidua [36]. Chronic lesions such as decidual necrosis is one of the key pathological findings associated with placenta abruption [37,38], which is a premature detachment of the placenta from the uterus that can increase the risk of fetal death [39,40]. In addition, vertical transmission of cytomegalovirus (CMV) was 4x more likely to occur in mothers with decidua abruption [41]. Decidua necrosis and premature detachment could be an etiologic factor of teratogenicity and fetal death in the SD rat model of congenital RVF but requires more detailed study. Severe hemorrhage within the placentas of RVFV infected dams should also be considered as a major contributor to fetal death. In pregnant SD rats, severe hemorrhaging always occurred in the labyrinth zone of infected placentas; however teratogenic placentas were more likely to experience diffuse hemorrhage, appearing within two or more layers, including the labyrinth zone. Hemorrhage also occurs in placentas from ZIKV infected mice [42], whereas placental hemorrhage is not associated with CMV infections [43]. Extensive placental hemorrhage occurs in sheep experimentally [11,12] or naturally [14] infected with RVFV. In sheep, hemorrhages occur next to the chorioallantoic villi of the sheep placentome and the uterine wall. Significant neutrophil influx was also observed in the lamina propria of the uterus, next to the maternal villus. Hemorrhaging of the placenta can occur due to a breach in endothelial cell structural integrity by virus-induced endothelial damage or over-abundant vascular permeability. Alterations in vascular permeability might account for hemorrhage seen in our model. The lack of inflammatory cells in infected rat placentas is puzzling because we detected elevated levels of cytokines and chemokines. Cytokine and chemokines can enhance cellular inflammation into the infected region. The lack of inflammation, perhaps, may be explained by the kinetics of cytokine and chemokine expression which proceeds the influx of immune cells. Since expression levels were only analyzed during later stages of gestation (E20-E22), it is feasible that our analyses preceded inflammation. Infection earlier in gestation may result in detectible inflammation at later stages of gestation. The lack of inflammation in the placenta may have resulted in exacerbated viral replication and virus-induced pathologies that could have otherwise been controlled by the innate and early adaptive cellular responses. Inflammation at the maternal-fetal interface however can be a double-edged sword. Inflammation of placental tissue can be a major issue because influx of immune cells can result in leaky vasculature leading to hemorrhage or entry of maternal effector cells into the immune privileged fetal tissue. In fact, increased protein levels of RANTES/CCL5 and VEGF receptor (VEGF-R) have been detected in human placentas with ZIKV infection [35] and their expression are associated with vascular permeability. RANTES/CCL5 expression was 4-5-fold higher in human placentas from babies with microencephaly, compared to ZIKV infected placentas from without microencephaly [35]. RANTES/CCL5 was significantly elevated in RVFV infected placentas and teratogenic placentas had approximately 10-fold more protein than infected placentas from pups with normal physical appearance. Looking a step further, blockage of RANTES/CCL5 responses can inhibit the spread of ZIKV in human brain microvascular endothelial cells [44]. RANTES/CCL5 might have a role in vertical transmission of RVFV to the fetal brains observed in our previous study [21]. At a protein level, we did not see an increase in VEGF expression, a regulator of vascular permeability, upon RVFV infection. VEGF receptor (VEGF-R) expression was not analyzed. Elevations in this receptor might also enhance vascular permeability and lead to extensive hemorrhage. A generalized pro-inflammatory response was detected in RVFV-infected rat placentas, including elevations in IL1ɑ, IL-18, type I interferon (IFNβ) and chemokines such as MCP-1/CCL2 and RANTES/CCL5. Teratogenic placentas accounted for these increases along with other chemokines associated with acute-viral infections, such as MIP-1α/CCL3 and Gro/KC/CXCL1 [45]. Inhibition of type I interferon signaling in pregnant mice infected with ZIKV has shown that interferon signaling promotes fetal demise [46]. Another study showed that the immune responses to ZIKV infection alone could cause fetal demise and adverse outcomes [47]. Recurrent miscarriages have been attributed to both low [48] and high [49] levels of IL-18 expression in women. Considering IL-18 is a pyrogen, identifying whether pyroptosis contributes to the pathologies observed upon vertical transmission of RVFV to the placenta should be evaluated. ZIKV can induce pyroptosis in JEG-3 placenta trophoblasts in vitro [50]. Considering inflammatory cells were present in the rat uterus and decidua, and less in the basal and labyrinth zones, the increase in pro-inflammatory mediators and chemokines could be mainly attributed to expression in the maternal tissue at the maternal-fetal interface. This could also explain why most of the cell death and necrosis was observed in the decidua of the rats in this study. CCL5 expression was primarily elevated in the decidua and Hofbauer cells of ZIKV infected placentas [35]. Flow cytometric analyses or single-cell RNA sequencing may pinpoint which cells are responsible for these elevated cytokines and chemokines. Future studies performing serial euthanasia could provide a more complete picture of the cellular inflammation and immune response present in the uterus and placenta from infection to delivery. This study is limited by the fact that the uterus was not collected upon delivery of the pups (E20-E22) when the placenta was collected, therefore direct comparisons in cytokine expression between post-partum uterus and E20-E22 placentas is unable to be performed. Although the uterus and placenta showed signs of infection, neither pathology nor viral staining was seen in the ovaries of infected dams that succumbed to infection, despite high levels of vRNA detected by qPCR. Considering other teratogenic arboviruses, such as ZIKV, have been shown to infect the ovaries and other regions of the female reproductive systems in mice [51] and non-human primates [52], RVFV infection of the ovaries should not be ruled out and warrant further analyses. Overall, we have identified important structures and cells in the placenta targeted by RVFV. Increased pro-inflammatory cytokine and chemokine expression was associated with more severe fetal outcomes such as teratogenicity. We have thus far only touched the surface of our understanding of RVFV infection and associated immune responses to infection of the placenta. To fully understand the impact of RVF on miscarriage rates in pregnant women, large scale population studies evaluating the effect of RVFV infection during pregnancy at various gestations is needed. Additionally, post-partum evaluation of human placenta will provide key information needed to understand the mechanism of RVFV vertical transmission and further confirmations of the relevance of our rodent model.
All animal work described here was carried out in strict accordance with the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health and the Animal Welfare Act. The protocol was approved and overseen by the University of Pittsburgh Institutional Animal Care and Use Committee. The Association for Assessment and Accreditation of Laboratory Animal Care has fully accredited the University of Pittsburgh.
Work with infectious RVFV (strain ZH501) was performed at biosafety level 3 (BSL-3) in the University of Pittsburgh Regional Biocontainment Laboratory (RBL). Personnel wore powered air-purifying respirators (Versaflo TR-300, 3M) for respiratory protection. All active work was performed in class II biological safety cabinets. Animals were housed in individually ventilated microisolator cages (Allentown Inc.) All waste and surfaces were disinfected with Vesphene IIse (1:128 dilution; Steris Corporation). All tissues or samples designated for removal from BSL-3 for downstream processing were inactivated using methods described below; all inactivation methods were verified and approved by a University of Pittsburgh biosafety oversight committee. The University of Pittsburgh RBL is a registered entity with the Centers for Disease Control and Prevention and the U.S. Department of Agriculture for work with RVFV.
Vero E6 cells (CRL-1568, ATCC) were grown in DMEM (Dulbecco’s Modified Eagle Medium: Corning) supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine and 1% (v/v) penicillin/streptomycin (pen-strep). Cells were maintained in a humid incubator at 37°C at 5% CO2.
Virulent RVFV (strain ZH501) was derived from reverse genetics plasmids [53] provided by B. Miller (CDC, Ft. Collins, CO) and S. Nichol (CDC, Atlanta). RVFV was propagated on Vero E6 cells using standard methods. Viral titer was determined by standard viral plaque assay (VPA). Prior to animal infections, stock virus was diluted in D2 (DMEM, 2% fetal bovine serum (FBS), 1% L-glutamine, 1% pen-strep) to the preferred concentration. To account for components within the D2 media, all uninfected control cohorts were mock infected with D2 media.
Six-to-eight-week-old timed-pregnant SD rats were obtained from Envigo Laboratories. A positive copulation plug verified pregnancy. Animals arrived at our facility at embryonic day 12 (E12) and housed individually in temperature-controlled rooms with a 12-hour day/12-hour night light schedule. Food (IsoPro Rodent 3000) and water were provided ad libitum. Animals acclimated to their new surroundings for 48 hours prior to infection. At E14 the SD rats were anesthetized by inhalation of isoflurane (IsoThesia, Henry Schein) then implanted with programmable temperature transponders (IPTT-300, Bio Medic Data Systems) subcutaneously between the shoulder blades. Following implantation, the rats were injected subcutaneously (s.c.) in the hind flank with 200uL of RVFV at the following titers: 1.5x105 pfu (n = 3), 2.6x104 pfu (n = 6), 1.4x103 pfu (n = 11), 1.8x102 pfu (n = 6) and 75 pfu (n = 7). D2 media was delivered to age- and gestation-matched animals as uninfected (no infection, NI) controls (n = 5). Weight and body temperature were documented daily starting the day of infection. The SD rats were monitored twice daily for development of clinical signs of disease. Euthanasia criteria was determined by the following parameters in adherence with IACUC guidelines. Rats were euthanized if they received a combined score of 10 or above or if they received a score of a 4 in any of the criteria outlined in Table 1. Upon RVFV infection the most prominent clinical signs of disease were hypothermia (<34.0°C), behavioral changes, and changes in appearance. Neurological signs were rare. Uninfected controls were euthanized at 5dpi (E19; n = 3) or 6dpi (E20; n = 2). The rats delivered their pups at embryonic day 22 (E22), at which time available placentas were collected. At 18- or 22-dpi dams were euthanized. Animals that met euthanasia criteria prior to this time were immediately anesthetized with isoflurane and euthanized by cardiac puncture, followed by perfusion using phosphate buffered saline (PBS). During necropsy, the following tissues or body fluids were collected from each dam: brain, liver, uterus, ovary, placenta, amniotic fluid. Serum was collected from each dam prior to perfusion. Half of each tissue was either frozen immediately for downstream molecular analyses or fixed in 4% PFA for histology.
Half of each tissue was weighed and homogenized in D2 media (v/v) using an Omni tissue homogenizer (Omni International), then stored at -80°C until downstream analyses were performed. Tissue homogenates were used to quantitate infectious virus by viral plaque assay (VPA) as described previously [21]. For quantitation of RVFV-specific viral RNA (vRNA) by semi-quantitative real-time polymerase chain reaction (RT-PCR), 100uL of each tissue homogenate or liquid sample (blood, amniotic fluid) was inactivated in 900uL of Tri-Reagent (Invitrogen) for 10 minutes prior to removal from the BSL-3 facility as per approved inactivation protocols. Subsequent storage at -80°C or immediate RNA isolation and RT-PCR analyses were performed in a BSL-2 setting using the parameters stated in McMillen, Arora (21). The other half of each dam tissue was submerged in 4% PFA for 24 hours for fixation and virus inactivation. Fresh 4% PFA was added prior to the removal of fixed tissues from the BSL-3 laboratory. Fixed tissues were delivered to a BSL-2 setting and stored in PBS prior to embedding in paraffin and cut onto slides, using standard methods, for hematoxylin and eosin (H&E) and in situ hybridization (ISH) analyses. For immunofluorescence (IF) staining, fixed tissues were cryopreserved using standard methods.
For detection of cytokines (IFNα, IFNβ, IFNλ3, IL-10, IL-1β) and chemokines (MCP-I /CCL2), total RNA was first converted to cDNA using the M-MLV reverse transcriptase (Invitrogen) following the manufacturer’s protocol, including the use of random primers (Invitrogen) and RNAseOUT recombinant ribonuclease inhibitor (Invitrogen). Next, semi-quantitative RT-PCR was performed using Taqman Multiplex Master Mix (2x; Applied Biosystems) and Taqman Gene Expression Assay kits (Invitrogen; S1 Table) following the manufacturer’s instructions. Endogenous controls for normalization comprised of β-actin, while corresponding uninfected, gestation-matched tissues served as reference tissue. The thermal cycling parameters included a hold step at 95°C for 20 seconds, then a cycling PCR amplification step including a 95°C hold for 1 second, a 60°C hold for 20 seconds that was repeated 40x.
Cytokines, chemokines, and growth factor protein expression was quantified using the Bio-Plex Pro Rat Cytokine 23-plex assay (Bio-Rad Laboratories, Inc). Analytes included in the assay were: G-CSF, GM-CSF, GRO/KC/CXCL1, IFN-ɣ, IL-1α, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-10, IL-12 (p70), IL-13, IL-17A, IL-18, M-CSF, MCP-1/CCL2, MIP-1α/CCL3, MIP-3α/CCL20, RANTES/CCL5, TNF-α, and VEGF. Tissue homogenates diluted 1:4 and technical replicates were run in duplicates. Samples were run and analyzed following the manufacturer’s instructions using the BioPlex 200 and HFT Systems (Bio-Rad Laboratories, Inc. Concentrations were calculated based on the provided standard curve; a 5-parametric fitted curve was calculated using the Bioplex Manager (Version 6.2, Build 175) analysis software. Analytes not included in graphs were either below the limit of detection of the assay or no significant changes were noted.
For pathology scoring, slides with 5μm tissue sections were deparaffinized using an alcohol rehydration series and then stained following standard H&E staining procedures. For colorimetric ISH, fixed slides were deparaffinized with an alcohol rehydration series, then boiled in 10mM citric acid buffer (pH 6.0) to unmask antigen-binding epitopes. Tissue sections were permeabilized using 0.1% Triton X-100 detergent in PBS at room temperature, followed by a protease inhibitor treatment. RNAscope 2.5HD Assay Red or RNAscope 2.5HD Assay Brown detection kits were used in accordance with the manufacturer’s instructions for ovary, uterus and placenta or liver sections, respectively (Advanced Cell Diagnostics, Inc (ACD)). The ISH probe, RNAscope 2.5 LS Probe- V-RVFV-ZH501-NP (ACD), targeted the nucleoprotein (NP) viral RNA. Slides were counterstained with hematoxylin and a coverslip was mounted with Permount (Fisher Chemical). For IF imaging, cryo-sectioned slides were rehydrated with PBS containing 0.5% bovine serum albumin (BSA), then blocked with 5% donkey serum in PBS. Tissues were then probed with the following antibodies, in-house custom rabbit anti-RVFV nucleoprotein polyclonal antibody (Genscript) and anti-pan cytokeratin typeI/II anti-cytokeratin polyclonal antibody (Invitrogen; MA5-13156). Secondary antibody staining used fluorescently labeled Cy3- and Alexa488- anti-rabbit IgG. Sections were counter stained with DAPI, then a coverslip was mounted with gelvatol.
H&E and colorimetric ISH slides were imaged using an Olympus CX41 microscope with the Levenhuk M300 base attachment. Immunofluorescence slides were imaged using the Nikon A1 confocal microscope provided by the University of Pittsburgh Center for Biological Imaging. Images were taken at 20x magnification. Denoising, contrasting and pseudo coloring were formed using the open-source image editor, Fiji with ImageJ [54].
H&E slides were blind scored by a licensed pathologist specializing in placental pathology. Scoring was based on severity of hemorrhage, inflammation, or necrosis on a scale of 0–4 (Table 2) [55,56]: Colorimetric ISH slides were blind scored in-house with a scale of 0–3 using the following parameters: 0 = no staining, 1 = 1–30% staining, 2 = 30–60% staining, and 3 = >60% staining within each placenta section, decidua/myometrium, basal zone, or labyrinth zone. The average of three individuals’ scores was used as the final score of each placenta section.
Two-way ANOVA with multiple comparisons were performed using Graphpad Prism 8.0.
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PMC9648854 | 36315609 | Drifa Belhadi,Majda El Baied,Guillaume Mulier,Denis Malvy,France Mentré,Cédric Laouénan | The number of cases, mortality and treatments of viral hemorrhagic fevers: A systematic review | 31-10-2022 | Background Viral hemorrhagic fevers (VHFs) are a group of diseases, which can be endemo-epidemic in some areas of the world. Most of them are characterized by outbreaks, which occur irregularly and are hard to predict. Innovative medical countermeasures are to be evaluated but due to the field specificities of emerging VHF, challenges arise when implementing clinical studies. To assess the state of the art around VHFs, we conducted a systematic review for all reports and clinical studies that included specific results on number of cases, mortality and treatment of VHFs. Methods The search was conducted in January 2020 based on PRISMA guidelines (PROSPERO CRD42020167306). We searched reports on the WHO and CDC websites, and publications in three international databases (MEDLINE, Embase and CENTRAL). Following the study selection process, qualitative and quantitative data were extracted from each included study. A narrative synthesis approach by each VHF was used. Descriptive statistics were conducted including world maps of cases number and case fatality rates (CFR); summary tables by VHF, country, time period and treatment studies. Results We identified 141 WHO/CDC reports and 126 articles meeting the inclusion criteria. Most of the studies were published after 2010 (n = 97 for WHO/CDC reports and n = 93 for publications) and reported number of cases and/or CFRs (n = 141 WHO/CDC reports and n = 88 publications). Results varied greatly depending on the outbreak or cluster and across countries within each VHF. A total of 90 studies focused on Ebola virus disease (EVD). EVD outbreaks were reported in Africa, where Sierra Leone (14,124 cases; CFR = 28%) and Liberia (10,678 cases; CFR = 45%) reported the highest cases numbers, mainly due to the 2014–2016 western Africa outbreak. Crimean-Congo hemorrhagic fever (CCHF) outbreaks were reported from 31 studies in Africa, Asia and Europe, where Turkey reported the highest cases number (6,538 cases; CFR = 5%) and Afghanistan the last outbreak in 2016/18 (293 cases; CFR = 43%). Regarding the 38 studies reporting results on treatments, most of them were non-randomized studies (mainly retrospective or non-randomized comparative studies), and only 10 studies were randomized controlled trials. For several VHFs, no specific investigational therapeutic option with strong proof of effectiveness on mortality was identified. Conclusion We observed that number of cases and CFR varied greatly across VHFs as well as across countries within each VHF. The number of studies on VHF treatments was very limited with very few randomized trials and no strong proof of effectiveness of treatment against most of the VHFs. Therefore, there is a high need of methodologically strong clinical trials conducted in the context of VHF. | The number of cases, mortality and treatments of viral hemorrhagic fevers: A systematic review
Viral hemorrhagic fevers (VHFs) are a group of diseases, which can be endemo-epidemic in some areas of the world. Most of them are characterized by outbreaks, which occur irregularly and are hard to predict. Innovative medical countermeasures are to be evaluated but due to the field specificities of emerging VHF, challenges arise when implementing clinical studies. To assess the state of the art around VHFs, we conducted a systematic review for all reports and clinical studies that included specific results on number of cases, mortality and treatment of VHFs.
The search was conducted in January 2020 based on PRISMA guidelines (PROSPERO CRD42020167306). We searched reports on the WHO and CDC websites, and publications in three international databases (MEDLINE, Embase and CENTRAL). Following the study selection process, qualitative and quantitative data were extracted from each included study. A narrative synthesis approach by each VHF was used. Descriptive statistics were conducted including world maps of cases number and case fatality rates (CFR); summary tables by VHF, country, time period and treatment studies.
We identified 141 WHO/CDC reports and 126 articles meeting the inclusion criteria. Most of the studies were published after 2010 (n = 97 for WHO/CDC reports and n = 93 for publications) and reported number of cases and/or CFRs (n = 141 WHO/CDC reports and n = 88 publications). Results varied greatly depending on the outbreak or cluster and across countries within each VHF. A total of 90 studies focused on Ebola virus disease (EVD). EVD outbreaks were reported in Africa, where Sierra Leone (14,124 cases; CFR = 28%) and Liberia (10,678 cases; CFR = 45%) reported the highest cases numbers, mainly due to the 2014–2016 western Africa outbreak. Crimean-Congo hemorrhagic fever (CCHF) outbreaks were reported from 31 studies in Africa, Asia and Europe, where Turkey reported the highest cases number (6,538 cases; CFR = 5%) and Afghanistan the last outbreak in 2016/18 (293 cases; CFR = 43%). Regarding the 38 studies reporting results on treatments, most of them were non-randomized studies (mainly retrospective or non-randomized comparative studies), and only 10 studies were randomized controlled trials. For several VHFs, no specific investigational therapeutic option with strong proof of effectiveness on mortality was identified.
We observed that number of cases and CFR varied greatly across VHFs as well as across countries within each VHF. The number of studies on VHF treatments was very limited with very few randomized trials and no strong proof of effectiveness of treatment against most of the VHFs. Therefore, there is a high need of methodologically strong clinical trials conducted in the context of VHF.
Viral hemorrhagic fevers (VHFs) are a group of febrile illnesses caused by four families of RNA viruses: arenaviridae, filoviridae, bunyaviridae and flaviviridae. [1] These highly infectious viruses are mainly zoonotic; meaning they naturally exist in animal or insect populations. [2] When a person encounter an infected animal or insect, the virus can spread through spillover into the human population, and subsequently is transmitted from person-to-person through contact with blood or other body fluids. Whatever their capacity to drive paramount hemorrhagic manifestations, many VHFs can cause severe, life-threatening disease. The agents that are causative of VHF are often classified as Biosafety Level 4 (BSL-4) pathogens that require special laboratory facilities with the highest level of safety measures. [2,3] VHFs are distributed worldwide and are often associated with high morbidity and mortality. Most of them are characterized by clusters or even outbreaks occurring irregularly and almost resulting from spillover or more recently from human reservoirs constituted by immunologically preserved sanctuaries where the virus may persist after recovery of conversant survivors. Patient outcomes are highly associated with the timing of curative treatment with improved outcomes when the specific or supportive therapy is started early. [4,5] Hence, VHF care remains essentially supportive and some VHFs are treated only with basic medical care that is not always reaching the optimized level of standards aimed to prevent or control the multi-systemic disorders that account for bad outcome. [2,5,6] Available VHF drugs are limited and clinical data on the efficacy of VHFs drugs is restricted. [4,5] New investigational treatments need to be evaluated but due to the field specificities of emerging VHF, difficulties arise when conducting clinical studies. Indeed, hard to predict outbreak duration leads to limited number of recruited patients. Moreover high case fatality rate (CFR) leads to reluctance to use methodologically strong trial design such as randomized controlled trials as part of the patients will not receive the potentially beneficial treatment. [7] VHFs have recently caused various outbreaks around the world. To assess the state of the art around VHFs, we systematically reviewed the World Health Organization (WHO) and Centers for Disease Control (CDC) websites and published literature for all reports and clinical studies that included specific results on number of cases, mortality and treatments of VHFs. We decided to focus mostly on VHFs caused by a selection of arenaviridae, filoviridae, bunyaviridae and flaviviridae and did not look at some other important VHF conditions such as the severe dengue and yellow fever.
The systematic review was registered on the International prospective register of systematic reviews (PROSPERO 2020 CRD 42020167306). The objective of the systematic review was to review the case fatality rates, number of cases and treatment options of VHFs. The initial protocol also included the review of sequelaes, which will not be presented here.
The PICOS (Participants, Intervention, Comparison, Outcomes and Study types) framework was used to identify relevant data.
Humans infected with a pathogen causative of a VHF from the following list: [8] Alkhurma hemorrhagic fever (AHF) Argentine hemorrhagic fever (ArHF) Bolivian hemorrhagic fever (BHF) Chapare hemorrhagic fever (CHF) Crimean-Congo hemorrhagic fever (CCHF) Ebola virus disease (EVD) Hantavirus pulmonary syndrome (HPS) Hemorrhagic fever with renal syndrome (HFRS) Kyasanur Forest disease (KFD) Lassa fever (LF) Lujo hemorrhagic fever (LHF) Lymphocytic choriomeningitis (LCM) Marburg virus disease (MVD) Omsk hemorrhagic fever (OHF) Rift Valley fever (RVF) Sabia-associated hemorrhagic fever (SHF) Tick-borne encephalitis (from a hemorrhagic variant) Venezuelan hemorrhagic fever (VeHF)
This review did not focus on any specific intervention.
Any studies reporting number of cases and/or CFR and/or mortality rates associated with treatments of a VHF were included.
Official information from WHO/CDC and national health websites, cross-sectional, cohort/case-control studies, descriptive reports and clinical trials were included.
Included studies were limited to studies published in English or French. The following types of study were deemed ineligible; case reports, case series, systematic reviews and meta-analyses.
The systematic review consisted in two parts. The first search was conducted in December 2019 on the WHO and CDC websites to identify the number of cases and deaths associated with each disease by year and by country. The second search consisted in a systematic literature search of bibliographic databases based on the PRISMA guideline. Relevant studies were identified by searching MEDLINE, Embase and the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library. We searched the electronic databases until January 21st, 2020. Search terms were developed using a combination of MeSH/EMTREE terms and free-text terms to capture the relevant populations, outcomes and study types (cf. S1 Fig in Supplementary Material). Reference lists of included studies were not reviewed. Additional hand searches were performed on national health websites.
For the systematic literature search, after removing duplicates, two researchers (DB and GM) independently evaluated all identified citations based on titles and/or abstracts. In case of disagreement, a third researcher (CL) served as tiebreaker. Full-text publications of studies included based on title and abstract were retrieved and reviewed by two researchers (DB and ME) to assess eligibility based on the inclusion/exclusion criteria. A third researcher (CL) served as a tiebreaker for any discordant decisions. Justification for study exclusion was documented.
Information identified from the WHO and CDC websites were extracted for each disease by period and by country. For the systematic literature search, following the study selection process, qualitative and quantitative data were extracted from each of the included articles. Numbers were extracted as reported in each study. If the CFR was not reported in a study, the number of cases and the number of deaths were used, if available, to estimate the missing CFR.
Quality assessment was performed using the Newcastle-Ottawa Quality Assessment Form [9] to evaluate cohort studies, and the Risk of Bias (RoB) tool described in the Cochrane Handbook for Systematic Reviews of Interventions [10] to evaluate randomized clinical trials.
A narrative synthesis approach by disease was used for each outcome. Descriptive statistics were conducted to describe the published articles and WHO/CDC reports. World map of the total number of cases and CFR were produced and summary tables of the number of cases and CFR by VHF, country and time period were also produced. Summary tables reporting the characteristics and results of each study on VHF treatments were produced. World maps were produced using R software version 3.6.0.
A total of 57 reports were identified from the WHO website, and 96 reports from the CDC website. After removing duplicates, 141 reports were extracted. After removing duplicates, 4 461 publications were identified from Medline, Embase, CENTRAL and through hand searches. We excluded 4 153 publications based on the screening of titles and abstracts. A total of 308 publications were included for full text review. Finally, 126 publications met the inclusion criteria and were extracted. The selection process and the numbers at each stage are shown in Fig 1. The exhaustive lists of included publications and WHO and CDC reports are reported in S1 and S2 Tables in Supplementary Material.
The characteristics of the included studies are summarized in Table 1. Most of the studies were published after 2010 (69%, n = 97 for WHO and CDC reports and 74%, n = 93 for publications) and were conducted mainly in Africa (57%, n = 81 for WHO and CDC reports and 48%, n = 60 for publications). The majority of studies reporting number of cases and/or CFR were descriptive reports (100%, n = 141 for WHO and CDC reports and 90%, n = 79 for publications) followed by retrospective studies (10%, n = 9 for publications). Regarding studies reporting results on treatments, 32% (n = 12) of them were retrospective studies, followed by 26% (n = 10) of randomized controlled trial and 16% (n = 6) of non-randomized comparative studies. Most of WHO and CDC reports focused on EVD (33%, n = 47), followed by HPS (18%, n = 26), LF (17%, n = 24) and RVF (16%, n = 23). In terms of publications, most of them focused also on EVD (33%, n = 43), followed by CCHF (21%, n = 27), HFRS (14%, n = 18) and HPS (10%, n = 13). No relevant data were identified for KFD, LCM and tick-borne encephalitis (from a hemorrhagic variant).
On the 38 studies reporting results on treatments, 10 were randomized trials and 28 were non-randomized. Regarding the randomized comparative studies, only two were classified as having a “low risk of bias”, both published after 2010, and the other eight studies were classified as having “some concerns” (cf. S3 Table). Regarding the non-randomized studies, 13 were classified as “poor quality”, 5 as “fair quality” and 10 as “good quality” (cf. S4 Table). On those 28 non-randomized studies, 6 were published before 2010 and 67% of them (n = 4/6) were classified as “poor quality”, compared with 41% (n = 9/22) for those published after 2010. The 88 studies reporting number of cases and CFR were evaluated using the Newcastle-Ottawa Quality Assessment Form: 59 were classified as “poor quality” and 29 as “fair quality” (cf. S5 Table). On these 88 studies, 31 were published before 2010 and a similar proportion than those published after 2010 were classified as “poor quality” (65%, n = 20/31, for studies before 2010, compared with 68%, n = 45/66, for studies published after 2010).
The worldwide distribution of VHFs by country is reported in Fig 2. We identified studies reporting outbreaks of at least one VHF across 55 countries. We found studies on 4 different VHFs in South Africa: CCHF, RVF, MVD and LHF. Based on identified studies, a total of 16 countries were associated with 2 VHFs, mainly in Africa with 9 countries, followed by America with 3 countries and Asia and Europe with 2 countries each. More detailed results are reported below for the VHFs with at least 5 studies. They are presented by alphabetical order.
The distribution of CCHF cases and CRFs are reported in Fig 3. Cases were reported in Africa, Asia and Europe in the following countries: Afghanistan, Bulgaria, Georgia, Iran, Iraq, Kazakhstan, Kosovo, Mauritania, Oman, Pakistan, South Africa, Tajikistan, Turkey and Uzbekistan. The first documented cases were reported in 1948–1969 in Kazakhstan (89 cases, CFR = 25%). At the date of the review, the country with the highest reported number of cases was Turkey with a total of 6,538 cases (CFR = 5%) and the last documented cases reported in 2016–2018 in Afghanistan (293 cases, CFR = 43%). More details by country and period are reported in S6 Table.
The distribution of EVD cases and CRFs are reported in Fig 4. Outbreaks were reported in sub-saharan Africa in the following countries: Democratic Republic of the Congo, Gabon, Republic of Guinea, Liberia, Mali, Nigeria, Sierra Leone, Sudan and Uganda. The first reported outbreak took place in 1976 in the Democratic Republic of the Congo (318 cases, CFR = 88%) and Sudan (284 cases, CFR = 53%). At the date of the review, the countries with the highest reported number of cases were Sierra Leone with a total of 14,124 cases (CFR = 28%) and Liberia with 10,678 cases (CFR = 45%), due to the 2014–2016 western Africa outbreak. The reported CFRs since 2010 varied greatly across countries (cf. S7 Table). In total, 28% in Sierra Leone in 2014–2016 (14,124 cases), 40% in Nigeria in 2014 (20 cases), 41% in Uganda in 2012 (17 cases), 45% in Liberia (10,678 cases), 66% in the Democratic Republic of the Congo in 2018 (3,470 cases), 67% in Guinea in 2014–2016 (3,811 cases) and 75% in Mali in 2014 (8 cases).
The distribution of HPS cases and CRFs are reported in Fig 5. Cases were reported in America in the following countries: Argentina, Brazil, Chile, Panama, Paraguay and USA. The first documented cases were reported in 1993 in the USA (48 cases, CFR = 56%) and Brazil (884 cases from 1993 to 2006, CFR = 39%). At the date of the review, the countries with the highest documented number of cases were Brazil with 2,370 cases (CFR = 39%) and the last documented cases were reported in 2018 in USA (3 cases, CFR = 67%). More details by country and period are reported in S8 Table.
HFRS cases were reported in Asia and Europe in the following countries: China, Croatia, Finland, Belgium, France, Germany, Netherlands, Luxembourg, South Korea, Montenegro and Russia. The first documented cases were reported in 1931–1941 in China (10,000 cases, CFR = 30%). At the date of the review, the country with the highest reported number of cases was China with 1,306,812 cases (CFR = 3%) and the last documented cases reported in 2000–2017 in Russia (131,590 cases, CFR = 0.4%). More details by country and period are reported in S9 Table.
The distribution of LF cases and CRFs are reported in Fig 6. Cases were reported in western Africa in the following countries: Benin, Liberia, Nigeria, Sierra Leone and Guinea. We identified a study which reported the first outbreak in 1996–1999 in Guinea (22 cases, CFR = 18%). At the date of the review, the country with the highest reported number of cases was Nigeria with 2,287 cases (CFR = 23%) and the last documented cases reported in 2019 in South-West Nigeria (554 cases, CFR = 22%). More details by country and period are reported in S10 Table.
MVD cases were reported in Africa and Europe in the following countries: Angola, Democratic Republic of the Congo, Germany, ex-Yugoslavia, South Africa, Kenya and Uganda. The first documented cases were reported in 1967 in Germany (29 cases, CFR = 24%) and ex-Yugoslavia (2 cases, CFR = 0%) and relied to lab.-accidental transmission in settings that were used to import monkeys from Central Africa. At the date of the review, the country with the highest reported number of cases was Angola with 374 cases (CFR = 88%) and the last documented cases reported in 2017 in Uganda (2 cases, CFR = 100%). More details by country and period are reported in S11 Table.
The distribution of RVF cases and CRFs are reported in Fig 7. Cases were reported in Africa and Asia in the following countries: Egypt, Kenya, Madagascar, Mauritania, Mayotte (France), Mozambique, Niger, Saudi Arabia, Somalia, South Africa, Sudan, Tanzania and Yemen. The first documented cases were reported in 1977–1978 in Egypt (18,000 cases, CFR = 3%). At the date of the review, the country with the highest documented number of cases was Egypt with 18,148 cases (CFR = 3%) and the last documented cases reported in 2018–2019 in Mayotte (129 cases, CFR not reported). More details by country and period are reported in S12 Table.
The remaining VHFs reported cases in one country each: Saudi Arabia for AHF (335 cases, CFR = 2%), Argentina for ArHF (981 cases, CFR not reported), Bolivia for BHF (690 cases, CFR = 23%), South Africa for LHF (5 cases, CFR = 80%), Russia for OHF (1144 cases, CFR = 14%), Brazil for SHF (4 cases, CFR = 2%) and Venezuela for VeHF (728 cases, CFR = 23%). More details by VHF and period are reported in S13 Table.
Publications evaluating the efficacy of specific treatments on mortality were identified for Argentine hemorrhagic fever, Crimean-Congo hemorrhagic fever, Ebola Virus Disease, Hantavirus Pulmonary Syndrome, Hemorrhagic fever with renal syndrome and Lassa fever (cf. Tables 2 and 3). More details on each investigational treatment are reported in S14 Table. Detailed results are reported below for the VHFs with at least one study. They are presented by alphabetical order.
One study on treatment was identified for ArHF: a randomized controlled trial published in 1979 assessing intravenous immune plasma obtained from convalescent donors.[11] This trial showed a significantly decrease in mortality when immune plasma is given before the ninth day of the disease (CFR = 1.1%) compared with normal plasma obtained from donors without a history of ArHF (CFR = 16.5%).
Nine studies on treatments were identified for CCHF: two randomized controlled trials, two non-randomized comparative studies, two case-control studies, two retrospective and/or prospective studies and one descriptive report. Most of the trials assessed oral or intravenous ribavirin, one trial assessed ribavirin +/- corticosteroids and one trial assessed immune globulins + ribavirin. Ribavirin was associated with inconsistent results across studies. Two studies reported significant results of ribavirin and ribavirin +/- corticosteroids on mortality. However, those two studies had a relatively weak design (a non-randomized comparative study [19] and a prospective and retrospective study [13]). In the randomized trial comparing ribavirin versus standard therapy alone [18], ribavirin was associated with no positive effect on mortality (CFR = 6.3% versus 5.6%). The other randomized trial was a small study (40 patients, [20]) assessing polyvalent immune globulins + ribavirin which showed with no positive effect of the treatment on mortality compared with ribavirin alone (CFR = 25% versus 11%). Therefore, no strong proof of effectiveness of specific treatment against CCHF was identified.
Seventeen studies on treatments were identified for EVD: two randomized controlled trials, two non-randomized comparative studies, four single-arm trials, eight retrospective studies and one descriptive report. Among these studies, four reported positive results on mortality. The two first studies showed that early vitamin A supplementation or IFN-beta 1a therapy may be associated with reduced mortality compared with no vitamin A supplementation or historical control (Relative risk reduction of mortality with vitamin A supplementation within 48h = 0.77 [0.59 to 0.99]; 21-day survival based on Kaplan Meier for IFN-beta 1a therapy versus historical control: 67% versus 19%). However, those results are to be taken with caution based on the weak design of the studies (retrospective study [22,23] and single-arm trial [33]). The third study was a retrospective study assessing favipiravir [25] and reported that the treatment was associated with prolonged survival compared with standard therapy alone (CFR = 44% versus 65%). However, this result was challenged by another retrospective study (adjusted odds ratio = 0.48 [0.20 to 1.01]) [38] and a single-arm trial (CFR = 54% versus 55%), [26] which reported no significant results of favipiravir on mortality. The last positive study was a randomized controlled trial assessing three treatments, the antiviral remdesivir, and the antibody-based therapies Mab114 and REGN-EB3 against ZMapp. [37] The study showed that both MAb114 and REGN-EB3 were superior to ZMapp (difference between MAb114 and ZMapp = -14.6% [-27.2 to -1.7]; difference between REGN-EB3 and ZMapp = -17.8% [-28.9 to -2.9]; in reducing mortality from EVD (with stringent findings among patients presenting with high levels of viral load).
Five studies on treatments were identified for HPS: two randomized controlled trials, two non-randomized comparative studies, and one retrospective study. Only two studies reported weakly significant results. The first study was a retrospective study evaluating the impact on survival of extracorporeal membrane oxygenation (ECMO) support in patients with HPS refractory to medical treatment and the associated complications. [42] This study reported a lower mortality in patients who had elective insertion of vascular sheaths and were almost concurrently intubated and placed on ECMO when they decompensated (CFR = 20%) compared with patients intubated when they became hypoxic and placed on ECMO when they became hemodynamically unstable (CFR = 46%). The second study was a non-randomized comparative study, which compared immune plasma infusion versus no treatment. [43] The study reported a weakly significant decrease in mortality associated with immune plasma infusion (Odds ratio = 0.35 [0.12 to 0.99]). Therefore, no strong proof of effectiveness of treatment against HPS was identified.
Three studies on treatments were identified for HFRS: two randomized controlled trials and one retrospective study. Only two studies reported positive results. The first study was a retrospective study comparing Renal Replacement Therapy (RRT) versus no RRT [45] and showed that RRT is associated with a decrease in mortality (CFR = 34% versus 70%). However, those results are to be taken with caution based on the weak design of the study. The second study was a randomized controlled trial assessing ribavirin [46] and showed a significant reduction in mortality among patients treated with ribavirin (CFR = 2% versus 9%).
Two studies on treatments were identified for LF: a randomized controlled trial and a descriptive report. The first study published in 1986 was a randomized controlled trial and evaluated ribavirin and convalescent plasma compared with no therapy in several subgroups [48]. The study showed that ribavirin was associated with a significantly lower mortality than no therapy (CFR = 21% versus 71%). Moreover, the second study which was a descriptive report published in 2019, [47] also reported that ribavirin was associated with a decrease in mortality.
To our knowledge, this is the first comprehensive systematic review to summarize all published information available on worldwide cases numbers, mortality and treatments of a range of VHFs, excluding severe dengue and yellow fever. Only a few number VHF systematic reviews were previously published and focused on single VHF, mainly EVD, CCHF and LF. Some meta-analyses were also conducted but often associated with heterogeneity issues. A previous meta-analysis on EVD found a pooled CFR of 60% in Africa [49]. However, this result was associated with a very high level of heterogeneity. This is consistent with our findings, which showed that CFR varied greatly across countries in outbreaks since 2010 ranging from 28% (2014–2016 outbreak in Sierra Leone) to 75% (2014 in Mali). In terms of specific treatments, a recent systematic review [50] focused mainly on the randomized controlled trial assessing remdesivir, Mab114 and REGN-EB3 against ZMapp. [37] The authors concluded as well that both MAb114 and REGN-EB3 were superior to ZMapp in reducing mortality from EVD with differences depending on the viral load at baseline. Regarding CCHF, we identified in our review no strong proof of effectiveness of treatment. A previous meta-analysis on the efficacy of ribavirin in CCHF patients showed that ribavirin decreased the mortality rate compared with patients not treated with ribavirin.[51] However, this meta-analysis included an important number of low-quality studies such as case series. Therefore, the results should be considered with caution. Regarding Lassa fever, a previous meta-analysis on the efficacy of ribavirin showed that ribavirin was associated with lower risk of death than patients not treated with ribavirin.[52] However, heterogeneity was identified across studies and the results are mainly based on retrospective studies. Our systematic reviews also has some limitations. One limitation of our review on the number of cases and CFR identified is that our findings are based on numbers registered on the WHO and CDC websites or published numbers, which can underestimate the reality. We also decided, when available, to prioritize laboratory confirmed cases numbers. Moreover, we restricted our review of the grey literature to national health websites and references reported on the WHO and CDC websites; data on clinical trials registries (e.g. ClinicalTrials.gov) were not included here. It is also important to mention that we did not stratify our results according to species or the strain of the virus. For example, in the case of Ebola disease investigational therapeutic options, the trials of importance were conducted during the 2014–2016 western Africa outbreak and the 2018–2020 North-Kivu (Democratic Republic of the Congo) outbreak that were related to Ebola virus (species Zaire Ebolavirus) and Makona and Kikwit specific strain respectively.[36,37] With respect to treatment of other ebolavirus diseases (e.g. Soudan and Bundibugyo virus diseases), options are even more limited. Notably, the three monoclonal antibody treatments tested in the PALM trial [37], ZMapp, REGN-EB3, and MAb114, have a narrow spectrum and are ineffective against other filovirus infections. In the case of other VHFs such as HPS or LF, the sparse comparative trials assessed mainly a nucleoside inhibitor (i.e., ribavirin). No strong proof of effectiveness of ribavirin was identified for HPS. Regarding LF, the efficacy of ribavirin was not considered as specific to Lassa virus lineage. Besides, considerable uncertainty was recently even more raised about its activity as an anti-infectious agent in the management of the condition.[52–54] Another limitation of our review is that we focused our results on the mortality of the VHF. However, the mortality does not account for transmissibility of the virus, contagiousness and immune escape. Mortality rates varied also greatly across countries, especially in Africa, which can be explained by the fact that treatment facilities in some places in Africa may be limited. The study context also has an impact on the results of our review. Some authors report the challenge they can face of studying a relatively rare disease that affect widely dispersed rural areas.[41] Regarding VHFs such as EVD, authors reported difficulties during the course of their study, with for example interruption of participating centers due to violence from local community or paramilitary groups who can be suspicious of the activities in those facilities.[37] Moreover, a large number of included studies on treatment evaluation in our review were associated with a high risk of bias. This highlights the need to conduct clinical trials with a methodologically strong design. This is of most importance to adapt the methodology of clinical trials in the specific context of VHFs. A recent study explored the application of Bayesian Decision Analysis (BDA) in order to incorporate the burden of disease and disease context into clinical trials, especially for the deadliest diseases in the US, such as cancers or liver cirrhosis. [55,56] This framework allows taking into account the disease context when determining the sample size and critical value of a fixed-sample test. Therefore, an interesting next step of our review would be to use these results to conduct a BDA to evaluate the optimal sample sizes and type I errors for future VHF clinical trials.
We observed that number of cases and mortality varied greatly across VHFs as well as across countries within each VHF. The number of studies on VHFs treatments was very limited with very few randomized trials and no strong proof of effectiveness of treatment against most of the VHFs. Therefore, there is a high need of methodologically strong clinical trials conducted in the context of VHF.
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PMC9648868 | Qin Liu,Qi Su,Fen Zhang,Hein M. Tun,Joyce Wing Yan Mak,Grace Chung-Yan Lui,Susanna So Shan Ng,Jessica Y. L. Ching,Amy Li,Wenqi Lu,Chenyu Liu,Chun Pan Cheung,David S. C. Hui,Paul K. S. Chan,Francis Ka Leung Chan,Siew C. Ng | Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome | 10-11-2022 | Clinical microbiology,Gastroenterology,Medical research | Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19. | Multi-kingdom gut microbiota analyses define COVID-19 severity and post-acute COVID-19 syndrome
Our knowledge of the role of the gut microbiome in acute coronavirus disease 2019 (COVID-19) and post-acute COVID-19 is rapidly increasing, whereas little is known regarding the contribution of multi-kingdom microbiota and host-microbial interactions to COVID-19 severity and consequences. Herein, we perform an integrated analysis using 296 fecal metagenomes, 79 fecal metabolomics, viral load in 1378 respiratory tract samples, and clinical features of 133 COVID-19 patients prospectively followed for up to 6 months. Metagenomic-based clustering identifies two robust ecological clusters (hereafter referred to as Clusters 1 and 2), of which Cluster 1 is significantly associated with severe COVID-19 and the development of post-acute COVID-19 syndrome. Significant differences between clusters could be explained by both multi-kingdom ecological drivers (bacteria, fungi, and viruses) and host factors with a good predictive value and an area under the curve (AUC) of 0.98. A model combining host and microbial factors could predict the duration of respiratory viral shedding with 82.1% accuracy (error ± 3 days). These results highlight the potential utility of host phenotype and multi-kingdom microbiota profiling as a prognostic tool for patients with COVID-19.
The coronavirus disease-2019 (COVID-19) pandemic has affected over 500 million people and killed 6 million people worldwide. Identifying predictors of disease severity and deterioration is a priority to guide clinicians and policymakers for better clinical management, resource allocation, and long-term management of COVID-19 patients. Several lines of evidence, such as replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human enterocytes, detection of viruses in fecal samples, and altered gut microbiota composition, including the increased abundance of opportunistic pathogens and reduced abundance of beneficial symbionts in the gut of patients with COVID-19 suggest involvements of the gastrointestinal (GI) tract. Recent studies have shown that gut dysbiosis is linked to the severity of COVID-19 and persistent complications months after disease resolution. Patients with severe disease exhibit elevated plasma concentrations of inflammatory cytokines and markers, including interleukin-6 (IL-6), IL-8, and IL-10, lactate dehydrogenase (LDH), and C-reactive protein (CRP), reflecting immune responses and tissue damages after SARS-CoV-2 infection. Among hospitalized COVID-19 patients, gut microbiota composition is also associated with blood inflammatory markers, and the lack of short-chain fatty acids and L-isoleucine biosynthesis in the gut microbiome are correlated with disease severity. In addition to bacteria, the human gut is home to a vast number of viruses and fungi that regulate host homeostasis, physiological processes, and the assembly of co-residing gut bacteria, which could potentially play an important role in the pathophysiological mechanisms that determine COVID-19 outcomes. Since the therapeutic potential for COVID-19 patients includes approaches to inhibit, activate, or modulate immune function, it is essential to define these characteristics related to clinical features in a well-defined patient cohort. We hypothesized that microbial interaction networks may improve our understanding of the pathophysiology and long-term consequences of COVID-19. Here, using an unsupervised classification approach based on fecal metagenomic profiling and blood inflammatory markers, we demonstrated that integrative microbiomes from a multi-kingdom network provide a novel framework for understanding disease complications and have potential applications in risk stratification and prognostication of COVID-19 cases.
We included 133 hospitalized patients with COVID-19 in three hospitals in Hong Kong between 13 March 2020 and 27 January 2021. We assessed viral RNA levels in nasopharyngeal swabs and fecal samples using reverse transcription quantitative real-time PCR (RT-qPCR). We also assessed plasma cytokine and chemokine levels and leukocyte profiles in freshly isolated peripheral blood mononuclear cells (PBMCs). We also analyzed the gut microbiome composition (bacteria, viruses, and fungi) in 296 serial fecal samples collected at up to three longitudinal time-points from admission to six months after virus clearance using shotgun metagenomic sequencing and assessed the metabolomics of 79 fecal samples at admission (Figs. 1, 2A). In total, 296 stool samples were sequenced, generating an average of 6.9 Gbp per sample. The gut multi-biome (bacteria, fungi, and viruses) profile at admission was integrated using an unsupervised weighted similarity network fusion (WSNF) approach. Weighting was assigned according to the total number of observed taxa present in a particular biome, with filtering based on a prevalence of at least 5% across the patient cohort; virome (732 species) > bacteriome (242 species) > mycobiome (12 species) observed across 133 patients. By subjecting multi-biome data to this non-supervised similarity network fusion approach, fecal samples were divided into two distinct patient clusters based on the microbiota matrix: 47.4% of patients in WSNF-Cluster 1 (n = 63), and 52.6% (n = 70) in WSNF-Cluster 2 (Fig. 2B). We next compared microbial profiles between clusters (adjusted for age, gender and comorbidity). The multi-biome composition of patients in Cluster 1 was characterized by a predominance of bacteria (Ruminococcus gnavus, Klebsiella quasipneumoniae), fungi (Aspergillus flavus, Candida glabrata, Candida albicans), and viruses (Mycobacterium phage MyraDee, Pseudomonas virus Pf1) (Fig. 2C, MaAsLin2, q < 0.1, Supplementary Data 1). They also exhibited significantly lower multi-biome diversity (Wilcoxon test, p = 0.029, Supplementary Fig. 1A) than those in Cluster 2. Principal Coordinates Analysis (PCoA) of multi-biome composition revealed a significant difference between the two clusters using permutational multivariate analysis of variance (PERMANOVA) (p < 0.001, Supplementary Fig. 1B and Supplementary Data 2). We found that patients belonging to Cluster 1 exhibited more symptoms such as diarrhea and chills (twofold increased risk), fever, and cough (1.3-fold increased risk; Chi-square, p value < 0.001, q < 0.1) than those in Cluster 2 at admission (Fig. 2D). They were also characterized by a higher viral load (Fig. 2E), greater disease severity (Fig. 2F), increased CRP levels (Fig. 2G), elevated C–X–C motif chemokine 10 (CXCL10) (Fig. 2H), longer duration of viral positivity in upper respiratory tract samples (Supplementary Fig. 2A) and a higher rate of viral positivity in fecal samples (Supplementary Fig. 2B) than those in Cluster 2. We also tested the viral load in fecal samples and found no significant differences between the two clusters (Supplementary Fig. 2C). Demographics and comorbidities were comparable between Cluster 1 and Cluster 2, except that patients within Cluster 1 were 9.2 years older than those in Cluster 2 (Table 1). Patients in Cluster 1 primarily comprised subjects with severe COVID-19 who exhibited more clinical signs (Fig. 2F, D) and these subjects presented with higher plasma CRP and chemokine levels, including CXCL10, which is known to be involved in leukocyte trafficking. These observations indicate that gut multi-biome profiles of COVID-19 patients at admission are associated with disease severity, and Cluster 1 was defined as representing patients with more severe disease. We explored the functional profiling of microbiome signatures in the two clusters and identified cluster-specific functional signatures (Supplementary Fig. 1C and Supplementary Data 3). For functional annotation, we used the Human Microbiome Project Unified Metabolic Analysis Network 3 (HUMAnN3) pipeline, which maps reads to functionally annotated organism genomes and uses a translated search to align unmapped reads to UniRef90 protein clusters. Amongst all microbiome functionalities, urea cycle, l-isoleucine degradation I, and l-arginine degradation II were enriched in Cluster 1 (Supplementary Fig. 1C, q < 0.1, fold change >2). Elevated blood urea nitrogen (BUN) levels have been reported to be associated with critical illness and mortality in COVID-19 patients and are predictive of poor clinical outcomes. We found that blood urea levels were strongly associated with the microbiome urea cycle pathway and were higher in COVID-19 patients with severe disease (Supplementary Figs. 1D, E, 3A). Next, we investigated how specific microbiome species were associated with elevated BUN levels in patients with severe COVID-19. The relative abundances of the urea cycle pathway and K01940 in the urea cycle were significantly higher in Cluster 1. Furthermore, we found a marked increase in K01940 (argininosuccinate synthase, the key enzyme in the urea cycle pathway, Supplementary Fig. 3B) in the severe cluster (Supplementary Fig. 3C), which was predominantly driven by Klebsiella species such as Klebsiella quasipneumonia, Klebsiella pneumoniae, and Klebsiella variicola (Supplementary Fig. 3D), by comparing subclass pathways and microbial contributors (quantifying gene presence and abundance in a species-stratified manner). High urea level is commonly an indication of kidney dysfunction. However, in our cohort, there was no significant difference in other blood markers of liver and kidney functions (total protein, alkaline phosphatase (ALP), alanine transaminase (ALT), creatinine, Supplementary Data 4, 5), except blood urea. Given the signatures that correlate with disease deterioration, gut-derived uremic toxins in the systemic circulation might be one of the explanations for the marked increase in urea in severe COVID-19 patients. Enriched l-isoleucine degradation I and l-arginine degradation II, and decreased l-isoleucine biosynthesis IV, as well as pyruvate fermentation to acetate and lactate II, were further verified by metabolomics sequencing and correlation analysis (Supplementary Fig. 4).
An exaggerated immune system response, cell damage, or physiological consequences of COVID-19 may contribute to the persistent and prolonged effects after acute COVID-19, known as post-acute COVID-19 syndrome (PACS). The exact pathophysiological mechanisms underlying PACS remain unclear. By following the gut microbiome dynamics of patients with COVID-19 from admission until six months after viral clearance, we explored microbiome composition (bacteria, viruses, and fungi) at admission and the association with the development of PACS. Although older age was recognized in Cluster 1, there were no significant differences in the age of patients with PACS after six months between the two clusters. For α-diversity based on the Shannon index, we found higher values at 3 months than in baseline samples, but there was no significant increase in the diversity of the microbiota at 6 months (Supplementary Fig. 5A, B). Within Cluster 1 and Cluster 2, there was no significant difference in the gut microbiome composition at admission and follow-up samples at 3 and 6 months (Supplementary Fig. 5C, D, p > 0.05) within each cluster, suggesting that the gut microbiome profile was stable over time. We further assessed whether there were temporal changes in patients without PACS in Cluster 2. The multi-microbiome exhibited stable microbiome profiles from baseline to as long as 6 months of follow-up (Supplementary Fig. 5E, 5F), indicating the persistent impact of SARS-COV-2 infection on the gut microbiome. After 6 months, patients in Cluster 1 exhibited significantly different gut microbiota compositions than those in Cluster 2 (Fig. 3A). The bacteria diversity in Cluster 1 was significantly lower than that in Cluster 2 (Fig. 3A, p = 0.0061). Cluster 1 was characterized by an increase in opportunistic pathogenic bacterial species, including Erysipelatoclostridium ramosum, Clostridium bolteae, and Clostridium innocuum at 6 months (adjusted for age, gender, and comorbidities, Fig. 3B). Significantly more patients within Cluster 1 (84 vs. 44%; FDR <0.1, Chi-square test) developed symptoms of PACS, including insomnia (23 vs. 2%; FDR <0.1), anxiety (28 vs. 7%; FDR <0.1) and poor memory (37% vs. 5%; FDR <0.1), compared with those in Cluster 2 (Fig. 3C).
We next incorporated host parameters (patient demographics, blood parameters, and cytokine levels) with the microbiome analysis of baseline samples. Using random forest modeling of both host factors and microbiome signatures and a stratified ten-fold cross-validation (Fig. 4A), this model could differentiate Cluster 1 and Cluster 2 with an area-under receiver operator curve (AUROC) of 0.94 (Fig. 4B and Supplementary Data 6). In contrast, a model that incorporated patient demographics (i.e., age, gender, and comorbidities), blood parameters (CRP and LDH), cytokines (i.e., CXCL10, IL-1b, and IL-10), and microbiome analysis alone achieved an AUC of 0.53, 0.60, 0.61, and 0.84, respectively, in differentiating the two clusters (Supplementary Data 6). Patients in Cluster 1 were characterized by more advanced age, higher LDH levels, a greater relative abundance of Candida albicans and Pseudomonas virus Pf1, and lower relative abundance of Bifidobacterium adolescentis and Faecalibacterium prausnitzii (Fig. 4C–G). We next evaluated the sub-model performance from the top five to the top 20 and found that using the top 11 achieved the best performance based on this model. With further limitation to the top 11 factors on the random forest, our model achieved an AUC of 0.98, differentiating between the two clusters. These 11 factors included host factors (age, viral load, blood LDH, CRP, and CXCL10 levels), bacteria (Bifidobacterium adolescentis, Faecalibacterium prausnitzii, and Blautia wexlerae), fungi (Candida albicans and Aspergillus niger) and virus (Pseudomonas virus Pf1) composition (Supplementary Data 7). These data suggest that a combination of host and microbial factors provides the most accurate discrimination ability for defining subjects with severe COVID-19. To explore whether the integration of clinical data with deep microbiome profiling could predict the duration of viral shedding in COVID-19 patients, we tested 1,378 samples from the upper respiratory tract (sputum and nasopharyngeal samples) for the presence of SARS-CoV-2 virus using RT-qPCR every two days for each patient. The median duration of viral shedding (based on positive RT-qPCR) was 21.1 days (IQR 14.5–24.5, range 4-56) after the onset of initial symptoms. We used a random forest analysis of ensembled datasets (demographics, blood tests, cytokines, and multi-biome) to predict the duration of viral shedding in an individual patient. Using a discovery cohort of 93 patients with COVID-19 followed by a test cohort of 40 patients, our predictive model produced an accuracy of 82.06% with an error of ±3 days in predicting the duration of viral shedding (Fig. 4H). A sparse model consisting of the top ten features was then validated using the validation set (30%, n = 40). The accuracy of using the top ten features was lower than that of using all features for viral shedding duration. The microbiome taxa that contributed the most to the model to determine the duration of viral shedding were from the three kingdom classes: Adlercreutzia equolifaciens, Asaccharobacter celatus, Candida dubliniensis, Klebsiella phage vB KpnP SU50, and Rhizobium phage vB RglS P106B (Supplementary Fig. 6).
We performed a network analysis of the interactions involving bacteriome, mycobiome, and virome to investigate the co-occurrence of multi-biome signatures in patients from the two clusters: Cluster 1 (severe) and Cluster 2 (non-severe). We first conducted a co-occurrence analysis by assessing the sparse compositional matrices approach to generate association networks. Taxa with close evolutionary relationships tended to be positively correlated, while distantly related microorganisms with functional similarities tended to be compete. Herein, a positive interaction of microorganisms was defined by a correlative score representing the co-occurrence of microbes, while a negative value indicates co-exclusion (Sparcc |R| >0.1; p < 0.05). We found that patients in the non-severe cluster had a higher total number of bacteria and a lower number of viruses in the multi-interactome (Supplementary Fig. 7A). Intriguingly, we found an increased number of negative associations among bacteria, viruses, and fungi in the microbiome of a severe cluster (Supplementary Fig. 7A), suggesting a stronger co-occurrence of trans-kingdom patterns in patients with severe disease. We examined the network metrics of node degree, stress centrality, and betweenness centrality (of the nodes) to depict the impact of microbes on network integrity. The top representative taxa were not shared in the non-severe cluster. This observation suggests that the interactome of a microbe, rather than the microbe itself, dictates clinical status, such as the severity of COVID-19. We found more interactions involving bacteria-viruses and fungi-viruses in patients in the severe Cluster 1, including the invasive gut opportunistic pathogen Ruminococcus gnavus, fungi hubs of Candida albicans and Wickerhamomyces ciferrii (reclassified and renamed Pichia ciferrii) (Supplementary Fig. 7C–E). In contrast, the core network in the non-severe cluster included more viruses, including Bifidobacterium phage BigBern1, Streptococcus satellite phage Javan415, and Roseobacter phage DSS3P8 (Supplementary Fig. 7E). The results indicated clear segregation in terms of the patterns of nodes between the severe and non-severe cluster. Taking R. gnavus as an example, it was positively correlated with other constituent microbes in the severe cluster but negatively correlated in the non-severe cluster (Supplementary Fig. 7F). These findings highlight a preferential mechanism for the loss of inhibitory effect of pathogenic microbes in the severe cluster.
Our cross-sectional and prospective multi-omics analyses reveal several new insights into the role of host and microbial factors in COVID-19 severity and long-term complications. First, we identified two robust ecological clusters that defined severe COVID-19 and post-acute COVID-19. Second, these clusters, defined by altered multi-biome composition and impaired microbiome functionalities, were associated with PACS. Lastly, host and microbial factors can predict the duration of respiratory viral shedding. Six host factors and five microbial candidates provided high accuracy, suggesting the prognostic potential of microbial markers for determining COVID-19 outcomes and consequences. Several studies have demonstrated that the gut microbiota composition correlates with the severity of COVID-19 infection and persisted months after disease resolution. The gut bacteriome has led to many discoveries of microbiota linked to disease progression in COVID-19, yet there is considerable untapped potential for non-bacterial microorganisms. Among the 133 patients, 110 were from the Prince of Wales Hospital, 17 from the United Christian Hospital, and 6 from Yan Chai Hospital. Since most (110/130) of the patients were assigned to the same hospital, which is nearest to their geographic location in Hong Kong, bias based on the geographic origins of patients should be limited in this study. There is considerable disease heterogeneity in COVID-19, given the variability in clinical, immunological inflammatory, and human fecal microbiome phenotypes. With the aid of data integration with a similarity network fusion approach for the multi-kingdom microbiome, we identified specific gut microbiome features that were linked to the severity, viral shedding duration, and post-acute complications of COVID-19. Evaluation of our model revealed that a combination of clinical information and gut microbiome data can substantially improve the differentiation capacities of the COVID-19 cohort. Among the microbiome and clinical variables, we found 11 factors, including bacteria, fungi, and viruses, which were significantly associated with cluster patterns and severe status. Using random forest modeling, we observed relationships between the features of the different multi-kingdom ecological constituents and the clinical features of patients with COVID-19. This embedding approach allowed us to connect these integrated multi-kingdom microbiome signatures to the specific clinically measurable features of the disease. Multi-kingdom microbiota analyses provide new and previously unrecognized targets that could be considered as alternatives to, or used in combination with, established regimens for the prognosis of COVID-19. Particularly in the severe cluster, relationships with other kingdoms, such as fungi (Candida glabrata, Candida albicans) and viruses, are novel and previously unrecognized in COVID-19. The uncovered co-exclusion relationship between opportunistic pathogenic microorganisms and other species is particularly interesting, given the association between disease severity and long-term complications. The assessment of key influential taxa of microorganisms in different clusters highlights the relevance of integrative microbiome in the precision microbiome. The more severe cluster was associated with higher levels of Candida albicans and Pseudomonas phages Pf1 and a lower abundance of Bifidobacterium adolescentis. The benefits of targeting influential microbes in an interactome, however, remain unknown and unaddressed in this work, and should be the focus of future studies. Previous studies have reported that blood urea levels, an indication of kidney dysfunction, increase throughout infection. Similarly, we found higher levels of urea in patients in the severe cluster than in those in the non-severe cluster. Moreover, functional microbiome analysis revealed that elevated urea might be explained by gut microbiome–mediated urea nitrogen recycling driven by Klebsiella species such as K. pneumoniae and K. variicola. Patients with severe COVID-19 exhibit abnormal bursts of the urea cycle in gut microbiome communities. We found that the involvement of gut microbes may hasten the accumulation of blood urea in COVID-19 patients. Klebsiella spp. are considered urease-producing and urea-hydrolyzing bacteria, which indicates that Klebsiella spp. can produce urease, an enzyme that catalyzes the hydrolysis of urea, to form ammonia and carbon dioxide. Meanwhile, the enhancement of nitro-recycling may, in turn, cause an increase in serum urea, but the presence of impaired kidney function in COVID-19 patients may also need to be considered. Eliminating pathogens to treat uremic toxins is a novel concept; however, if proven effective, it may have a significant impact on the management of patients with COVID-19. Our study demonstrates an integrative microbiome approach; however, it has some limitations. First, the sample size was small, and our findings should be confirmed in larger cohorts across different populations. Besides, despite timely hospital admission and sample collection, there is also the possibility that patients were admitted at different stages of infection, which might be reflected in their viral load and gut microbiome composition. Despite the accelerated pace of advances in DNA sequencing and computational tools, bioinformatic techniques available for bacteriophage and phage crAss-like phages metagenomic libraries still have several inherent limitations. Reconstitution of the entire viral genome in the gut remains challenging. Future work and alternative approaches to the assessment of viromes, such as RNA sequencing, may yield different results and be more comprehensive, thereby enabling greater weighting of the vital contribution to the overall integrated microbiome, an important area of future exploration given the relatively poorly defined role of gut viruses in COVID-19. Bacteria, fungi, and viruses have been investigated; however, other types of microorganisms, such as archaea and protists, may also have important regulatory roles and require further exploration. Furthermore, although networks were weighted based on species richness and abundance, their true influence on the gut microbiome is not necessarily captured by richness and abundance alone, but rather by a function of functional genes, competition, substrate utilization, and energy flux through the ecosystem traits that cannot be comprehensively assessed by metagenomic sequencing alone. It is also important to test the robustness of the findings using publicly available subsets. An integrated modeling approach could be improved in the future with additional data concerning other immune markers, metabolomic data, and blood biomarkers. Many emerging variants of COVID-19 continue to impose a global burden on healthcare systems. Ascertaining factors underlying differential susceptibility and poor outcomes following viral exposure is critical in improving public health responses and resource allocation via identification of those at high risk for severe disease and post-acute COVID-19 and their coordinated management through dedicated COVID-19 clinics. This study provides a compendium of gut multi-biome, immune response data, and an integrated framework to link gut microbiota to disease outcomes. By integrating patient microbiomes into either of the gut microbiome cluster identified in this study, we can begin to infer risk stratification and personalized management, and how microbiome therapeutic interventions may be most useful in specific patients. Our findings provoke the idea of future gut microbiome-based diagnostics and therapeutics based on an individual’s multi-biome signature and propose applications of multi-omics technologies that could lead to an improved mechanistic understanding of microorganism–host interactions.
Participants were recruited and consented under Research Ethics Committee (REC) no. 2020.076 and all subjects provided informed consent. This is a cross-sectional and prospective cohort study involving 133 patients with a confirmed diagnosis of COVID-19 (defined as a positive RT-PCR test for SARS-CoV-2 in the nasopharyngeal swab, deep throat saliva, sputum, or tracheal aspirate) hospitalized at three regional hospitals (110 from the Prince of Wales Hospital, 9 from the United Christian Hospital and 6 patients from Yan Chai Hospital) in Hong Kong, China between 13 March 2020 and 27 Jan 2021, followed-up to 6 months. Disease severity at admission was defined based on a clinical score of 1 to 5: (1) asymptomatic, individuals who tested positive for SARS-CoV-2 but who had no symptoms consistent with COVID-19. (2) mild, individuals who had any signs of COVID-19 (e.g., fever, cough, sore throat, malaise, headache, and muscle pain) but no radiographic evidence of pneumonia; (3) moderate, if pneumonia was present along with fever and respiratory tract symptoms; (4) severe, if respiratory rate ≥30/min, oxygen saturation ≤93% when breathing ambient air, or PaO2/FiO2 ≤ 300 mm Hg (1 mm Hg = 0.133 kPa); or (5) critical, if there was respiratory failure requiring mechanical ventilation, shock, or organ failure requiring intensive care. We defined post-acute COVID-19 syndrome (PACS) as at least one persistent symptom or long-term complication of SARS-CoV-2 infection beyond 4 weeks from the onset of symptoms which could not be explained by an alternative diagnosis. We assessed the persistence of the 30 most commonly reported post-COVID symptoms at 3 and 6 months after illness onset (Supplementary Data 8). Patients who fulfilled the following criteria were eligible for analyses: (i) 18–70 years of age, (ii) no antibiotic therapy before at least 6 months, during, and 6 months after acute infection of SARS-CoV-2, (iii) no gastrointestinal symptoms during acute infection. Written informed consent was obtained from all patients. Dietary data were documented for all COVID-19 patients during the time of hospitalization (whereby standardized meals were provided by the hospital catering service of each hospital), and individuals with special eating habits, such as vegetarians, were excluded. After discharge, patients with COVID-19 were advised to continue a diverse and standard Chinese diet that was consistent with habitual daily diets consumed by Hong Kong Chinese. Data on the medical history, including age, gender, smoking status, and comorbidities (i.e., hypertension, diabetes mellitus, and hyperlipidemia), were recorded. Laboratory results include liver function tests (total bilirubin, creatine kinase, and LDH), renal function (urea and creatinine), complete blood count (i.e., hemoglobin, red blood cell, lymphocyte, monocyte, platelet, and polynuclear neutrophil), and CRP were collected.
Stool samples were collected at admission from 133 patients and at 3 months and 6 months after discharge (average of three stool samples per subject). Stool samples from in-hospital patients were collected by hospital staff while discharged patients provided stools on the day of follow-up at 3 months and 6 months after discharge or self-sampled at home and had samples couriered to the hospital within 24 h of collection. Baseline (stools collected at admission) samples were the first sample after hospital admission and collected before antibiotic treatment. All samples were collected in tubes containing preservative media (cat. 63700, Norgen Biotek Corp, Ontario, Canada) and stored immediately at −80 °C until processing. We have previously shown that data on gut microbiota composition generated from stools collected using this preservative medium is comparable to data obtained from samples that are immediately stored at −80 °C. The full sample list is summarized in Supplementary Data 9.
Upper respiratory tract samples (pooled nasopharyngeal and throat swabs), lower respiratory tract samples (sputum and tracheal aspirate), and stool samples from 94 participants were collected at admission. We determined SARS-CoV-2 viral loads in these samples, using real-time reverse-transcriptase-polymerase chain-reaction (RT-PCR) assay with primers and probe targeting the N gene of SARS-CoV-2 designed by the US Centers for Disease Control and Prevention.
Whole blood samples collected in anticoagulant-treated tubes were centrifuged at 2000×g for 10 min and the supernatant was collected. Concentrations of cytokines and chemokines were measured using the MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel—Immunology Multiplex Assay (Merck Millipore, Massachusetts, USA) on a Bio-Plex 200 System (Bio-Rad Laboratories, California, USA). The concentration of N-terminal-pro-brain natriuretic peptide (NT-proBNP) was measured using Human NT-proBNP ELISA kits (Abcam, Cambridge, UK). Laboratory results at admission, including blood count test (platelet count, white blood cell count, neutrophil count) and the plasma concentrations of lactate dehydrogenase (LDH), C-reactive protein (CRP), albumin, hemoglobin, alkaline phosphatase, and aspartate aminotransferase, alanine aminotransferase, total bilirubin, and creatinine, were extracted from the electronic medical records in the Hong Kong Hospital Authority clinical management system.
The quantification of fecal metabolites from 79 fecal samples at admission was performed by Metware Biotechnology Co., Ltd. (Wuhan, China). Acetic was detected by GC-MS/MS analysis. Agilent 7890B gas chromatography coupled to a 7000D mass spectrometer with a DB-5MS column (30 m length × 0.25 mm i.d. × 0.25 μm film thickness, J&W Scientific, USA) was used. Helium was used as a carrier gas, at a flow rate of 1.2 mL/min. Injections were made in the splitless mode and the injection volume was 2 μL. The oven temperature was held at 90 °C for 1 min, raised to 100 °C at a rate of 25 °C/min, raised to 150 °C at a rate of 20 °C/min, and held at 150 °C for 0.6 min, further raised to 200 °C at a rate of 25 °C/min, held at 200 °C 0.5 min. After running for 3 min, all samples were analyzed in multiple reaction monitoring modes. The temperature of the injector inlet and transfer line were held at 200 and 230 °C, respectively. l-isoleucine and l-arginine were detected by LC-MS analysis. LC-ESI-MS/MS system (UPLC, ExionLC AD, https://sciex.com.cn/; MS, QTRAP® 6500+ System, https://sciex.com/) was used for analysis. The analytical conditions were as follows, HPLC: column, Waters ACQUITY UPLC HSS T3 C18 (100 mm × 2.1 mm i.d.,1.8 μm); solvent system, water with 0.05% formic acid (A), acetonitrile with 0.05% formic acid (B). The gradient was started at 5% B (0–10 min), increased to 95% B (10–11 min), and ramped back to 5% B (11–14 min); flow rate, 0.35 mL/min; temperature, 40 °C; injection volume: 2 μL. The ESI source operation parameters were as follows: an ion source, turbo spray; source temperature 550 °C; ion spray voltage (IS) 5500 V (Positive), −4500 V (Negative); DP and CE for individual MRM transitions were done with further DP and CE optimization.
Detailed methods for extracting bacterial and fungal DNA are described in ref. 8. Briefly, the fecal pellet was added to 1 mL of CTAB buffer and vortexed for 30 seconds, then the sample was heated at 95 °C for 5 min. After that, the samples were vortexed thoroughly with beads at maximum speed for 15 min. Then, 40 μL of proteinase K and 20 μL of RNase A were added to the sample and the mixture was incubated at 70 °C for 10 min. The supernatant was then obtained by centrifuging at 13,000×g for 5 min and was added to the automated Maxwell RSC machine (Promega, Wisconsin, USA) for DNA extraction. The total viral DNA was extracted from each fecal sample, using TaKaRa MiniBEST Viral RNA/DNA Extraction Kit (Takara, Japan) following the manufacturer’s instructions. Extracted total viral DNA was then purified by the DNA Clean & Concentrator Kits (Zymo Research, CA, USA). After the quality control procedures by Qubit 2.0, agarose gel electrophoresis, and Agilent 2100, extracted DNA was subject to DNA libraries construction, completed through the processes of end repairing, adding A to tails, purification, and PCR amplification, using Nextera DNA Flex Library Preparation kit (Illumina, San Diego, CA). Libraries were subsequently sequenced on our in-house sequencer Illumina NextSeq 550 (150 base pairs paired-end) at the Center for Microbiota Research, The Chinese University of Hong Kong. Raw sequence data generated for this study are available in the Sequence Read Archive under BioProject accession: PRJNA714459.
Raw sequence data were quality filtered using Trimmomatic V.39 to remove the adapter, low-quality sequences (quality score <20), and reads shorter than 50 base pairs. Contaminating human reads were filtering using Kneaddata (V.0.7.2 https://bitbucket.org/biobakery/kneaddata/wiki/Home, Reference database: GRCh38 p12) with default parameters. Following this, microbiota composition profiles (bacteria and fungi) were inferred from quality-filtered forward reads using MetaPhlAn3 version 3.0.5 and MiCoP. Micop has been proven to be more effective for eukaryotes identification in human microbiome data. GNU parallel was used for parallel analysis jobs to accelerate data processing.
Identification of viral sequences in the process of viral metagenomic analysis is notoriously challenging due to the lack of a universal viral marker as opposed to bacterial 16 S rRNA, for example. Thus, reference-based read mapping is limited by a scarcity of annotated viral genomes. We used an optimized pipeline, capable of de novo extraction and retrieval of viral contigs from shotgun sequencing reads. Raw sequence quality was assessed using FASTQC and filtered utilizing Trimmomatic using the following parameters; SLIDINGWINDOW: 4:20, MINLEN: 60 HEADCROP 15; CROP 225. Contaminating human reads were filtering using Kneaddata (Reference database: GRCh38 p12) with default parameters. Megahit, with default parameters, was chosen to assemble the reads into contigs per sample. Assemblies were subsequently pooled and retained if longer than 1 kb. Bacterial contamination was removed by using an extensive set of inclusion criteria to select viral sequences only. Briefly, contigs were required to fulfill one of the following criteria; 1) Categories 1–6 from VirSorter when run with default parameters and Refseqdb (–db) (1) positive, (2) circular, (3) greater than 3 kb with no BLASTn alignments to the NT database (January ‘19) (e-value threshold: 1e-10), (4) a minimum of 2 pVogs with at least 3 per 1 kb, (5) BLASTn alignments to viral RefSeq database (v.89) (e-value threshold: 1e-10), and (6) less than three ribosomal proteins as predicted using the COG database. HMMscan was used to search the pVOGs hmm profile database using predicted protein sequences on VLS with an e-value filter of 1e-5, retaining the top hit in each case. Afterward, a fasta file combining viral contigs was compiled. The redundant sequences were eliminated by CD-HIT-EST provided from CD-HIT 4.8.1. This viral database includes the viral contigs recovered by the screening criteria from the bulk metagenomic assemblies. Then the paired reads were mapped to the viral contig database with BWA, using default parameters. The viral operational taxonomic unit (OTU) table of viral abundance was pulled from BWA sam output files by script, and normalized by the number of metagenomic reads and the OUT sequence length. The contigs were analyzed according to their open reading frames (ORFs). The ORFs on the contigs were predicted using MetaProdigal (Hyatt et al., 2012) (v2.6.3) with the metagenomics procedure (-p meta). To annotate the predicted ORFs, the amino acid sequences of the ORFs were queried by Diamond against the viral RefSeq protein (v84) with an E-value <10−5 and a bitscore >50. The viral Refseq proteins with the top closest homologies (E-value <10−5 and bitscore >50) were considered for each ORF, analogous to a previously reported method.
For each biome dataset, microbes prevalent in at least 5% of patients (that is, n ≥ 7) with an average abundance of 1% were kept for analysis (Detected 737, Kept 242, Removed 495). Integration of bacterial, fungal, and viral community data was performed by weighted SNF (WSNF) using an online tool (https://integrative-microbiomics.ntu.edu.sg). Briefly, the respective weights of each biome are assigned based on the richness of the data, as demonstrated by the number of species present in each biome. Using the merged dataset (bacteria, fungi, and viruses), the tool generates a corresponding patient similarity network using a spectral clustering algorithm with the default settings (Bray–Curtis), outputting the cluster assignments for each patient. The optimal number of clusters (n = 2) was determined by WSNF using the eigengap method and the value of K nearest neighbors, which was set based on the optimal silhouette width.
R package random Forest v4.6–14 was used to develop a stratification model of patients in different clusters. Four datasets from 133 patients, including demographic, blood tests, cytokines, and multibiome were used separately or in combination (ensemble) to train the model for cluster stratification. Machine learning models were first trained on the training set (70%, n = 93) with fivefold cross-validation, and then were applied to the test set (30%, n = 40) for validation. Each time a new feature was added to the model, the performance of the model was re-evaluated using the above training and validation set. This process was repeated ten times to obtain a distribution of random forest prediction evaluations. The training dataset (70%) was used for feature selection. A trained forest produces a variable importance list based on a mean decrease in the Gini index. The feature importance vector (mean decrease Gini index), including weights for every species, demographic, blood test, or cytokines predictive capacity was collected. The final model for stratification was chosen when the best overall AUC value was achieved. For the construction of an optimal prediction model in the ensembled dataset, the importance value of each feature to the stratification model was evaluated by recursive feature elimination first, and then the selected features are added to the model one by one according to the descending importance value. The hyperparameters for the random forest model were ntree = 10,000, Gini index as impurity criterion, and the default square root of the number of features (species in this case) to use for each split in the decision tree.
The random forest regression model was used to regress features from ensembled dataset (demographic, blood test, cytokines, and multibiome) in the time-series profiling of COVID-19 patients against their SARS-CoV-2019 positive time (Upper respiratory tract) using default parameters of R package randomForest v4.6–14 (ntree = 10,000, using default mtry). The dataset was divided into 70% training and 30% testing set. The RF algorithm, due to its non-parametric assumptions, was applied and used to detect both linear and nonlinear relationships between multiple types of features and positive time, thereby identifying features that discriminate different viral persistent duration in COVID-19 patients. The top-ranking important positive duration-discriminatory features required for prediction were identified based on “rfcv” function in the randomForest package. Ranked lists of important features in order of reported feature importance were determined over ten times fivefold of the algorithm on the training set (70%, n = 93). Using the profiles of a multi-microbiome, demographic, blood test, and cytokines, the performance of models was further evaluated with a fivefold cross-validation and repeated ten times to obtain a distribution of random forests prediction evaluations. The final model for regression was chosen when the best overall accuracy was achieved. The predicted positive time was paired with the real positive time for accuracy evaluation, and the accuracy was calculated at different error levels from ±0 to ±5 days.
SparCC was used to identify co-occurrence correlations among bacteria, fungi, and virus from the R package “SpiecEasi v1.1.1” with 20 iterations in the outer loop and 10 iterations in the inner loop. The correlation strength exclusion threshold was 0.1 using the SparCC default setting. Absolute values of correlations below 0.1 are considered zero by the inner SparCC loop, and p value below 0.05 was considered significant. The resulting network was characterized and visualized via Cytoscape (v3.9.1).
Continuous variables of demographic features were expressed in the median (interquartile range), whereas categorical variables (disease severity, five-point scale of severity) were presented as numbers. Qualitative and quantitative differences between subgroups were analyzed using chi‐squared or Fisher’s exact tests for categorical parameters and the Wilcoxon test for continuous parameters, as appropriate. The odds ratio and adjusted odds ratio (aOR) with a 95% confidence interval (CI) were estimated using logistic regression to examine clinical parameters associated with the development of PACS. The site-by-species counts and relative abundance tables were input into R V.3.5.1 for statistical analysis. Principal Coordinates Analysis (PCoA) was used to visualize the clustering of samples based on their species-level compositional profiles. Associations between gut community composition and patients’ parameters were assessed using permutational multivariate analysis of variance (PERMANOVA). Associations of specific microbial species with patient parameters were identified using the linear discriminant analysis effect size (LEfSe) and the multivariate analysis by linear models (MaAsLin2) statistical frameworks implemented in the Huttenhower Lab Galaxy instance (http://huttenhower.sph.harvard.edu/galaxy/). PCoA, PERMANOVA, and Procrustes analysis are implemented in the vegan R package V.2.5–7.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Supplementary information Peer Review File Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Data 6 Supplementary Data 7 Supplementary Data 8 Supplementary Data 9 Reporting Summary |
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PMC9648873 | 36357651 | Qingni Wu,Longxue Li,Yao Jia,Tielong Xu,Xu Zhou | Advances in studies of circulating microRNAs: origination, transportation, and distal target regulation | 10-11-2022 | Circulating microRNA,Plant microRNA,Distal regulation,Gastrointestinal absorption,Post-translational regulation | In the past few years, numerous advances emerged in terms of circulating microRNA(miRNA) regulating gene expression by circulating blood to the distal tissues and cells. This article reviewed and summarized the process of circulating miRNAs entering the circulating system to exert gene regulation, especially exogenous miRNAs (such as plant miRNAs), from the perspective of the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence of distal regulation to gene expression, thereby providing a basis for their in-depth research and even application prospects. Graphical Abstract | Advances in studies of circulating microRNAs: origination, transportation, and distal target regulation
In the past few years, numerous advances emerged in terms of circulating microRNA(miRNA) regulating gene expression by circulating blood to the distal tissues and cells. This article reviewed and summarized the process of circulating miRNAs entering the circulating system to exert gene regulation, especially exogenous miRNAs (such as plant miRNAs), from the perspective of the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence of distal regulation to gene expression, thereby providing a basis for their in-depth research and even application prospects.
MicroRNAs (miRNAs) are a class of small, single-stranded endogenous non‐coding RNA comprising ~ 22 nucleotides (nt) that processed from a stem-loop structured precursor transcript (Kim 2005). MiRNAs exert their affects via base complementary pairing with target message RNA (mRNA), degrading corresponding mRNA or suppressing mRNA translation, as well as act as fine-tuners of the expression of mRNAs (Bartel and Chen 2004). In 2008, Chen et al. found that there were a large number of miRNAs stably existing in human plasma and serum that could circulate to recipient cells and may play a role in regulating gene expression, termed as circulating miRNAs (Chen et al. 2008). Moreover, the circulating miRNA has been demonstrated to own unusually high stability is able to remain stable under various extreme conditions, such as boiling, a very low or high pH, repeated freeze and thaw, and storage at room temperature for a long time (Chen et al. 2008). This feature assures the miRNAs enter and stably exist in the circulatory system of the body and then reach the receptor cells to play a distal regulatory role. In addition, miRNAs were also found in other body fluids such as saliva (Park et al. 2009), urine (Hanke et al. 2010), breast milk (Kosaka et al. 2010), but these miRNAs are outside the scope of this paper. Since researchers first discovered some dietary-derived plant miRNAs stably in human blood in 2011 (Zhang et al. 2012), the academic community has carried out a series of studies and made significant progress on the biological process that how a circulating miRNA originates from cells or plant food enters the circulatory system, circulate to receptor cells, and play a distal regulatory role in the gene expression. The present article performed a review study by dividing the process into the circulating miRNAs source (cell secretion or gastrointestinal absorption), the transport form and pharmacokinetics in circulating blood, and the evidence to function as distal regulators for gene expression, aiming to demonstrate the feasibility of exogenous miRNA as a new active substance entering the receptor cells of the body to play a regulatory role, and to provide scientific basis for subsequent development, research and application prospects.
Typically, miRNA is synthesized by transcription of cell genes. The process of miRNA biogenesis in animal cells involves the following steps. First of all, genes are transcribed as primary transcripts (pri-miRNAs) containing stem-loop structures in the nucleus. Next, the stem-loop structure in pri-miRNA will be cut by the endonuclease Drosha, resulting in a length of about 70nt precursor miRNA (pre-miRNAs). The pre-miRNA is then transported from the nucleus by Exportin-5 into the cytoplasm and the “loop” structure is further cleaved by Dicer/TRBP (TAR RNA-binding protein) to generate mature miRNA/miRNA* duplex. The mature miRNA is then loaded onto Argonautes (Ago) to form the core effector complexes, known as miRNA-induced silencing complexes (miRISCs), whereas the miRNA* strand in the duplex undergoes unwinding, shedding, and degradation (Iwakawa and Tomari 2022). In plants, the biological process of miRNA is different from that in animals. In plants, the cleavage of pri-miRNA is performed by Dicer-like protein (DCL) due to the lack of Drosha homologous proteins, generating pre-miRNA with hundreds of nt in length (Liang et al. 2014). Coincidentally, the cleavage of pre-miRNA is also performed by Dicer-like protein, and both the cleavage of pri-miRNA and pre-miRNA are occurred in the nucleus. After transport to the cytoplasm, the 3’ terminal riboses of plant miRNA undergoes methylation by Hua enhancer 1 (HEN1), resulting in the stability of plant miRNAs against strong acid, strong alkali, and high temperature, which lays a molecular biological foundation for its entry into the organism through the digestive tract (Yu et al. 2005). In animal or human cells, miRNAs bind target mRNA sequences mainly through the canonical base pairing between the seed sequence, which includes nucleotides 2–8 from the 5′-end, and the complementary sequence found in the 3′ untranslated region (3′UTR) of its target mRNA, leading to transcriptional inhibition (Fabian et al. 2011), cleavage (Yekta et al. 2004) or degradation (Wu et al. 2006) of target mRNA. In-depth study of miRNA has illustrated that it also targeted bind to the 5’UTR (Jopling et al. 2008; Orom et al. 2008) and open reading frame (ORF) (Bartel 2004), to play the role of gene regulation. In common with animal miRNAs, plant miRNAs exert their affects via base complementary pairing with target gene mRNA after entering animal or human cells (Cavalieri et al. 2016; Chin et al. 2016; Hou et al. 2018). MiRNAs have been estimated to be involved in about 1/3 of human gene expression regulation, covering many aspects of cellular behavior such as cell growth, division, differentiation, proliferation, apoptosis, and metabolism, and are an important class of gene regulatory molecules (Lewis et al. 2005; Chen et al. 2006).
Part of the miRNA synthesized in cells can be actively secreted into the blood circulation, which increases the content of the corresponding circulating miRNAs and reaches the receptor cells to perform distal regulatory functions. This phenomenon is most prominent during special physiological and pathological periods, like pregnancy, intestinal flora changes, pathogenic microorganism infection, and tumor occurrence. There is evidence that the expressions of miR-516-5p, miR-518b, miR-520 h, miR-525, and miR-526a in the plasma of pregnant women are up-regulated and increase with the progress of pregnancy, and eventually return to basal levels after delivery (Gilad et al. 2008; Gunel et al. 2011; Kotlabova et al. 2011). The above-mentioned circulating miRNAs can pass the placental barrier and affect fetal development (Li et al., 2015). Host miRNAs or food miRNAs may enter the gut bacteria and affect the growth and reproduction of gut flora (Liu et al. 2016, 2019; Teng et al. 2018). On the contrary, the disturbance of intestinal flora may lead to the change of miRNA expression profile in the host circulation (Peck et al. 2017; Moloney et al. 2018; Virtue et al. 2019; Zhu et al. 2020a, b), exerting a regulatory role in the physiological function of the body. Generally, virus infection often induces the differential expression of host cells and secretes signature miRNA (Xu et al. 2021), which is an important source of circulating miRNA in the infected state and contributes to the repertoire of virus-host interactions (Gonda et al. 2019; Zhu et al. 2020a, b). For example, Epstein Barr virus (EBV) can specifically express viral miRNA in B cells, such as miR-BART15, which is secreted by cells through exosomes and transferred to uninfected cells, inhibiting the expression of its binding nucleotide binding oligomerization domain like receptor protein 3 (NLRP3), thereby increasing the susceptibility of B cells to the virus (Pegtel et al. 2010). During tumorigenesis, tumor cells, stromal cells and endothelial cells exposed to the tumor microenvironment can secrete miRNAs through micro-vesicles (Yin et al. 2014) and exosomes (Zhang et al. 2015; Liang et al. 2016; Sun et al. 2018), thereby significantly changing the expression profile of circulating miRNAs. Tumor cells secrete exosomes at least 10-fold more than normal cells (Sun et al. 2018), and the miRNA carried by tumor cell-derived exosomes can enter the circulatory system to play a distal regulatory role as a carcinogen or tumor suppressor (Selth et al., 2012; Chin et al. 2016). For example, miR-222 is highly expressed in exosomes derived from tumors (Di Leva and Croce, 2010; Mao et al. 2018), the expression of miR-222 in the plasma of breast cancer patients is significantly higher than that of normal people, furthermore, the expression of miR-222 in breast cancer patients with lymphatic metastasis is higher than that in non-metastatic group (Ding et al. 2018). Other pathological tissue cells may also lead to significant changes of circulating miRNA (Zhang et al. 2010; Kimura et al. 2018). The expression levels of these characteristic circulating miRNAs have clinical implications for disease diagnosis, prognosis, and even treatment. MiRNAs are mainly secreted into the circulatory system by cells in the form of encapsulated exosomes or micro-vesicles. The secretion of miRNA by cells in the body has been proved to be selective (Squadrito et al. 2014; Garcia-Martin et al. 2022). RNA binding protein (RBP) holds an irreplaceable position in the selective secretion of miRNA, which can regulate the loading of specific miRNA into extracellular exosomes or micro-vesicles through various mechanisms, and finally achieve the purpose of regulating the secretion of miRNA (Groot and Lee 2020), as shown in Table 1. Currently, the underlying mechanism of many RBPs regulating miRNA remains elusive. In addition, studies have shown that the selective secretion of miRNAs by cells is affected by the expression ratio of intracellular miRNAs to target mRNAs (miRNA/mRNA). Specifically, a small miRNA/mRNA ratio favors miRNA residency in cells, and vice versa favors miRNA exocytosis, thereby ensuring moderate regulation of target mRNAs (Squadrito et al. 2014). In addition to active secretion, apoptotic bodies formed during normal cell metabolism can also carry miRNAs into the circulatory system. For example, vascular endothelial cells can release miR-126 into the blood through apoptotic bodies once the body develops atherosclerosis (Zernecke et al. 2009). In the case of tissue cell damage and necrosis, its miRNA can also be passively released into the circulatory system. For example, a large number of cardiomyocytes miR-208 and miR-499 are passively released into the circulatory system during acute myocardial infarction, increasing their plasma concentrations by 1600- and 100-fold, respectively (Corsten et al., 2012).
Another important source of circulating miRNAs is the absorption of exogenous miRNAs (such as plant miRNAs) into the blood through the digestive tract. Zhang et al. (2012) first found that about 5% of miRNAs in human and animal serum have anti-sodium periodate properties in 2011, and thus speculated that their possible sources were plant miRNAs in daily diet, and further proposed the view that plant miRNAs in food could be absorbed into the body’s blood circulation through digestive tract. The research team subsequently confirmed through experiments that mice miR-168a was absorbed into the blood circulation through the digestive tract and reached the liver, simultaneously targeted the liver low-density lipoprotein receptor adaptor protein 1 (LDLRAP1) and inhibited its expression, ultimately promoted the metabolism of low-density lipoprotein (LDL) (Zhang et al. 2012). Since then, the view that exogenous plant miRNAs can enter the blood through the digestive tract and reach distant tissues and organs in the body has been continuously confirmed by other studies. Liang et al. detected cabbage miRNA in the gastrointestinal, blood, spleen, liver, kidney, feces, and other samples of mice after feeding mice with cabbage (Liang et al. 2014). Liang et al. recruited volunteers to eat watermelon juice or mixed fruits and detected a variety of corresponding fruit miRNAs in their plasma (Liang et al. 2015). Luo et al. detected 16 maize miRNAs in the serum, pancreas, and longissimus dorsi of pigs after feeding fresh maize for 7 days (Luo et al. 2017). In addition, Tarallo et al. found that there were significant differences in the expression profiles of fecal miRNA among vegans, vegetarians, and omnivores, indirectly indicating the influence of food miRNA on human body (Tarallo et al. 2021). Based on the above argument and evidence, the researchers further studied the absorption of herbal miRNA in the body, successfully found that herbal miRNA could still exist stably after being decocted at high temperature and was able to be absorbed into the blood through the digestive tract, as well as played a regulatory role in the distal tissues of the body (Li et al., 2015; Zhou et al. 2015; Zhou et al. 2020; Kalarikkal and Sundaram, 2021; Teng et al. 2021). Besides plant miRNAs, miRNAs in milk exosomes could also be absorbed into the blood through the digestive tract (Manca et al. 2018). Moreover, our body might obtain circulating miRNA by in vitro injection as well, making sense for its applicable usage (Teng et al. 2021). Some plant or herbal miRNAs have been confirmed by existing studies to have strong heat, strong acid, and other stability, and can withstand high-temperature cooking, torment, and digestion and degradation. The reasons can be attributed to the following aspects: (1) Methylation at the 3’ end of plant miRNAs (Zhang et al. 2012) and/or its special sequence composition (such as rich in CG) (Zhou et al. 2015); (2) Plant cell exosomes have strong thermal and acid stability (Lasser et al. 2011; Mu et al. 2014), which can effectively protect their encapsulated miRNA. The differences in the expression profiles of plant miRNAs in food and plasma indicate that the body also selectively absorbs plant miRNAs (Zhang et al. 2010). Based on the synthesis and action process of miRNA, it is speculated that the existing forms of miRNA in food include pri-miRNA, pre-miRNA, miRNA:miRNA*duplex, free miRNA, AGO2-bound miRNA, miRISC. Studies have shown that mature free miRNA (Zhang et al. 2012) and miRNA:miRNA*duplex (Chin et al. 2016; Hou et al. 2018) can be absorbed into the blood and become circulating miRNAs. Two mechanisms may be involved in the entry of exogenous plant miRNAs into the body’s circulation. First, plant miRNAs are absorbed by gastrointestinal epithelial cells, and then actively secreted into the blood by gastrointestinal epithelial cells (Jia et al. 2021). Chen et al. demonstrated through in vitro and in vivo experiments that dietary plant miRNAs can be absorbed into the blood by SID-1 transmembrane family member 1 (SIDT1) in the plasma membrane of gastric epithelial mucous cells (Chen et al. 2021). Second, plant miRNAs are encapsulated in plant exosomes and absorbed by gastrointestinal epithelial cells through endocytosis (Kusuma et al. 2016; Manca et al. 2018); and then actively secreted into the blood by gastrointestinal epithelial cells.
Circulating miRNAs are characterized by high stability, and one of the important reasons for this characteristic is that they are usually encapsulated and transported by exosomes, micro-vesicles, apoptotic bodies, and other vesicles (A et al., 2009; Blanc and Vidal 2010), thus reducing or avoiding the degradation of various nucleic acid metabolizing enzymes. There is evidence that up to 83–99% of circulating miRNAs are stored in exosomes (Gallo et al. 2012); miRNAs in exosomes include AGO2-bound miRNAs, free miRNA, mature miRISCs (Mao et al. 2015). Another important reason is that circulating miRNAs inside and outside vesicles are often bound to proteins, which can also improve their stability. Studies have shown that 90% of circulating miRNAs are bound to RBPs (Chang et al. 2004; Arroyo et al. 2011), such as AGO2 protein (Arroyo et al. 2011), high density lipoprotein (HDL) (Vickers et al. 2011; Tabet et al. 2014) and LDL (Wagner et al. 2013), and most circulating miRNAs are transported as AGO2-bound miRNAs (Arroyo et al. 2011). The metabolism of circulating miRNAs in blood is likewise a significant parameter. In theoretical terms, plant miRNA in food is absorbed through the gastrointestinal tract, and its content level first increases in the gastrointestinal tissue, and then enters the blood circulation to become circulating miRNA, which is selectively absorbed by distant tissue cells. Zhang et al. (2012) showed that the content level of MIR-168a in plasma and liver tissue increased significantly at 6-hour after mice ate fresh rice. Further research found that the content level of MIR-168a in plasma and liver tissue reached the peak 3-hour after mice were intervened by total RNA extract of rice (Zhang et al. 2012). Hou et al. found that serum MIR-156a peaked within 1 to 3 h in most of the five healthy volunteers eating lettuce (Hou et al. 2018). Additional studies have shown that the plasma MIR-2911 of animals reached the peak after continuous feeding of honeysuckle for 3 days and returned to the baseline level 2-day after stopping feeding (Yang et al. 2015a, b). After oral administration of honeysuckle decoction, the content of MIR-2911 in plasma and lung tissue of mice increased and reached the peak at 6 h, followed by a gradual decline, eventually the lung MIR-2911 recovered to the baseline level at 12 h (Zhou et al. 2015). Furthermore, it is reported that MIR-2911 could be detected in the serum of male ICR mice 5-minute after tail vein injection of an equal mixture of MIR-2911, MIR-168a, MIR-156a and MIR-161, and all miRNAs were cleared after 3 h (Yang et al. 2015a, b). All the data above indicates that circulating miRNAs can exist in the body for hours or days (Ruegger and Grosshans 2012), and their levels can be kept stable through continuous feeding. It is worth noting that intravenous injection can rapidly increase circulating miRNA content (Yang et al. 2015a, b).
Recent research demonstrates that circulating miRNAs, especially exogenous plant miRNAs that are absorbed into the blood, can enter receptor cells through endocytosis, membrane fusion or ligand-receptor binding (Cavalieri et al. 2016; Chin et al. 2016; Hou et al. 2018), and play a distal role to regulate gene expression. Table 2 lists the research reports about researchers intervening in cells or organisms by molecular biology techniques in recent years so that exogenous miRNAs can enter recipient cells and play a regulatory role in cells or organisms. These identified studies amply illustrate that circulating miRNAs, especially exogenous plant miRNAs, can overcome multiple barriers to enter the body, reach recipient cells, and perform transboundary gene regulation functions.
Taken together, endogenous miRNA and exogenous plant miRNA can enter the body’s blood circulation through cell excretion or gastrointestinal absorption, correspondingly causing changes of circulating miRNA expression profile, as well as enter the distal tissue receptor cells to play a role in regulating gene expression. The above-mentioned findings provide significance for the application prospects of miRNAs, especially plant miRNAs. Currently, circulating miRNA has been used as a biomarker for early screening, diagnosis, and prognosis of diseases (Takahashi et al. 2019) because of its stable existence in body fluids such as plasma and serum, state-specific differences in expression profiles in different physiological and pathological states, convenient sampling, and sensitive detection. Moreover, the long-range regulatory properties of miRNA make it potential for gene-targeted therapy. For the moment, miRNA drugs for targeted therapy of leukemia have entered phase I and phase II clinical trials (Takahashi et al. 2019). How to deliver exogenous miRNA drugs to target tissue cells for targeted therapy is a key technology to improve the effect of miRNA targeted therapy on cancer (Sabit et al. 2021) and also a difficulty in the development and utilization of miRNA drugs. In recent years, viruses are generally considered to be the first-choice for miRNA delivery due to their advantages of strong specificity, high delivery efficiency, low off-target effects, and long expression duration (Havlik et al., 2020). Some plants are rich in miRNAs with specific functions (Cavalieri et al. 2016; Zhu et al. 2017; Hou et al. 2018; Teng et al. 2021), and can introduce miRNAs with therapeutic effects into the body using natural vegetables or herbs as carriers. This type of miRNA delivery method is low-toxic and economical, which is considered to be a promising research and development direction. Although existing studies have proved that circulating miRNAs, especially exogenous plant miRNAs, can overcome many barriers to reach the receptor cells and exert the function of remote gene regulation (Cavalieri et al. 2016; Zhu et al. 2017; Hou et al. 2018; Teng et al. 2021), the molecular biological processes of plant miRNAs, including selective uptake and secretion, entry into recipient cells, and formation of mature miRISCs, are still poorly understood. As outlined previously, during the assembly of intracellular miRNAs into miRISCs, mature miRNAs are loaded onto AGO proteins in the form of miRNA:miRNA*duplex to form miRISC precursors, and mature miRISCs are formed after miRNA* unwinds and falls off. However, in the current functional studies on the introduction of exogenous miRNAs into the body or cells, the initial miRNA interveners used involve many forms, including mature free miRNA monomer (Zhang et al. 2012), miRNA: miRNA* dimer (Chin et al. 2016), exosomes (Shen et al. 2021) or natural plants (Hou et al. 2018) containing target miRNA, pre-miRNA (Almanza et al. 2018). Here are some key issues to be further interpreted. What is the difference between the above-mentioned exogenous miRNA interveners and intracellular miRNAs during their assemblies of miRISC? Which exogenous miRNA interveners have better distal regulatory effect? And how to artificially synthesize exogenous miRNA interveners with good stability and high absorption rate for subsequent application? All these are important issues to be solved in the process of miRNA drug development. In conclusion, circulating miRNAs, originated from cell excretion or gastrointestinal absorption, can overcome barriers to reach the receptor cells and exert the function of remote gene regulation. While the detail mechanisms are still waiting to be interpreted. |
PMC9648901 | 36381197 | Inna Shokolenko,Mikhail Alexeyev | Mitochondrial DNA: Consensuses and Controversies | 10-06-2022 | mtDNA,mtDNA transcription,mtDNA replication,mtDNA repair,mitochondrial theory of aging,extramitochondrial mtDNA | In the course of its short history, mitochondrial DNA (mtDNA) has made a long journey from obscurity to the forefront of research on major biological processes. mtDNA alterations have been found in all major disease groups, and their significance remains the subject of intense research. Despite remarkable progress, our understanding of the major aspects of mtDNA biology, such as its replication, damage, repair, transcription, maintenance, etc., is frustratingly limited. The path to better understanding mtDNA and its role in cells, however, remains torturous and not without errors, which sometimes leave a long trail of controversy behind them. This review aims to provide a brief summary of our current knowledge of mtDNA and highlight some of the controversies that require attention from the mitochondrial research community. | Mitochondrial DNA: Consensuses and Controversies
In the course of its short history, mitochondrial DNA (mtDNA) has made a long journey from obscurity to the forefront of research on major biological processes. mtDNA alterations have been found in all major disease groups, and their significance remains the subject of intense research. Despite remarkable progress, our understanding of the major aspects of mtDNA biology, such as its replication, damage, repair, transcription, maintenance, etc., is frustratingly limited. The path to better understanding mtDNA and its role in cells, however, remains torturous and not without errors, which sometimes leave a long trail of controversy behind them. This review aims to provide a brief summary of our current knowledge of mtDNA and highlight some of the controversies that require attention from the mitochondrial research community.
The metazoan genome is sequestered in two spatially distinct compartments: the nucleus and the mitochondria. The nuclear genome encodes the vast majority of genetic information and is represented by two chromosomes of each type inherited biparentally (one from each parent). In contrast, the metazoan mitochondrial genome is typically a much smaller (~14,000–18,000 bp), maternally inherited circular molecule, present in multiple copies, from less than a dozen per mature human sperm [1] to more than 100,000 in oocytes [2]. Discovered almost 60 years ago by Nass and Nass [3], mitochondrial DNA (mtDNA) for a long time was considered vestigial until 1988, when several groups established a link between mtDNA mutations and incurable, devastating, and often lethal human diseases, for which we still do not have effective treatments [4–6]. These discoveries brought about an era of mitochondrial biology in which mtDNA plays a central role as it contributes to all mitochondrial functions, either directly or indirectly. Over the years, mtDNA alterations have been implicated in the pathogenesis of virtually all organ systems: respiratory system [7,8], digestive and excretory system [9,10], circulatory system [11,12], urinary system [13], integumentary system [14,15], skeletal system [16,17], muscular system [18], endocrine system [18], lymphatic system [19,20], nervous system [21,22], and reproductive system [23–25]. Due to this ubiquitous involvement in biological processes, mtDNA elicits widespread interest. As a result of this interest, most of today’s knowledge on mtDNA has been derived from human and related mammalian and vertebrate species. Unless otherwise indicated, this review will be limited to these species.
Most commonly, mtDNA is a circular molecule present in cells in a tissue-specific number of copies [26] (Figure 1). The first sequenced mitochondrial genomes were those of humans and mice [27,28], which are of similar length and have identical organization. Each genome encodes 37 genes: 2 rRNA, 22 tRNA, and 13 polypeptides, all of which are components of the mitochondrial oxidative phosphorylation (OXPHOS) system. Seven polypeptides contribute to OXPHOS complex I (CI), one polypeptide (cytochrome B) contributes to complex III (CIII), three polypeptides contribute to complex IV (CIV), and two polypeptides contribute to complex V (CV). Notably, none of the complex II (CII) subunits are encoded in mtDNA; therefore, mtDNA mutations should not affect CII activity. This consideration is used to normalize experimentally determined activities of other OXPHOS complexes, especially when one is studying the primary mitochondrial disease. Two strands of mtDNA have asymmetric nucleotide composition and, as a result, could be separated in alkaline denaturing CsCl gradients based on their G+T (as opposed to separation based on G+C content observed with dsDNA). This is believed to be because, in alkaline solutions, G and T bases become ionized and can interact with Cs+ ions, thus conferring a higher density to the strand with a higher content of these bases [29]. Therefore, a heavy (high G+T content) and light (low G+T content) strand can be identified in each mtDNA molecule (the H-strand and L-strand, respectively). This distinction between the two mtDNA strands, in conjunction with the asymmetric distribution of genes between strands, is associated with the first mtDNA controversy. Siv Anderson’s and Clayton’s groups, which sequenced the first mitochondrial genomes [27,28], used different definitions for “coding strand”. Anderson et al. defined the coding strand much like we do today, as the DNA strand whose base sequence is identical to the base sequence of the RNA transcript. They reported that L-strand is the main coding strand in human mtDNA (hmtDNA). It encodes 12 of 13 polypeptides, both ribosomal RNAs (rRNAs), and 14 of 22 transfer RNAs (tRNAs). Bibb et al. called the opposite strand (the template or noncoding strand by today’s convention) coding, and they concluded that the H-strand encodes most genes. Despite being at odds with the contemporary terminology, statements that the H-strand is the main coding strand in hmtDNA can be found even in recent reviews [30]. Interestingly, the L-strand of mtDNA is not the main coding strand in all organisms. A recent review examined 4205 vertebrate mitogenomes and established that the H-strand was the main coding strand in five of them [31]. The confusion about coding mtDNA strands also contributes to the disparate presentation of mtDNA molecules in the literature. Conventionally, circular genomes are annotated clockwise. However, in the mitochondrial literature, it is not uncommon to see mtDNA maps with inverted gene order in addition to the conventional maps that can be automatically generated by software packages from GenBank entries (similar to the one present in Figure 1A). This situation is not helped by the fact that some scientific illustration software makers adopted an outdated counterclockwise depiction of mtDNA (see, e.g., Figure 1B and [32]). Inside mitochondria, mtDNA is organized into nucleoids named for their resemblance to the irregularly shaped regions within the cell of a prokaryote containing all or most of the genetic material. Apart from mtDNA, these structures contain various proteins that facilitate mtDNA compaction and metabolism [33]. Most commonly, nucleoids are visualized by labeling with various DNA stains, including anti-DNA antibodies, BrdU/anti-BrdU antibodies, and fluorescent intercalators, such as DAPI and Pico Green, etc., followed by fluorescence microscopy. Therefore, the number of nucleoids detected per cell (and thus estimates of the number of mtDNA molecules per nucleoid) depends on the properties of the optical system used, such as optical resolution and signal-to-noise ratio. As a result, reported numbers of mitochondrial genomes per nucleoid greatly vary in the literature. The lowest reported estimate of 1.45 mtDNA molecules per nucleoid, obtained with the help of stimulated emission depletion (STED) microscopy [34], is consistent with the “resting” ratio of one mtDNA molecule per nucleoid and is likely the most accurate. However, it is not possible to exclude the chance that this number is variable and/or depends on the cell type/physiological condition. Nucleoids are ovoid structures that are diverse in size, with an average diameter of about 100 nm [34,35]. They are associated with the mitochondrial inner membrane and are often wrapped around cristae or cristae-like membrane invaginations [35]. Experimental evidence suggests that there is little, if any, exchange of mtDNA between nucleoids [36]. mtDNA in nucleoids is packed more densely than in Escherichia coli nucleoids or human nuclei [37]. mtDNA compaction in nucleoids is driven by mitochondrial transcription factor A (TFAM), a high mobility group (HMG)-box DNA binding protein with functions in mtDNA packaging, replication, and transcription [38]. TFAM’s reported footprint on DNA is 23 bp or 30 bp [39]. Its reported abundance in mitochondria exceeds that of mtDNA by a factor of 1000. This high molar excess of TFAM is sufficient to completely coat mtDNA, assuming that most TFAM is bound to it [34]. This latter assumption is supported experimentally, as it has been reported that TFAM not bound to mtDNA is subject to phosphorylation at Ser55 and Ser56 and degradation by Lon protease [40]. TFAM binds mtDNA specifically at mitochondrial promoters (the L-strand promoter, LSP, and the H-strand promoter 1, HSP1) to facilitate transcription and replication. It also binds the mitochondrial genome non-specifically [41] to induce mtDNA compaction. This compaction depends on TFAM dimerization, which, in turn, is promoted by its N-terminal HMG domain [42]. Whether bound to mtDNA specifically or non-specifically, TFAM imposes on it a sharp bend, dubbed a U-turn. This bending is essential for both transcription and packaging [42].
Even though mtDNA encodes only 37 genes, their nomenclature remains discordant, controversial, and at times confusing. At least two nomenclatures for mtDNA-encoded genes co-exist in the literature [43,44]. However, the HUGO Gene Nomenclature Committee (HGNC) recommends the one based on the system first implemented by D. Wallace [43,45]. This nomenclature takes historical precedence over its alternative and boils down to a few simple rules. Every mitochondrial gene receives a prefix MT to distinguish it from nuclear genes. This prefix is followed by a hyphen and a gene name. Gene names are RNR1 and RNR2 for 12S and 16S mitochondrial rRNAs, respectively, ND1-ND6 for mtDNA-encoded CI subunits, CO1-CO3 for CIV subunits, APT6 and ATP8 for CV subunits, and CYB for the cytochrome B gene. Mitochondrial tRNA gene names consist of the letter T followed by a single-letter amino acid code for the amino acid that acylates this tRNA (e.g., MT-TV for mitochondrial valine tRNA). The amino acids leucine and serine acylate two isoacceptor tRNAs, and two corresponding isoacceptor tRNAs are designated MT-TL1 and MT-TL2 (for tRNAs recognizing codons UUR and CUN, respectively), and MT-TS1 and MT-TS2 (for tRNAs recognizing codons UCN and AGY, respectively). Strikingly, the alternative nomenclature [44] provides the opposite designations to leucine and serine isoacceptor tRNAs (i.e., L2 or trnL2 for MT-TL1 and vice versa). As both nomenclatures have been implemented in software packages, care must be taken when automatically annotating newly sequenced mitochondrial genomes. Another issue lacking consistency in the literature across the taxa is the mtDNA starting nucleotide. The human reference genome (NC_012920) base numbering starts in the middle of the control region, whereas murine (NC_005089) and rat (NC_005089) reference genomes start at the edge of the control region, with the first base of the gene for mt-Tf. In the Boore nomenclature [44], mtDNA is opened at MT-CO1. For reasons that appear obvious, the “human” base numbering is impossible to directly extend to species whose mtDNA has more than one control region (e.g., NC_021479) or that lack the control region altogether (e.g., NC_000834). This underscores the need to develop a uniform mitochondrial nomenclature, which would require concerted efforts of investigators from diverse fields.
In recent years, it became increasingly apparent that in addition to its 37 “formal” genes, mtDNA may encode short open reading frames (ORFs), which can be translated into peptides with important biological functions. The first such peptide, humanin, was identified more than 20 years ago in an unbiased functional screen for clones that protect neuronal cells from death induced by amyloid precursor protein (APP) mutants, which are associated with early-onset familial Alzheimer’s disease [46]. Humanin is encoded by a 75 bp ORF within the gene for MT-RNR2 and was independently isolated in a yeast two-hybrid screen as a partner of the insulin-like growth factor-binding protein-3 (IGFBP-3) [47]. Humanin has since been shown to exert cytoprotective effects against not only mutant APPs but also neuronal cell death induced by other stimuli such as mutant presenilins 1 and 2, and cytotoxic Aβ peptides, Aβ1–42, Aβ1–43, and Aβ25–35 [48]. It has also been shown to protect against IGFBP-3-induced apoptosis [47]. Six other humanin-like peptides were discovered in MT-RNR2. Another short ORF encoding a 16-amino-acid-long mitochondrial open reading frame of the 12S rRNA-c (MOTS-c, reviewed in [49]) has been discovered within the gene for mitochondrial MT-RNR1. This peptide targets skeletal muscle, and its cellular actions inhibit the folate cycle and de novo purine biosynthesis, leading to activation of the AMP-activated protein kinase. MOTS-c treatment in mice prevented age-dependent and high-fat-diet-induced insulin resistance as well as diet-induced obesity [50]. Since then, MOTS-c has been described as an exercise-induced mitochondrial-encoded regulator of age-dependent physical decline and muscle homeostasis [51]. Apart from MT-RNR-encoded small peptides, RNAseq studies established that up to 15% of the mitochondrial transcriptome is made up of long noncoding RNAs (lncRNAs) and micro RNAs, some of which are encoded in mtDNA (micromiRs) [52,53]. At least eight mtDNA-encoded lncRNAs have been identified so far, in addition to small noncoding RNAs and circular RNAs (reviewed in [30,54]). While some of these RNAs have already found their use, e.g., as biomarkers of cardiac remodeling [55], others await the establishment of their bona fides.
In the mammalian mitochondria, a single DNA polymerase (DNA polymerase γ, POLG) mediates both replication and repair of mtDNA, and consists of a large catalytic subunit and two accessory subunits. The DNA polymerase, 3′ → 5′ exonuclease (proof-reading), and 5′-deoxyribose phosphate (dRP) lyase activities are found in the catalytic subunit. The accessory subunits enhance DNA binding and processivity. The mitochondrial replisome consists of POLG, the mitochondrial single-stranded DNA binding protein (SSBP1), mitochondrial DNA helicase TWINKLE, and includes topoisomerase and RNaseH activities [56]. No dedicated DNA primase activity was described in the mitochondria. The mode for mtDNA replication remains controversial (reviewed in [57,58]). The original asynchronous strand-displacement model [59–61] suggests that mtDNA replication is primed by an abortive LSP transcript (7S RNA). Interaction of the transcription elongation factor TEFM with POLRMT regulates the balance between priming mtDNA replication and generating a near-genomic-length transcript [62]. Once initiated, replication of the H-strand proceeds unidirectionally over ~70% of mtDNA length until it exposes the origin of the L-strand replication (OL). Then, synthesis of a new L-strand is initiated in the opposite direction. This model agrees well with multiple lines of experimental evidence, including the distribution of the de novo point mutations in mtDNA [63]. In the alternative strand-coupled (synchronous) model, there is thought to be a zone of replication initiation within a broad area beyond the D-loop. Within this zone, both strands are synthesized bidirectionally as the conventional double-stranded replication forks advance through continuous synthesis of leading strands and discontinuous synthesis (through Okazaki fragments) of lagging strands. However, this model relies on continuous ligation of Okazaki fragments during the lagging strand synthesis and appears to be inconsistent with recent findings that 100-fold reduction in mitochondrial DNA ligase III does not appreciably affect the rate of mtDNA replication or copy number [64]. The third model is based on the observation of RNA incorporation throughout the lagging strand (RITOLS) [65]. According to this model, replication proceeds as in the strand-displacement model, except the displaced H-strand is present not as single-strand DNA, but rather as DNA/RNA hybrid sensitive to RNAse H, up until it is made duplex by POLG. It is not clear whether RITOLS serve as primers for Okazaki fragments, but it appears unlikely due to the low reliance of mtDNA replication on the quantity of DNA ligase III (see above). Recently, it was demonstrated that the in vivo occupancy profile of mtSSB displays a distinct pattern, with the highest levels of SSBP1 close to the mitochondrial control region and with a gradual decline towards OriL. This pattern correlates with the replication products expected for the strand displacement mode of mtDNA synthesis, thus lending strong in vivo support [66].
The copy number of mtDNA molecules per cell varies between tissues; the two extremes of this spectrum are mammalian erythrocytes and sperm, which have no mtDNA and ~5 copies of mtDNA per cell [1], respectively, and oocytes, which may contain >500,000 copies [2]. mtDNA can be eliminated from sperm in the Drosophila male genital tract prior to fertilization, and fertilizing sperm may contain no mtDNA at all, ensuring the uniparental inheritance [67]. In contrast, in mice, uniparental mtDNA inheritance may be facilitated by autophagy of paternal mitochondria in fertilized zygotes [68–70]. Curiously, human oocyte quality directly correlates with mtDNA copy number (mtCN), whereas this correlation is inverse for human sperm [2]. It is important to note that the normal mtCN in a given tissue is not a set figure but can vary over a considerable range. In many studies, mtCN in apparently healthy individuals varies over a 2–10-fold range [71], and mtDNA content between 40–150% of the average is considered clinically normal [72]. The most commonly used techniques for quantifying cellular mtDNA content, qPCR and ddPCR, have resolution limits of approximately 50–60% and 30%, respectively [73]. In samples taken from the same culture over a one-week period, mtCN can vary over the 2–3-fold range [74]. Taking into account this variability, it is unclear how much of the experimentally observed range is attributable to technological difficulties, but it seems obvious that the modest variations in mtCN reported in some studies require thorough validation before being ascribed any biological significance. Apart from the normal variation, mtDNA content can be altered in various pathologic scenarios. mtDNA depletion syndromes [75] are associated with the most dramatic alterations in mtCN, which can drop as much as 50-fold [76]. Such dramatic changes are usually associated with perinatal lethality; however, long survival has been reported in some cases. A 29-year-old patient with 24% residual was observed for this condition since early childhood [77]. In another example, a profound (91%) loss of mtDNA in a 47-year-old patient was associated with relatively mild symptoms such as daytime sleepiness, exercise intolerance, and myalgias in the lower-limb muscles [78]. Therefore, more research is needed to thoroughly delineate the relationship between mtCN and clinical phenotypes. Contributions of specific proteins to mtCN control remain controversial. The best-studied and most controversial protein in this respect is, perhaps, TFAM. Available evidence suggests that, at least in some experimental systems, mtDNA copy number, mtDNA transcription, and translation of mtDNA-encoded polypeptides, as well as some mitochondrial functions, may closely parallel TFAM expression [79–83]. Therefore, it is often thought that the strictly proportional abundance of TFAM and mtDNA observed in some studies may be dictated by the mutual stabilization of these two components of mitochondrial nucleoids. These views led to a model that describes TFAM’s involvement in mitochondrial biogenesis [84]. Notwithstanding the evidence in support of the close positive correlation between TFAM expression and mtCN, there is a large body of contradictory evidence. TFAM overexpression in flies did not affect mtDNA copy number [85]. In cultured cells, recovery of TFAM levels after ethidium bromide-induced mtDNA depletion lagged behind the recovery of mtDNA copy numbers, suggesting that an increase in mtCN can occur without a proportional increase in TFAM levels [86]. Conversely, a transient TFAM overexpression in cultured cells did not affect the mtDNA copy number [79]. Other investigators observed in developing muscle cells a decrease in mtDNA copy number despite a 4-fold increase in TFAM expression [87 in TFAM levels [86]. Conversely, a transient TFAM overexpression in cultured cells did not affect the mtDNA copy number [79]. Other investigators observed in developing muscle cells a decrease in mtDNA copy number despite a 4-fold increase in TFAM expression [87], indicating that, at least in some settings, increased TFAM expression does not drive increased mtCN. By employing TFAM knockdown and overexpression, we have found that in some cell lines, but not others, mtCN qualitatively, rather than quantitatively, correlates with TFAM expression [88]. Examination of the human protein atlas (www.proteinatlas.org/ENSG00000108064-TFAM/single+cell+type, accessed on 6 June 2022) provides a vivid illustration of the disjunction between TFAM expression and known mtCNs. For example, in the heart, TFAM expression in cardiac myocytes (in which up to 37% of the cellular volume is occupied by mitochondria [89]) is on par or even lower than in endothelial cells, fibroblasts, mixed immune cells, or smooth muscle cells. Finally, and most significantly, in a patient with myoclonic epilepsy with ragged red fibers (MERRF), the tissue with the highest mtDNA copy number had the lowest TFAM levels [90]. Collectively, this evidence indicates that the strong positive relationship between TFAM expression and mtDNA replication observed in some systems is not universal. Incongruency between TFAM expression and mtDNA copy number deserves attention in the context of the models for mtDNA replication and packaging into nucleoids. Despite the dissenting reports [79,91,92], the prevailing view is that TFAM is present in cells in quantities sufficient to completely cover mtDNA [34,80,81,93,94]. It has also been reported that some cells lacking mtDNA have reduced TFAM expression compared to parental cells containing mtDNA and that the release of TFAM from complexes with mtDNA occurs by Lon-mediated degradation [40]. This appears to suggest a mandatory stoichiometric relationship between TFAM and mtDNA. However, a recent study indicates that in a tissue-specific knockout of the mitochondrial RNA polymerase (PolRmt), TFAM expression remains unchanged despite a severely reduced mtDNA copy number. This TFAM persists free of mtDNA and is not degraded by Lon, which suggests that TFAM/mtDNA stoichiometry is not a universal phenomenon [95]. These observations also suggest that though TFAM may be present in quantities sufficient to completely cover mtDNA, mtDNA in vivo may, in fact, be only partially covered, and a significant pool of “free” TFAM may exist in mitochondria, at least in some scenarios. This latter consideration agrees well with a recent “sliding” model of mtDNA transcription [39] and with the above-cited observations in the MERRF patient [90]. Observations made with some other proteins were also inconclusive. Thus, both increased [96] and decreased [97] skeletal muscle mtDNA content has been reported in patients with mutations in mitofusin 2 (MFN2). While not directly attributable to any particular protein, changes in mtDNA content in the tissues of aged individuals have been widely reported, although the direction of these changes also remains controversial. Some studies report an increased mtDNA copy number in the elderly [98], while others report a decrease and associate frailty with either a lower [99] or higher [100] mtDNA copy number. In search of the possible mechanisms of mtCN control, sex-specific quantitative trait loci for mtDNA content have been identified on human chromosomes 1, 2, and 3 [101]. Moreover, epigenetic modification of exon 2 of the gene for the catalytic subunit of the mitochondrial DNA polymerase (POLG) has been recently implicated in mtDNA copy number regulation [102]. Despite this progress, cellular mechanisms that govern mtCN control remain largely enigmatic. In the laboratory setting, mtCN can be reduced by blocking mtDNA replication with intercalating agents such as ethidium bromide and/or POLG inhibitor dideoxycytidine. However, some cells either are naturally resistant to such treatments or may develop resistance during treatment and recover their mtCN [103]. Such treatments are of intrinsically limited utility as they do not allow for the establishment of cell lines with stable mtCN. However, stable cell lines with reduced mtCN can be established by limiting mitochondrial DNA ligase activity by expressing bacterial DNA ligase in the cytosol of cells deficient in DNA ligase III, the only DNA ligase found in mitochondria [104].
Compared to the nucleus, the repertoire of DNA repair pathways documented in mitochondria is limited (reviewed in [58,105]). As all polypeptides encoded by mtDNA are components of the OXPHOS system, all mitochondrial functions, including mtDNA repair, depend on proteins encoded in the nucleus, which are translated on cytoplasmic ribosomes and post-translationally imported into mitochondria. Mitochondria are proficient in both short-patch and long-patch subpathways of the Base Excision Repair (BER) pathway. This pathway is responsible for the repair of oxidative and alkylating lesions as well as single-strand breaks in both nuclear (nDNA) and mtDNA [58,105]. Importantly, some evidence suggests that certain oxidized base lesions are repaired more efficiently in mitochondria than in the nucleus [105]. Considering that oxidative mtDNA damage is most frequently mentioned as relevant, it would, perhaps, be inappropriate to state that mitochondria are deficient in DNA repair, at least as far as the repair of the biologically most relevant lesions is concerned. The evidence for the presence of other complete DNA repair pathways in mitochondria remains inconclusive. Although mismatch repair (MMR) [106] and double-strand break repair (DSBR) [107] activities have been demonstrated in mammalian mitochondrial lysates, some argued that these results should be interpreted with caution because of the challenges involved in obtaining mitochondrial preparations [108]. How, then, do mitochondria cope with the mutagenic effects of DNA lesions that they are unable to repair? It turns out that in mammalian cells, the high redundancy of mtDNA enables a unique, mitochondria-specific pathway for the preservation of DNA integrity through the degradation of damaged molecules. This pathway is nonspecific to the type of lesion and could be mobilized not only in response to lesions that mitochondria are unable to repair but also in response to the presence of an overwhelming amount of lesions that mitochondria can repair in moderate quantities, such as oxidative lesions [109], abasic sites [110], and gapped duplexes [111] (Figure 2). The kinetics of this process may be different in different cell lines, and in some cell lines, mtDNA loss can be detected as soon as 5–10 min after the challenge with H2O2 [112]. mtDNA degradation in response to overwhelming damage is well documented and has been used to completely destroy mtDNA in cells and generate so-called ρ0 cells [103,110]. For a long time, the enzymatic activities responsible for mtDNA degradation eluded identification. However, recently it has been revealed that mtDNA degradation in cultured human and mouse cells may be mediated by Mitochondrial Genome Maintenance Exonuclease 1 (MGME1) and the proofreading activity of mitochondrial DNA polymerase gamma (POLG) [113,114]. Of note though is that other evidence suggests that in Drosophila spermatogenesis, POLG (Tamas) may mediate mtDNA degradation by mechanisms that do not involve its proofreading activity [115]. While preservation of mtDNA integrity through the degradation of damaged molecules has only been documented in mammalian cells, the ability of Drosophila Tamas to destroy mtDNA during spermatogenesis [115] suggests that similar mechanisms may operate in other taxa.
At least two promoters are needed to transcribe genes encoded in two mtDNA strands. The existence of a single light strand promoter (LSP) is generally accepted. However, it remains controversial whether there is one or two heavy strand promoters (HSP). Very early on, it has been noted that MT-RNR1 transcript is about 15–60-fold more abundant and is transcribed at a 50–100-fold higher rate compared to the most abundant mRNA transcript encoded by the H-strand [116,117]. Two possible explanations were proposed: (a) the existence of two HSPs, and (b) the premature termination downstream of the mitochondrial 16S rRNA (MT-RNR2). Both models are currently supported by experimental evidence. Two transcription initiation sites were identified in H-strand: one at bp 561 of human mtDNA, 16 nucleotides upstream of the MT-TF gene (HSP1), and a second (HSP2) at bp 646, just two nucleotides upstream of the MT-RNR1 gene inside the MT-TF gene (Figure 1) [118–120]. Both promoters are active in an in vitro system; although, in this system the major transcription start site of HSP2 maps to A644 instead C646 [121,122]. An alternative school of thought argues for the existence of a single HSP promoter [123,124]. It has been argued that since mitochondrial transcription termination factor 1 (MTERF1) is dispensable for mouse viability and since in MTERF1 knockout mice no changes in the abundance of putative HSP1 and HSP2 promoter transcripts are observed, MTERF1 cannot selectively stimulate HSP1 transcription by DNA looping as predicted by one of the two HSP-promoter models [120]. However, this argument addresses the mechanism of HSP1 regulation rather than the existence of the HSP2. Generally speaking, one should exercise caution when extrapolating findings in the human system to those in the murine system and vice versa, even though an assumption that these two systems are regulated in a very similar or identical way appears reasonable. Therefore, it remains possible that there are two HSP promoters in human mtDNA, while in murine mtDNA there is only one. Some evidence suggests that regulation of at least LSP may be different in human and murine cells. Indeed, mLSP has more extensive upstream sequence requirements for maximal transcription in vitro than hLSP does (Figure 1 and [125]). To summarize, available evidence does not conclusively rule out the existence of two separate HSP promoters in human cells in vivo.
Animal mtDNA is inherited through the maternal germline with few exceptions (reviewed in [105]). As stated above, in different species, sperm mtDNA can be destroyed either prior to fertilization or after fertilization through autophagy of sperm mitochondria [68–70,115,126]. In either case, the resulting zygote inherits only maternal mtDNA [127]. In most animals, this uniparental mtDNA inheritance is further enforced by a 10,000–100,000-fold dilution of the paternal mtDNA in zygotes (compare, e.g., [1,2]). Despite these formidable mechanisms guarding maternal mtDNA inheritance, accumulating evidence suggests that, in rare cases, paternal mtDNA can be inherited, bringing about another mtDNA controversy [128–134]. Much of the criticism of the rare paternal mtDNA inheritance in humans is centered around the existence of the (hypothetical) mega NUMTs (nuclear sequences resembling mtDNA). It has been argued that these sequences can be artifactually amplified in the course of sequencing library preparation and thus create an impression of heteroplasmy [132,135]. However, Luo et al. reasonably pointed out that their experimental setup was not conducive to such misinterpretation [136]. Our (admittedly superficial) BLAST analysis of the recently released first complete human genome sequence [137], did not reveal any mega-NUMTs that were both complete in length and nearly identical to mtDNA to an extent that would support the notion of possible artifactual nature of the results by Luo et al. [128]. Looking beyond humans, paternal mtDNA inheritance has been described in mice [138] and sheep [139], lending further credibility to the possibility of paternal mtDNA transmission in mammals.
Closely related to the subject of mtDNA damage and repair are issues of mtDNA mutagenesis and the role of acquired somatic mtDNA mutations in aging. The progressive accumulation of reactive oxygen species (ROS)-induced somatic mtDNA mutations with age formed the basis for the mitochondrial theory of aging (MTA) in one of several definitions of this theory. Harman first formulated the theory as the free radical theory of aging, in which free radical damage to cellular components was the driving force of aging [140]. Subsequently, it was reported that, in certain conditions in vitro, mitochondria might divert as much as 1–2% of their total electron flow to ROS production [141], which led to the notion of mitochondria being a biologic clock [142]. These high rates of mitochondrial ROS production were obtained with partial oxygen pressure and substrate concentrations much higher than those physiologically obtained, despite mitochondrial rates of ROS production being eventually revised down by an order of magnitude [143–146]. Furthermore, it was recognized that most cellular macromolecules such as proteins, RNA, and lipids are turned over and represent poor candidates for the progressive accumulation of damage with aging. In response to this criticism, Miquel and Fleming [147–149] introduced mtDNA as an oxidative-damage tally keeper. While MTA today is largely abandoned in its form that centers on mtDNA, some of its postulates are still used to justify the ongoing studies. Therefore, it may be useful to review some of these postulates and contradictory experimental evidence that led to their abandonment.
The extent of mtDNA’s accessibility to ROS remains unclear, and without accessibility the proximity appears irrelevant. It has also been demonstrated that mtDNA mutations lack the canonical ROS signature (G>T transversions) [150–152]. This is inconsistent with the leading role of ROS in mtDNA mutagenesis.
The protective role of histones has been impossible to elucidate directly in vivo because it is impossible to generate cells knocked out for all histones. Indirect studies indicate that, depending on the experimental system utilized, histones can either sensitize DNA to [153–155] or protect against [156–158] the ROS damage. Importantly, mitochondrial nucleoid proteins can be as protective as histones (reviewed in [105]). Therefore, the notion of histones’ “protective role” is speculative.
Base excision repair (BER) pathway is responsible for the repair of the bulk of oxidative DNA damage in both the nucleus and mitochondria. Moreover, oxidative damage in mtDNA may be repaired more efficiently than in the nucleus [159]. mtDNA damage that cannot be repaired is addressed by degradation of the damaged molecule and resynthesis [105,160]. These authors are not aware of any credible experimental evidence proving the notion that mtDNA mutation rates can be reduced by expanding the repertoire of DNA repair pathways available in mitochondria. These observations indicate that the lack of some DNA repair pathways in mitochondria could be inconsequential. Importantly, most substrates for BER and MMR do not induce replicative DNA base mispairing (do not directly lead to point mutations). Rather, they induce mutations due to the low fidelity of DNA polymerases involved in the repair or bypass of these lesions. Therefore, the lack of NER and MMR pathways in mitochondria may play a protective role against mutations, as counterintuitive as it may sound. Indeed, in the absence of NER, damaged mtDNA molecules are presumed to be channeled for degradation [161], thus avoiding error-prone repair. Therefore, the notion that a reduced repertoire of available DNA repair pathways is driving elevated rates of mtDNA mutagenesis is speculative.
The “vicious cycle” hypothesis is based on the assumption that the majority of mtDNA mutations result in mitochondrial dysfunction. This dysfunction, in turn, is necessarily accompanied by increased ROS production. This arrangement results in a “vicious” feed-forward cycle. This concept is incompatible with the experimentally observed unresponsiveness of ROS production to elevated levels of mtDNA mutations in mito-mice [162–164].
Zhong et al. described circulating cell-free mtDNA (mtDNAcf) in blood plasma more than two decades ago. [165]. This extramitochondrial mtDNA species has been suggested to have prognostic value in cancer, cardiac arrest, and severe sepsis [166,167]. Subsequently, mtDNAcf was identified as a major mediator of innate immunity and systemic inflammatory response syndrome (SIRS). In response to tissue damage (e.g., blunt-force trauma) mtDNA is released into plasma by an unknown mechanism. This results in toll-like receptor 9 (TLR9)-mediated neutrophil activation and systemic inflammation [168]. Controversially, while TLR9 is believed to be exclusively activated by DNA that lacks CpG methylation, a number of studies reported mtDNA methylation and even identified DNMT1 as a putative mediator of this methylation [169–175]. It is possible, however, that severe hypomethylation of mtDNA mediates its specific recognition by TLR9 [176,177]. Unexpectedly, mtDNA was also reported in the cytosol. Both strong insults (e.g., oxidative stress, bacterial or viral infection, etc.) and altered compaction of mtDNA in nucleoids resulting from TFAM haploinsufficiency were shown to promote cytosolic mtDNA release. [178]. This intracellular release of mtDNA has been implicated in cell-intrinsic innate immune responses [179,180]. Mechanistically, the cytosolic release of mtDNA could be mediated by Bax/Bac-mediated herniation of the inner mitochondrial membrane [181–183].
Our understanding of mtDNA and its contribution to biological processes continues its exponential growth. Things that were unthinkable less than two decades ago, such as mtDNA DAMPS, mtDNA control of the innate immunity, and mtDNA-derived peptides, are now an everyday reality. Yet, many basic mechanistic puzzles related to mtDNA replication, copy number control, transcription, etc., have proved remarkably difficult to solve and have bred controversy. Many of those difficulties are secondary to the insufficient resolution power of currently available analytical techniques. However, the continuously growing repertoire of new analytical and genetic technologies available to investigators bears the promise of resolving current controversies and even greater discoveries in the near future. |
PMC9648949 | 33111882 | Miki Taketomi SAITO,Luciana Souto MOFATTO,Mayra Laino ALBIERO,Márcio Zafallon CASATI,Enilson Antonio SALLUM,Francisco Humberto NOCITI,Karina Gonzales SILVÉRIO | Transcriptome profile of highly osteoblastic/cementoblastic periodontal ligament cell clones | 19-10-2020 | Cell differentiation,Clone cells,Sequence Analysis, RNA,Periodontal ligament,Osteoblasts | Abstract Heterogeneous cell populations of osteo/cementoblastic (O/C) or fibroblastic phenotypes constitute the periodontal dental ligament (PDL). A better understanding of these PDL cell subpopulations is essential to propose regenerative approaches based on a sound biological rationale. Objective Our study aimed to clarify the differential transcriptome profile of PDL cells poised to differentiate into the O/C cell lineage. Methodology To characterize periodontal-derived cells with distinct differentiation capacities, single-cell-derived clones were isolated from adult human PDL progenitor cells and their potential to differentiate into osteo/cementoblastic (O/C) phenotype (C-O clones) or fibroblastic phenotype (C-F clones) was assessed in vitro. The transcriptome profile of the clonal cell lines in standard medium cultivation was evaluated using next-generation sequencing technology (RNA-seq). Over 230 differentially expressed genes (DEG) were identified, in which C-O clones showed a higher number of upregulated genes (193) and 42 downregulated genes. Results The upregulated genes were associated with the Cadherin and Wnt signaling pathways as well as annotated biological processes, including “anatomical structure development” and “cell adhesion.” Both transcriptome and RT-qPCR showed up-regulation of WNT2, WNT16, and WIF1 in C-O clones. Conclusions This comprehensive transcriptomic assessment of human PDL progenitor cells revealed that expression of transcripts related to the biological process “anatomical structure development,” Cadherin signaling, and Wnt signaling can identify PDL cells with a higher potential to commit to the O/C phenotype. A better understanding of these pathways and their function in O/C differentiation will help to improve protocols for periodontal regenerative therapies. | Transcriptome profile of highly osteoblastic/cementoblastic periodontal ligament cell clones
Heterogeneous cell populations of osteo/cementoblastic (O/C) or fibroblastic phenotypes constitute the periodontal dental ligament (PDL). A better understanding of these PDL cell subpopulations is essential to propose regenerative approaches based on a sound biological rationale.
Our study aimed to clarify the differential transcriptome profile of PDL cells poised to differentiate into the O/C cell lineage.
To characterize periodontal-derived cells with distinct differentiation capacities, single-cell-derived clones were isolated from adult human PDL progenitor cells and their potential to differentiate into osteo/cementoblastic (O/C) phenotype (C-O clones) or fibroblastic phenotype (C-F clones) was assessed in vitro. The transcriptome profile of the clonal cell lines in standard medium cultivation was evaluated using next-generation sequencing technology (RNA-seq). Over 230 differentially expressed genes (DEG) were identified, in which C-O clones showed a higher number of upregulated genes (193) and 42 downregulated genes.
The upregulated genes were associated with the Cadherin and Wnt signaling pathways as well as annotated biological processes, including “anatomical structure development” and “cell adhesion.” Both transcriptome and RT-qPCR showed up-regulation of WNT2, WNT16, and WIF1 in C-O clones.
This comprehensive transcriptomic assessment of human PDL progenitor cells revealed that expression of transcripts related to the biological process “anatomical structure development,” Cadherin signaling, and Wnt signaling can identify PDL cells with a higher potential to commit to the O/C phenotype. A better understanding of these pathways and their function in O/C differentiation will help to improve protocols for periodontal regenerative therapies.
Periodontitis is a polymicrobial, infection-induced inflammatory disease in the periodontium characterized by connective attachment loss and alveolar bone destruction. Epidemiological studies indicate that periodontitis is still a globally prevalent disease. This periodontal disease may lead to functionally compromised dentition, which affects the quality of life of many subjects. In the last decade, several attempts have been made to regenerate the tissues impaired due to periodontitis, including bone replacement grafts, guided tissue regeneration, enamel matrix derivative, and combined therapy. However, these clinical approaches have not shown complete and predictable regeneration of periodontal tissues, namely cementum, periodontal ligament (PDL), and alveolar bone. Therefore, emerging regenerative approaches based on a biological rationale have been proposed to achieve improved clinical outcomes, such as enamel matrix derivative (EMD), recombinant human platelet-derived growth factor-BB (rhPDGF-BB)/beta tricalcium phosphate (b-TCP), and synthetic peptide-binding protein P-15/anorganic bovine bone matrix. Regenerative stem cell therapy has recently gained attention, since postnatal mesenchymal stem/progenitor cells can be isolated from the periodontal ligament and other dental tissues. These progenitor cells have been characterized by the expression of mesenchymal surface markers (CD105, CD146, CD166, CD73, and STRO1), low expression of hematopoietic stem cell markers (CD34 and CD45), and by having stem cell-like properties, including the capacity for self-renewal and multipotency. The periodontium is a complex structure composed of mineralized (cementum and alveolar bone) and non-mineralized tissues (PDL). Consequently, the regeneration of the periodontium requires a well-coordinated process of cell differentiation. However, a detailed understanding of periodontal-derived cells, which is crucial for these emerging approaches, remains unclear. It is known that PDL is constituted by heterogeneous cell populations. However, the molecular profile that distinguishes cells committed to osteo/cementoblastic (O/C) or fibroblastic phenotypes in PDL is still not fully understood. To date, some studies have suggested that cathepsin K is involved in PDL tissue homeostasis through stimulation of collagen fiber accumulation and inhibition of osteoblast differentiation of human PDL cells. Additionally, evidence suggests that the activation of the canonical Wnt signaling pathway enhances in vitro cementoblast differentiation of human PDL cells. Emerging methods using high-throughput sequencing technologies (such as the massive parallelization of RNA-seq) have broadened our view of the extent and complexity of the PDL transcriptome. For instance, RNA-seq analysis allows the detection and quantification of a broad range of transcripts and their splice-forms without requiring target specification, which leads to an unbiased and systematic approach to produce insights into important biological pathways and molecular mechanisms for cell regulation in a hypothesis-neutral environment. In our study, CD105-enriched PDL cell clones with osteoblastic/cementoblastic or fibroblastic potential were purified and had their transcriptomes compared after high-throughput RNA sequencing. Our hypothesis is that a comprehensive analysis of periodontium cells may shed light on how to promote an optimal microenvironment for periodontal mineralized and non-mineralized tissue formation. Finally, we expect that our results help in the development of more predictable outcomes for future regenerative approaches.
The study design and procedures were approved by the Institutional Review Board of Piracicaba Dental School – State University of Campinas (#053/2013). Unsorted PDL cells and PDL-CD105+ enriched populations from permanent teeth were isolated and characterized in a previously published study, in which participants signed an informed consent form. In short, CD105+-enriched PDL cell subsets were obtained using the magnetic cell sorting system (MACS, Milteny Biotech, Germany) following the manufacturer’s recommendations. To confirm the expression of mesenchymal cell-surface markers, flow cytometry was performed as previously described. The cell suspension was obtained by detaching monolayers of PDL-CD105+ cells with 5 mg/mL of Collagenase IV (Gibco, USA) and 5mM EDTA (Applied Biosystems, USA), and resuspended in blocking buffer for 20 minutes with 10% normal donkey serum (Sigma). Cells (1 × 10) were incubated with mouse anti-human monoclonal antibodies against CD105-allophycocyanin (eBioscience, USA), CD146-allophycocyanin (BioLegend, USA), CD166-phycoerythrin (BD Bioscience, USA), CD34-fluorescein isothiocyanate (BD Bioscience, USA), CD45-peridinin chlorophyll (BD Bioscience, USA), Stro-1 Alexa Fluor 647 (BioLegend, USA), or isotype-matched control IgGs /IgM for 40 min at 4°C. A FACScan instrument (BD FACSCalibur™; BD Bioscience Pharmigen, USA) was used for quantitative fluorescence-activated cell sorter (FACS) analysis, and the results were processed using CELLQUEST software (BD Bioscience Pharmigen, USA)
As previously described, only one cryovial from the PDL-CD105+ cell population was used for cloning isolation through the ring-cloning technique. In total, 250 cells (passage 2) were seeded into 100-mm dishes and incubated at 37°C, 5% CO2, in a standard medium composed by Dulbecco’s modified Eagle medium-high glucose (DMEM) supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 mg/mL) (Gibco, USA). Individual clones were allowed to develop for 14 to 21 days until they reached approximately 50 cells per colony. Then, the ring-cloning technique was performed by placing 8-mm-diameter cylinder polystyrene rings (Millipore, USA) around each colony. Lastly, the cells were detached with 0.05% (w/v) trypsin and 0.05 mM (w/v) EDTA (Gibco, USA), transferred to 24-well plates, and recultured as above.
To assess the ability of in vitro mineralized matrix formation, unsorted PDL cell populations, PDL-CD105+ enriched populations, and PDL-CD105+ cell clones were seeded (2 × 10cells/well) in 24-well plates for 24 h with standard medium (Control) composed of Dulbecco’s modified Eagle medium-high glucose (DMEM). The medium was supplemented with 10% FBS, penicillin (100 U/ml) and streptomycin (100 mg/mL) (Gibco, USA), and then cells were incubated for 24 h at 37°C and 5% CO2. Subsequently, cells were cultivated in fresh standard medium or osteogenic medium (OM), composed of standard medium supplemented with 50 mg/mL ascorbic acid, 10 mM b-glycerophosphate, and 10- M dexamethasone (Sigma-Aldrich, USA). After 28 days of the induction period, we performed the Alizarin Red staining (AR-S, Sigma-Aldrich, USA) assay as described elsewhere. Cell clones that formed a mineralized matrix in vitro were classified as clones of osteo/cementoblastic (O/C) phenotype (C-O). In contrast, cell clones that could not form a mineralized matrix in vitro were named fibroblastic phenotype (C-F).
For the metabolic analysis, cell clones were seeded (5 × 10 cells/well) in a 96-well plate (Corning Costar, USA) using standard medium and incubated in a humidified incubator at 37°C and 5% CO2 for 24 h to allow cell adhesion to the discs. Thenr, the medium was changed for DMEM supplemented with 2% FBS, penicillin (100 U/ml), and streptomycin (100 mg/mL). This time point was considered as the baseline (time 0h) for the metabolic assay. The media was then replaced on days 3 and 7, and the metabolic activity of the cell on the experimental groups was evaluated at days 1, 3, 7, and 10, as previously described using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium (MTT, Life Technologies, USA) assay.
Each cell clone was seeded, and RNA extraction was performed as previously described. RNA isolated from two C-O clones and two C-F clones cultivated in the standard medium during 14 days were subjected to RNA-seq, and each clone was considered a biological replicate for C-O and C-F group. RNA-seq was performed using Illumina TruSeq RNA Sample Preparation kit v2 (Illumina, USA), according to the manufacturer’s instruction. For RT-qPCR, single-stranded complementary DNA (cDNA) was synthesized from 1 µg total DNA-free RNA using Transcriptor First Strand cDNA synthesis kit (Roche Applied Science, USA) following the manufacturer’s recommendations. RT-qPCR was performed using the samples of cDNA and LightCycler 480 SYBR Green I master kit on the LightCycler 480 II real-time PCR system (Roche Applied Science, USA) for primers sequences WNT2, WNT2B, WNT16, WIF1, PCDHGA10, BMP4 and GAPDH (Table 1). Distilled water (no template control) was used as a negative control for all experiments. Relative quantification of reaction products was accomplished to GAPDH and estimated by the ΔCT-method.
All RNA-seq data generated in our study are available at the GEO repository (http://www.ncbi.nlm.nih.gov/geo/; accession #GSE94599). For RNA-seq data, the quality of raw data was evaluated by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Data were filtered by quality using Perl scripts with a 20 quality score threshold. The adapters were removed with Cutadapt and trim galore (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Filtered reads of the RNA library were mapped against the human genome (GRCh38) using the pipeline Tophat2-Cufflinks. The number of reads aligned per genes and fragments per kilobase of transcript per million mapped reads (FPKM) were estimated by RSEM program. Differentially expressed genes (DEG) between C-O and C-F clones were obtained by DESeq and EdgeR packages (R/Bioconductor) with α=5% and |log2FC|≥1. Heatmaps were then generated based on z-score values estimated from FPKMs values of DEG using the heatmap package on R. DEG between C-O and C-F clones were subjected to functional annotation using the DAVID program (Database for Annotation, Visualization, and Integrated Discovery), version 6.8 (https://david.ncifcrf.gov/), to identify enriched terms of Gene Ontology (GO). GO analysis was generated with the DAVID software based on biological processes (GO_TERM_BP_2 database). Pathway overrepresentation analysis was performed in the Panther Classification System (http://pantherdb.org/). We only considered the enriched GO terms and pathways generated by a modified Fisher Exact test followed by the Bonferroni test and p-value threshold of <0.05. For other experiments, data were expressed as mean ± standard deviation (SD). T-test was used to analyze differences between two groups, and one-way or two-way analysis of variance (ANOVA) (α=0.05) was used to analyze differences among three or factorial analyses, respectively.
Since we aimed to isolate clones with distinct O/C differentiation potentials from a PDL-CD105+-enriched population, the biomineralization potential of this population was evaluated and compared to the total pool of cells obtained from PDL from third molars. The PDL-CD105+-enriched population exhibited a high proportion of cells that expressed mesenchymal stem cell (MSC)-related markers (Figure 1A). However, the capacity for biomineralization of the PDL-CD105+-enriched population was not statistically higher than the unsorted population (PDL) (Figures 1B and 1C).
We obtained a total of 46 cell clones from the PDL-CD105+ mesenchymal progenitor population. According to the AR-S assay, two of the 46 clones showed a significantly higher potential to form a mineralized matrix in vitro under OM induction, namely G13 and G48, and were defined as clones of O/C phenotype (C-O) (Figures 2A and 2B). The remaining 44 clones showed a lower ability to form a mineralized matrix in vitro and were classified as clones of the fibroblastic phenotype (C-F). In the C-F group, two clones that rapidly expanded during clonal expansion were selected to represent the C-F group, namely clones G16 and G23 (Figure 2A). The C-F and C-O groups were evaluated for their metabolic activity and showed no significant difference at any time point (Figure 2C). Both clone groups showed increased metabolic activity on day 3 (Figure 2C).
All RNA-seq data showed in our study are available at GEO repository (http://www.ncbi.nlm.nih.gov/geo/; accession #GSE94599). FastQC analysis showed that ~84% of the reads were at optimum quality and aligned to the human genome. Only aligned reads were retained for further analyses. As the heatmap shows, C-O clones presented a higher transcriptional activity (Figure 3). Out of 235 DEG, 193 were significantly upregulated in the C-O group compared to C-F, and 42 genes were significantly downregulated in the C-O group (Figure 3).
To investigate the differences between C-O and C-F clones, DEG were analyzed for pathway overrepresentation. C-O clones showed upregulated genes related to 55 pathways, from which the Cadherin signaling pathway (P00012) and Wnt signaling pathway (P00057) were significantly enriched according to the Panther Classification System (Table 2). Downregulated genes in C-O clones were associated with 10 pathways, but no significantly overrepresented pathway was observed compared to Homo sapiens genome background (data not shown). To understand the biological context of DEG, GO analysis was used to map the biological processes enriched in DEG. Genes upregulated in C-O clones showed significant enrichment in 20 biological processes compared to Homo sapiens genome background (Figure 4A). Among the significantly enriched biological processes in C-O clones compared to C-F clones, the biological process “anatomical structure development” (GO: 0048856) harbored genes related to the Wnt pathway: WNT2, WNT2B, WNT16, and WIF1 (Figure 4B). Moreover, the biological process “cell adhesion” (GO:0007155) included the gene PCDHGA10, which is related to Cadherin pathway, and BMP4, which is linked to TGFβ/BMP pathway (Figure 4C).
The significantly upregulated genes WNT2, WNT2B, WNT16, WIF1, PCDHGA10, and BMP4, found after RNA-seq analyses, were then selected for RT-qPCR validation. The data showed that C-O clones presented significantly higher expression of WNT2, WNT16, and WIF1 (Figures 5A-C). Although a trend of higher expression in C-O clones was observed, the genes WNT2B, PCDHGA10, and BMP4 presented no significant difference of expression between C-O and C-F clones (Figures 5D-F).
The effective regeneration of the periodontium depends on the formation of mineralized and non-mineralized tissues in an intimate relationship. Although PDL contains heterogeneous cell subpopulations, the molecular characteristics that distinguish these subpopulations remain unclear. Attempts to isolate a more purified population by MSCs surface markers, such as CD105 (endoglin), have been reported for periodontal ligament-derived stem cells. However, the heterogeneity of MSC proliferative and differentiation capacities may not be explained only based on surface markers. Our study showed that PDL-CD105+ enriched cell populations and unsorted PDL cells present a similar ability to form mineralized nodules under osteogenic conditions. In agreement with our previous study, we obtained a PDL-CD105+ enriched population that remained with two distinct cell subsets, committed to fibroblastic phenotype (44 of 46 clones) or O/C phenotype (two clones, only). Therefore, since the characteristics of a given cell are determined by patterns of gene expression, we expanded the investigation about PDL-CD105+ clones using next-generation sequencing technology to understand what distinguishes a cell line that is more prone to differentiate into an O/C phenotype or to a fibroblastic phenotype. The use of a label-free approach enables the most inclusive and unbiased description of genes. This approach has been used to describe many types of cells, including osteoblasts and MSC. Despite the existence of previous descriptions of a group of important transcripts to PDL, high throughput analysis of human PDL clones with distinct differentiation capacity remais unexplored. To the best of our knowledge, this is the first study to analyze primary human PDL cell clones on a comprehensive scale using RNA-seq. Furthermore, our study showed that, in a heterogeneous cell population derived from PDL, cell clones prone to differentiate into O/C phenotype (C-O clones) have a more active transcriptional profile compared to clones related to fibroblastic phenotype (C-F clones), even when cultivated in standard conditions, such as without osteogenic-inducing mediums. Moreover, genes related to anatomical structure development and Cadherin and Wnt signaling pathways – such as WNT2, WNT16, and WIF1 – allowed us to distinguish PDL cells profile with higher potential to commit to the O/C phenotype. Wnt signaling is critical for the homeostatic regulation of craniofacial tissue, including PDL and alveolar bone. Wnt/β-catenin signaling pathway drives mesenchymal progenitor cell differentiation towards osteoblastic phenotype through inhibition of chondroblastic differentiation. However, after osteoblastic differentiation, the Wnt signaling pathway is downregulated, and the continuous stimulation of this pathway may inhibit mineralization. In addition, the upregulation of WNT2 in dental follicles cells at early time points may promote the commitment to O/C progeny before the acquisition of a more mature phenotype. Furthermore, WNT16 is associated with regulation of cortical bone homeostasis and induces expression of osteoprotegerin, which suggests that WNT16 expression can inhibit osteoclastogenesis. Accordingly, we observed that periodontal ligament cells more prone to O/C differentiation presented high expression of WNT2 and WNT16, demonstrating the significance of these genes to initial commitment towards the O/C phenotype. We also found that WIF1 was one of the most significantly upregulated genes in C-O clones compared to C-F clones. Although Panther analysis did not correlate WIF1 to the Wnt signaling pathway, previous reports classify this gene as a Wnt antagonist. In a recent study comparing murine osteoblasts and cementoblasts, the inhibitor of the Wnt pathway, Wif1, was found to be upregulated in cementoblasts when compared with osteoblasts and poorly expressed in PDL cells. Our data suggest that clonal C-O cells are more prone to cementoblastic differentiation; however further investigation is required to confirm this hypothesis. Wnt pathway alone may not be enough for maturing bone, and other signaling pathways, such as TGF-β/BMPs, and Cadherin pathways may interact with the Wnt pathway to control O/C differentiation. It has been shown that BMP2 requires the activation of canonical Wnt signaling at the early stage of differentiation of murine dental follicle cell line (SVF4 cells) along O/C phenotype. Additionally, at the time of follicle cell maturation, BMP2 promotes a negative Wnt-feedback loop by increasing expression of Wnt pathway inhibitors, including Wif1, Dkk1, and Sfrp4.BMP4, another member of TGF-b/BMPs superfamily, also plays an important role in the process of bone nodule formation as already described to BMP2. Our results showed that the expression of BMP4 was constitutively increased about six-fold in C-O compared to C-F cell clones. Similar data were found in immortalized PDL cell clones, in which clones that presented high expression of BMP4 showed intrinsic ability to form mineralized tissue in vitro, whereas another clone that did not express BMP4 was unable to form mineral nodules. BMP4 is considered one of the most predictive gene expression markers of in vivo bone formation. Moreover, it has been reported that BMP4 also interacts with the Wnt signaling pathway during tooth organogenesis. Higher BMP4 levels are essential to overcome the inhibitory effects of Wnt antagonists, such as WIF1 and DKK2, during tooth development beyond bud stage. Altogether, we supose that BMP4 expression in C-O cell clones may be related to the capacity of these clones to acquire the O/C phenotype through functional interactions with the Wnt signaling pathway. However, further experiments are necessary to elucidate how Wnt-BMP interactions affect the O/C maturation process in these cell clones. The Cadherin superfamily was significantly overrepresented in clones with the potential to differentiate into the O/C phenotype. Studies have reported that the cross-talk between Cadherin and Wnt signaling regulates the mechanism underlining osteoblast differentiation. Cadherins are suggested to bind to β-catenin, hindering its translocation to the nucleus, thus reducing canonical Wnt signaling. Cadherins also interact with Wnt co-receptor lipoprotein receptor-related protein 5 (LRP5), which is essential to regulate bone mass. In consistence with these findings, our study showed that C-O clone cells presented nine upregulated genes common between Cadherin and Wnt pathways, suggesting an interaction of these two pathways in the regulation of O/C cell lineage commitment localized into the periodontal dental ligament. In short, we provided a comprehensive assessment of the transcriptome of human PDL progenitor cell clones with high O/C differentiation potential using a next-generation sequencing technology (RNA-seq). These findings evidence that Wnt pathway-related genes are critical for identifying PDL cells committed towards O/C differentiation. Further studies are necessary to shed light on the mechanisms of action and the extent of this process of differentiation. A better understating of the molecular regulation of PDL cells committed to the O/C phenotype offers the potential to improve protocols for periodontal regenerative therapies. |
PMC9648953 | 31508795 | Cristine D'Almeida Borges,Milla Sprone Ricoldi,Michel Reis Messora,Daniela Bazan Palioto,Sérgio Luís Scombatti de Souza,Arthur Belém Novaes,Mario Taba | Clinical attachment loss and molecular profile of inflamed sites before treatment | 30-08-2019 | Periodontal attachment loss,Biological markers,Gingival crevicular fluid,Biopsy,Gene expression | Abstract Objective: To monitor early periodontal disease progression and to investigate clinical and molecular profile of inflamed sites by means of crevicular fluid and gingival biopsy analysis. Methodology: Eighty-one samples of twenty-seven periodontitis subjects and periodontally healthy individuals were collected for the study. Measurements of clinical parameters were recorded at day −15, baseline and 2 months after basic periodontal treatment aiming at monitoring early variations ofthe clinical attachment level. Saliva, crevicular fluid and gingival biopsies were harvested from clinically inflamed and non-inflamed sites from periodontal patients and from control sites of healthy patients for the assessment of IL-10, MMP-8, VEGF, RANKL, OPG and TGF-β1 protein and gene expression levels. Results: Baseline IL-10 protein levels from inflamed sites were higher in comparison to both non-inflamed and control sites (p<0.05). Higher expression of mRNA for IL-10, RANK-L, OPG, e TGF-β1 were also observed in inflamed sites at day −15 prior treatment (p<0.05). After the periodontal treatment and the resolution of inflammation, seventeen percent of evaluated sites still showed clinically detectable attachment loss without significant differences in the molecular profile. Conclusions: Clinical attachment loss is a negative event that may occur even after successful basic periodontal therapy, but it is small and limited to a small percentage of sites. Elevated inflammation markers of inflamed sites from disease patients reduced to the mean levels of those observed in healthy subjects after successful basic periodontal therapy. Significantly elevated both gene and protein levels of IL-10 in inflamed sites prior treatment confirms its modulatory role in the disease status. | Clinical attachment loss and molecular profile of inflamed sites before treatment
To monitor early periodontal disease progression and to investigate clinical and molecular profile of inflamed sites by means of crevicular fluid and gingival biopsy analysis.
Eighty-one samples of twenty-seven periodontitis subjects and periodontally healthy individuals were collected for the study. Measurements of clinical parameters were recorded at day −15, baseline and 2 months after basic periodontal treatment aiming at monitoring early variations ofthe clinical attachment level. Saliva, crevicular fluid and gingival biopsies were harvested from clinically inflamed and non-inflamed sites from periodontal patients and from control sites of healthy patients for the assessment of IL-10, MMP-8, VEGF, RANKL, OPG and TGF-β1 protein and gene expression levels.
Baseline IL-10 protein levels from inflamed sites were higher in comparison to both non-inflamed and control sites (p<0.05). Higher expression of mRNA for IL-10, RANK-L, OPG, e TGF-β1 were also observed in inflamed sites at day −15 prior treatment (p<0.05). After the periodontal treatment and the resolution of inflammation, seventeen percent of evaluated sites still showed clinically detectable attachment loss without significant differences in the molecular profile.
Clinical attachment loss is a negative event that may occur even after successful basic periodontal therapy, but it is small and limited to a small percentage of sites. Elevated inflammation markers of inflamed sites from disease patients reduced to the mean levels of those observed in healthy subjects after successful basic periodontal therapy. Significantly elevated both gene and protein levels of IL-10 in inflamed sites prior treatment confirms its modulatory role in the disease status.
Periodontal disease is a chronic microbial infection characterized by the inflammation of supportive tissues and alveolar bone loss. Particularly in chronic periodontitis, the presence of local irritants is compatible with the severity of the disease. Although bacteria are essential in the onset and maintenance of periodontitis, susceptibility and disease progression are determined by a complex interaction driven by the modulation of an immune-inflammatory host response., Locally, bacterial lipopolysaccharides induce inflammatory cells to release pro-inflammatory mediators that seem to act in the destruction of periodontal tissues. The presence of inflammatory cells and lymphocytes infiltration, chemotactic factors involved in recruiting these cells and cytokines involved in the pathogenesis and progression of the periodontal disease. The activation of a local immune response by T helper cells would determine the stability or progression of the periodontal disease. Th1 lymphocytes are characterized by the secretion of cytokines involved in eradicating intracellular pathogens, whereas Th2 cells are responsible for secreting cytokines involved in eliminating extracellular micro-organisms. Also, Th17 and T regulatory (Treg) cells are involved in disease progression. Th17 subset presents pro-inflammatory and pro-resorptive activities, especially for secretion of IL-17 and RANKL, both involved in the differentiation and activation of osteoclasts. On the other hand, Treg cells subset displays suppressor functions producing IL-10 and transforming growth factors (TGF-β1)., In this context, IL-10 seems to have a modulatory role on inflamed and progressive sites. Host modulatory effects of specific cytokines such as IL-10, IL-13, OPG and TGF-β1 are responsible for the selective recruitment of different cells, cytokines production and may determine the disease progression. These cytokines associated to host defense have been identified in saliva,, blood,, gingival crevicular fluid, and gingival tissues., Elevated levels of these molecules may be related to periodontal disease condition, allowing identification and controlling patients with periodontal disease. Studies that aim to analyze cytokines host modulatory effect during disease progression seem to be promising in periodontal diagnostic. Therefore, in this study, we aimed to monitor early changes in attachment levels of progressive sites and investigate clinical and molecular features of progressive sites through saliva, gingival crevicular fluid and gingival tissue samples.
Twenty-seven participants were selected; amongst them eighteen presented periodontitis stage II grade B (periodontitis group) and nine were healthy (control group). Post hoc power analysis was made through G*Power 3.1.9.2 using mean and standard deviations of the total amount level of IL-10 in inflamed and control sites, and 99% of power was obtained in this study. Participants were chosen from the dental clinics of the Ribeirão Preto School of Dentistry and were invited to take part in the study. All enrolled patients gave written consent on a form approved by the Ethics Committee Protocol of the Ribeirão Preto School of Dentistry -USP (approval number # 02841912.0.0000.5419). Participants underwent anamnesis, clinical and radiographic examination. Included participants had at least 14 natural teeth and posterior occlusion stability. Participants in the chronic periodontitis group were at least 35 years old with 5 teeth presenting probing depth (PD) of ≥5 mm and clinical attachment loss of ≥3 mm. Participants in the control group had PD ≤3 mm in all teeth and plaque index and bleeding on probing values ≤20%. Participants presenting any disorder or ongoing medication usage were excluded. Also, they could not have received periodontal treatment in the past six months.
Clinical examinations and data collections were performed at day −15, baseline and two months after basic periodontal therapy. Figure 1 illustrates the timeline of the study. Probing pocket depth (PPD), relative clinical attachment level (rCAL) and bleeding on probing (BOP) were recorded at six sites per tooth (mesio-buccal, buccal, disto-buccal, mesio-lingual, lingual and disto-lingual) with the aid of a computerized periodontal probe (Florida Probe Corporation, Gainsville, FL, USA). The presence or absence of biofilm at four sites per tooth (plaque index -PI) were also recorded. It was also verified the furcation involvement with the aid of a manual periodontal probe (Hu-Friedy, Chicago, IL, USA). After clinical examinations of day −15 and baseline, sites were categorized according to the presence or absence of inflammation: (i) inflamed sites (PD ≥ 5 mm and recurrent BOP after clinical exams at −15 days and baseline); (ii) non-inflamed sites (PD ≤4 mm without BOP after clinical exams at −15 days and baseline); (iii) and control sites (PD ≤3 mm without BOP after clinical exams at −15 days and baseline). For matching comparison purpose, inflamed sites and non-inflamed sites were from the same participant (periodontitis group) for gingival crevicular fluid and gingival biopsy analysis. Scaling and root planning sessions were performed by the same operator in two to four sessions within 24- to 48-hour interval using hand instruments (Hu-Friedy, Chicago, IL, USA) and an ultrasonic (Dentsply, York, PA, USA) device. Oral hygiene was reviewed after a week and repeated 30 days after periodontal disinfection, followed by dental prophylaxis. After two months, a new periodontal examination was performed to evaluate PI, BOP, PD and rCAL using a computerized probe to detect progressive sites. Progressive sites categorization was based on the tolerance method., In brief, progressive sites were those that presented clinical attachment loss of ≥1 mm after two months considering the average error of 0.3 mm of the electronic probe multiplied by 3. Scaling and root planning sessions, clinical examinations and data collections were made by only one examiner, who is an experienced Periodontist (Borges, C.D.).
The patients were instructed not to drink or eat for at least 60 min before the saliva sample collection. Non-stimulated whole expectorated saliva was collected (~3 ml) from each subject into sterile tubes, according to the method described by Navazesh (1993), by one calibrated examiner the day after the initial diagnosis and on the day after post-therapy periodontal evaluation. Saliva samples were placed on ice immediately and aliquoted prior to freezing at −80°C. The salivary inflammatory protein levels were identified simultaneously using Multiplex Cytokine Profiling Assay in the Luminex platform (Luminex Corporation, Austin, TX, USA). The following proteins were analyzed: IL-10, MMP-8, VEGF, RANKL, OPG and TGF-β1. The assay was performed according to the manufacturer's protocol. Briefly, ten microliters of the diluted sample (proteins) were added to a 50 µl cocktail of capture beads and an antibody detector, and the mixture was incubated for 4 hours at room temperature. Excess unbound antibody detector was washed off and flow cytometric analysis were performed using the appropriate CMA analysis software.
Gingival crevicular fluid samples were collected at baseline, 15 days and 2 months after therapy. In periodontitis group patients, gingival crevicular fluid samples were collected from three inflamed sites and one non-inflamed site. In control patients, fluid samples were collected from one control site. First, the supragengival plaque was removed, sites were isolated with cotton rolls and gently air dried. Fluid samples were collected with sterile Periopaper strips (Oraflow Inc., Planviwe, NT, USA) that were inserted into the gingival crevice until mild resistance was felt and left in place for 30 seconds. After gingival crevicular fluid collection, strips were placed in Eppendorf vials and immediately frozen at −80°C until use. Gingival crevicular fluid samples were placed into 60 µl of sodium phosphate buffer (Life Technologies, Carlsbad, California, USA) and 0.01 ml of Tween® 20 (USB Corporation, Cleveland, USA). Protein levels of IL-10 and VEGF were identified simultaneously using multiplex cytokine profiling assay (Luminex Corporation, Austin, TX, USA). MMP-8 levels were analyzed by ELISA and carried out according to the manufacturer's instructions.
For collecting gingival tissue samples (containing both epithelial and connective tissues), all patients received local anesthesia. In periodontitis group patients, samples were harvested from one inflamed and one non-inflamed site. In Control patients, samples were removed from one control site. The gingival biopsies were harvested from the same site that had the gingival crevicular fluid collected. Two incisions were made for samples collection. First, the initial incision was made 1.5 mm away from the tooth with a scalpel, until bone crest. Then, an intracrevicular incision was made for gingival tissue removal that consists of periodontal pocket/gingival sulcus wall. Incisions were made around the selected sites, not around the tooth. In patients from periodontitis group, samples were removed during periodontal treatment, before scaling and root planning. In patients from control group, samples were removed during surgical procedures as root coverage. These samples were immediately flash frozen in liquid nitrogen then preserved under −80°C for posterior RNA extraction and gene expression analysis of IL-10, MMP-8, VEGF, RANKL, OPG e TGF-β1.
Total RNA from biopsies was extracted using the Trizol reagent (Invitrogen, Milan, Lombardy, Italy) method. The aqueous phase was transferred to a new tube, to which 0.25 ml of 95% ethanol (Sigma, St Louis, MO, USA) was added. The suspension was transferred to the spin basket assembly of the kit (Promega, Madison, WI, EUA) and centrifuged at 10,500 rpm for 1 min at 4°C. From 1 µg of total RNA, a strand of complementary DNA (cDNA) was synthesized through a reverse transcription reaction (SABioscience, Frederick, MD, USA). Reactions were carried out in triplicate for each sample (inflamed sites, non-inflamed sites and control sites). The reactions were performed on a real-time thermocycler (Life Applied Biosystems, Carlsbad, California, USA), according to the directions supplied by the manufacturer. Following sample amplification and calculations, the expression levels were determined.
Data were grouped by average and their respective standard deviation. Specific sites and individuals were considered for parametric or non-parametric statistical analysis when appropriated after Lilliefors normality test.
For intra-group comparison, before and after treatment, Wilcoxon test or t test was applied. For intergroup comparison, Mann-Whitney test or t test was applied. A significance level of 5% was adopted for all statistical analyses (P<0.05).
For intra- and intergroup comparison, at baseline, 15 days and 2 months, Kruskal-Wallis test or ANOVA was applied.
Differential expression calculation was done by a specific software for data analysis (SABiosciences, Frederick, MD, USA). Relative gene expression normalization and quantification were performed by 2-ΔΔCT method. This software also performed pair-wise comparisons between groups of experimental replicates and defined fold-change and statistical significant thresholds. Therefore, data were presented as a difference (fold regulation) in gene expression, which would be normalized by the geometric mean value of actin-beta (ACTB). Significance level was set at p<0.05.
The subjects’ demographic data are displayed in Table 1. There was a higher prevalence of women and Caucasians in our sample. At baseline, periodontitis and control groups had different mean values of clinical parameters (Table 1). After basic periodontal therapy, periodontitis group showed a significant improvement in the clinical parameters (p<0.05). Significant differences between inflamed and non-inflamed sites for PD, rCAL and BOP, and between inflamed and control sites for PD and PI (p<0.05) (Table 2) were also observed. 2,436 sites from periodontitis group were analyzed and after periodontal therapy, 17% of total sites showed progressive clinical attachment loss (p<0.05). Comparisons of clinical measurements between −15 days and baseline, without any interventional therapy, showed difference in PD (5.6±0.85 and 5.9±1.30, respectively) in inflamed sites, but not significant (p=0.37). For non-inflamed sites (2.7±0.6 and 2.5±0.9, respectively), difference was also not significant (p=0.39).
In the baseline, higher expression of RANK-L in periodontitis group 2.99 pg/mL in comparison to control group 1.2 pg/mL (p=0.0313) was observed. OPG protein expression was higher in periodontitis group before therapy. After 2 months, a 40% reduction was observed (p=0.0002).
Eighty-one samples were included for the gingival crevicular fluid analyses. IL-10, VEGF and MMP-8 were detected in gingival crevicular fluid collected at baseline, 15 days and 2 months (Figure 2). Our data showed a higher total amount of VEGF in inflamed sites in comparison to non-inflamed sites at all times. There were no differences between baseline and 2 months in all sites. The total amount of IL-10 was higher in inflamed sites in comparison to non-inflamed sites at all times (p<0.05). Also, non-inflamed sites showed higher amounts of IL-10 in comparison to control sites at all times (p<0.05). The total amount of MMP-8 had a reduction 15 days after periodontal therapy, but not statistically significant, and the total amount was higher in inflamed sites after two months (p<0.05). Also, it was higher in control sites in comparison to non-inflamed sites after 15 days (p<0.05).
We examined the expression of IL-10, RANKL, OPG, MMP-8, VEGF, and TGF-β1 in inflamed, non-inflamed and control sites after periodontal therapy. Comparisons between inflamed sites and non-inflamed sites, showed increased expression of IL-10 (p=0.03), RANKL (p<0.001) OPG (p=0.02), and TGF-β1 (p<0.05) in inflamed sites. Control sites demonstrated higher expression of OPG (p<0.001) and TGF-β1 (p<0.05) when compared to non-inflamed sites. Inflamed sites had higher expression of IL-10 when compared to control sites (p=0.026). MMP-8 and VEGF showed no differences (Figure 3).
In the present study, we monitored inflammation and progressive periodontal sites to investigate potential differences in the molecular profile of gingival crevicular fluid and gingival biopsies from inflamed and non-inflamed sites. Groups and sites were categorized in order to express significant clinical differences measured by periodontal parameters (PD a rCAL) and inflammation (BOP). Additionally, early changes in the clinical attachment levels were used to investigate the role of inflammatory markers in disease modulation. Samples were collected at baseline, 15 days and 2 months after basic periodontal therapy. As expected, our data showed significant difference in clinical parameters between periodontitis group and control group at baseline. After periodontal therapy, data showed significant improvements on clinical parameters in periodontitis group. It was observed reduction in PD (0.7 mm ±0.4), BOP (37.1%±5.0), PI (27.2%±7.3), and rCAL gain (0.9 mm ±0.5). These results confirm the short-term beneficial effect of the therapy and are in accordance with previous data that showed better clinical conditions after full mouth disinfection, or scaling and root planning over a 3- to 4- week period., Inflamed sites showed higher amount of IL-10 (0.29 pg ±0.09) in comparison to control sites (0.21 pg ±0.08) before treatment (p<0.05). Furthermore, IL-10 mRNA expression was higher in inflamed sites in comparison to non-inflamed and control sites. This is in accordance to some previous results.– Goutoudi, et al. (2004) using a different methodology observed a similar amount of IL-10 when compared diseased and non-diseased sites instead of the inflamed sites classification of our study. Periodontal disease activity is accepted as bone and attachment loss related to variations in inflammatory cells, migration of monocytes/macrophage and has been associated to inflammatory biomarkers., , Our results found that 17% of total sites could be classified as progressive, according to the tolerance method., , However, we did not find differences in the protein levels of MMP-8, VEGF and IL-10 in gingival crevicular fluid of progressive sites compared to inactive sites after therapy. Indeed, no association was observed between bleeding on probe and disease progression. A previous study observed a relationship between bleeding on probe and disease activity, but it is yet controversial and other authors showed similar results to ours. Interestingly, the higher expression of MMP-8 in inflamed sites observed in our study may explain why progressive sites also demonstrated higher IL-10 levels. The anti-inflammatory effect of IL-10 decreases the expression of pro-inflammatory cytokines, like TNF-alfa, IL-1β and matrix metalloproteinases (MMPs). Because of its protective function against bone loss, IL-10 inhibits MMPs through the up-regulation of Tissue Inhibitor of Metalloproteinase (TIMPs) that are capable of inhibiting almost every member of the MMP family Thus, the higher expression of IL-10 in inflamed sites may have moderated the destructive effect of Th1 response and may have been accounted for lowering the expression of MMP-8. Although clinical results demonstrated periodontal pocket reduction after periodontal therapy, some sites remained with probing depth >4 mm. This can explain the increase in MMP8 levels in 2 months, although its reduction after 15 days. Remaining periodontal pocket could increase inflammatory cytokines. Furthermore, our site-specific analysis presented higher expression of RANKL mRNA in inflamed sites compared to non-inflamed sites. Inflamed sites also had higher expression of OPG mRNA compared to non-inflamed sites and, consequently, relative ratio RANKL/OPG mRNA was higher. Garlet, et al. (2004) observed higher expression of RANKL mRNA in chronic periodontitis patients compared to healthy patients, as well as higher expression of IL-10 mRNA. The expression of OPG was also higher, but not significant. According to the authors, higher expression of OPG could control the alveolar bone loss driven by RANKL, attenuating the progression and severity of the disease. The expression of RANKL and MMPs may result in tissue destruction and disease progression, whereas the higher expression of TIMPs and OPG possibly induced by IL-10, could be responsible for the control of tissue destruction. Indeed, these results are in agreement to ours and suggest that, in higher amounts, IL-10 could control bone resorption and moderate periodontal destruction. We also found higher expression of TGF-β1 mRNA in inflamed sites compared to non-inflamed sites (p<0.05). Dutzan, et al.41 (2012) observed higher expression of TGF-β1 in healthy sites compared to periodontitis sites, which in our study showed no difference. Unlikely, we found higher expression of TGF-β1 mRNA in inflamed and control sites compared to non-inflamed sites, probably indicating the anti-inflammatory characteristic and modulatory role of TGF-β1 in inflamed sites, possibly promoting modulation of pro-inflammatory cytokines and stimulating the production of IL-1 receptor antagonist, which regulates anti-inflammatory and immunesupressor activities. Regarding VEGF, we found significant difference between inflamed sites and control sites at all times. This result is subject to bias given gingival tissue samples collected from sites that received periodontal therapy. Besides, the presence of VEGF in gingival fluid of healthy patients may reflect sub-clinical levels of inflammation, healing following bacterial assault or physiological angiogenesis in periodontal tissues. Despite having some sites with periodontal disease progression, our site-specific analysis also showed considerable levels of anti-inflammatory markers, possibly reducing risk for more attachment loss. In conclusion, in spite of data analysis limitations and the short follow-up period to appreciate major disease breakdown, this preliminary study stressed out that progressive disease activity is a possible occurrence even after basic periodontal therapy, but is limited to a small percentage of sites. Also, periodontal treatment reduces elevated inflammation markers of inflamed sites from disease patients to levels of those observed in healthy subjects, but these levels could not be sustained in case of residual periodontal pockets. However, as elevated gene and protein anti-inflammatory marker levels in inflamed sites prior treatment could suggest its modulatory role, it does not seem to discriminate future progressive sites. Predictors of future attachment loss are still a challenge in periodontal diagnosis. |
PMC9648965 | Parth K Raval,Sriram G Garg,Sven B Gould | Endosymbiotic selective pressure at the origin of eukaryotic cell biology | 10-11-2022 | endomembrane system,mitochondria,eukaryogenesis,FECA,LECA,endosymbiosis | The dichotomy that separates prokaryotic from eukaryotic cells runs deep. The transition from pro- to eukaryote evolution is poorly understood due to a lack of reliable intermediate forms and definitions regarding the nature of the first host that could no longer be considered a prokaryote, the first eukaryotic common ancestor, FECA. The last eukaryotic common ancestor, LECA, was a complex cell that united all traits characterising eukaryotic biology including a mitochondrion. The role of the endosymbiotic organelle in this radical transition towards complex life forms is, however, sometimes questioned. In particular the discovery of the asgard archaea has stimulated discussions regarding the pre-endosymbiotic complexity of FECA. Here we review differences and similarities among models that view eukaryotic traits as isolated coincidental events in asgard archaeal evolution or, on the contrary, as a result of and in response to endosymbiosis. Inspecting eukaryotic traits from the perspective of the endosymbiont uncovers that eukaryotic cell biology can be explained as having evolved as a solution to housing a semi-autonomous organelle and why the addition of another endosymbiont, the plastid, added no extra compartments. Mitochondria provided the selective pressures for the origin (and continued maintenance) of eukaryotic cell complexity. Moreover, they also provided the energetic benefit throughout eukaryogenesis for evolving thousands of gene families unique to eukaryotes. Hence, a synthesis of the current data lets us conclude that traits such as the Golgi apparatus, the nucleus, autophagosomes, and meiosis and sex evolved as a response to the selective pressures an endosymbiont imposes. | Endosymbiotic selective pressure at the origin of eukaryotic cell biology
The dichotomy that separates prokaryotic from eukaryotic cells runs deep. The transition from pro- to eukaryote evolution is poorly understood due to a lack of reliable intermediate forms and definitions regarding the nature of the first host that could no longer be considered a prokaryote, the first eukaryotic common ancestor, FECA. The last eukaryotic common ancestor, LECA, was a complex cell that united all traits characterising eukaryotic biology including a mitochondrion. The role of the endosymbiotic organelle in this radical transition towards complex life forms is, however, sometimes questioned. In particular the discovery of the asgard archaea has stimulated discussions regarding the pre-endosymbiotic complexity of FECA. Here we review differences and similarities among models that view eukaryotic traits as isolated coincidental events in asgard archaeal evolution or, on the contrary, as a result of and in response to endosymbiosis. Inspecting eukaryotic traits from the perspective of the endosymbiont uncovers that eukaryotic cell biology can be explained as having evolved as a solution to housing a semi-autonomous organelle and why the addition of another endosymbiont, the plastid, added no extra compartments. Mitochondria provided the selective pressures for the origin (and continued maintenance) of eukaryotic cell complexity. Moreover, they also provided the energetic benefit throughout eukaryogenesis for evolving thousands of gene families unique to eukaryotes. Hence, a synthesis of the current data lets us conclude that traits such as the Golgi apparatus, the nucleus, autophagosomes, and meiosis and sex evolved as a response to the selective pressures an endosymbiont imposes.
‘A scientist in his laboratory is not a mere technician: he is also a child confronting natural phenomena that impress him [her] as though they were fairy tales’ (Marie Curie). In evolutionary biology, the transition from prokaryotic to eukaryotic life was a true game changer. Eukaryogenesis involves the origin of new cell biology, genetics, and an unprecedented emergence of morphological diversity. Historically, the prokaryote-eukaryote divide was based on observed differences in morphology and in turn defined this aboriginal branch in the tree of life by their lack of traits that eukaryotes posses (Stanier and Van Niel, 1962). Phylogeny and biochemistry separate prokaryotes into bacteria and archaea (Fox et al., 1980; Koga et al., 1998; Woese et al., 1990) and document the dichotomy of pro- and eukaryotes, which is further evident in the number of protein families (Rebeaud et al., 2021), average protein length (Brocchieri and Karlin, 2005), cellular and morphological complexity (Stanier et al., 1963), and the overall prevalent contribution to the planet’s biomass (Bar et al., 2018). For decades the field of eukaryogenesis speculated on the existence of a eukaryotic lineage with intermediate biology bridging the prokaryote-eukaryote divide, an elusive grade known as archezoa (Cavalier-Smith, 1987). For curious reasons (see Martin et al., 2017a, for details) this search focused on a eukaryotic phylum lacking a mitochondrion (Cavalier-Smith, 1987; Speijer, 2020), but not necessarily one lacking a nucleus or endoplasmic reticulum (ER). Varying models, but with the common theme of promoting an autogenous origin of a last eukaryotic common ancestor (LECA) independent of a bacterial partner, were proposed (reviewed in Martin et al., 2015). Through the identification of hydrogenosomes and mitosomes (reduced mitochondria; Tovar et al., 1999; Williams et al., 2002) and modern phylogenomics (Burki et al., 2020), we now understand that the biology of LECA matched that of extant garden variety protists. This might appear trivial from todays’ perspective, but reaching this consensus and settling on a mitochondrion-bearing LECA took decades. LECA evolved from an archaeal host cell and its endosymbiotic alphaproteobacterial partner (Imachi et al., 2020; Wu et al., 2022; Zaremba-Niedzwiedzka et al., 2017) and could have been syncytial and fungus-like, with the first gametes budding off as a selectable unit, in what one could describe to be a flagellated protist (Garg and Martin, 2016; Skejo et al., 2021). The field exploring eukaryogenesis has moved on to studying the nature and origin of the first eukaryotic common ancestor (FECA). This subtle change in terminology has far-reaching consequences. The term FECA only puts a label on the first lineage that we would no longer define as prokaryotic, but which had not yet evolved all traits characterizing the LECA. But at what point did prokaryotic evolution transition into eukaryotic origin? Was it upon the emergence of meiosis and sex? Or the ER and its specialized compartment the nucleus? Or after the transition from archaeal to eukaryotic (bacterial-type) membrane lipids? The transition between prokaryotes and eukaryotes was fluid in nature, with the emergence of the new traits occurring in a currently unknown order (Gould et al., 2016; López-García and Moreira, 2020; Vosseberg et al., 2021). The critical question is: what drove the emergence of eukaryotic traits and what fixed them in evolution? Here, we discuss the scenarios of a morphologically simple FECA versus a complex one on the basis of reviewing models and data that emerged after the report of the asgard archaeal superphylum from which the eukaryotic host lineage stems. We explore key eukaryotic traits and the phylogenetic distribution of protein associated families, in light of housing an endosymbiont that differs by all other traits in that it represents a semi-autonomous living entity. This imposed unique challenges onto the host throughout eukaryogenesis and whose solution, we argue, is witnessed in the form of compartmentalization, meiosis, and sex.
‘It can be considered a relatively harmless habit, like eating peanuts, unless it assumes the form of an obsession; then it becomes a vice’ (Roger Y Stanier). The relatively harmless habit of tracing the origins of the eukaryotic cell has occupied scientists across several generations, a historical account of which is beyond the scope of this review but has been summarized elsewhere (Martin, 2017). Current models of eukaryogenesis differ above all in the relative placement and contribution of the endosymbiont and consequently the cellular complexity of the host archaeon prior to endosymbiosis. Briefly, mitochondria-early theories place endosymbiosis closer to or at FECA (Figure 1), suggesting that the traits that characterize LECA evolved after endosymbiosis from a prokaryotic-like host cell. On the contrary, mitochondria-late scenarios view endosymbiosis and mitochondrial origin as a finishing touch to the LECA (Figure 1). Intermediate models are gaining popularity, but are often vague on which traits evolved prior or after endosymbiosis. The notion that eukaryote-like complexity was a prerequisite to phagocytosis for promoting mitochondrial origin appears mandatory to some, but the idea remains unsubstantiated (Leão et al., 2018; Martin et al., 2017b; Mills, 2020; Shiratori et al., 2019). Mitochondria-lacking but phagocytosing LECA models – such as the archezoa hypothesis (Cavalier-Smith, 1987) – lost support due to the now known universal presence of mitochondria across the diversity of all eukaryotic super groups (Hjort et al., 2010; Stairs et al., 2015), but variations of the archezoa hypothesis populate the literature, rekindled on the basis of inferred proteomes from asgard archaea. Reports of a patchy distribution of homologues of the eukaryotic ESCRT-III, a ubiquitin modifying system, and eukaryote-like actin in the TACK superphylum, triggered thoughts about phagocytosing archaea (Guy and Ettema, 2011; Yutin et al., 2009). The identification of proteins with homology to ESCRT I and II, longin domains, sec23 and sec24 (Zaremba-Niedzwiedzka et al., 2017), Rab-like GTPase (Akıl and Robinson, 2018; Klinger et al., 2016; Surkont and Pereira-Leal, 2016), or profilin that can inhibit (in vitro) rabbit actin polymerization (Akıl and Robinson, 2018; Survery et al., 2021) quickly channelled into speculations that asgard archaea might have a dynamic cytoskeleton, intracellular membrane trafficking, and are morphologically complex (Klinger et al., 2016; Neveu et al., 2020; Zachar et al., 2018; Zaremba-Niedzwiedzka et al., 2017). These interpretations mirror a FECA that is reminiscent of the host lineage at the centre of the archezoa hypothesis (discussed in Martin et al., 2017b), culminating in the depiction of a mitochondrion-lacking eukaryote on the cover of Nature (Pittis and Gabaldón, 2016). When transmission electron microscopy revealed images of asgard archaea, that of Prometheoarchaeon syntrophicum, they uncovered tiny prokaryotes with no intracellular eukaryotic traits living in syntrophy with bacteria (Imachi et al., 2020). Such images contradict the narrative of complex asgard archaea, but resonate well with early warnings regarding overinterpretations of metagenome data (Dey et al., 2016). Eukaryogenesis models rapidly adapted to the discovery of asgard archaea. They now focus on FECA with various speculations regarding the roles of the discovered genes in host biology prior to endosymbiosis. While the level of cellular complexity is not always explicitly declared, several cases can be made out that depict FECA without an endosymbiont (Baum and Baum, 2020; Dacks et al., 2016; Eme et al., 2017; Pittis and Gabaldón, 2016; Vosseberg et al., 2021). Some models can be interpreted one way or the other (Imachi et al., 2020), while some explicitly state that the host cell was a bona fide prokaryote and that eukaryotic traits and biology evolved after endosymbiosis (Gould et al., 2016; López-García and Moreira, 2020; Wu et al., 2022). Notably, the differences among these models rest upon a few dozen genes from the pan-asgard archaeal genome repertoire, whose overall unique contribution to the roots of eukaryotes was 0.3% or less (Knopp et al., 2021; Liu et al., 2021). Sources and timing of gene acquisition in the FECA to LECA transition are equally essential to correctly quantify as they remain hard to predict.
Among eukaryotic genomes there are more genes of bacterial than of archaeal origin (Alvarez-Ponce et al., 2013; Brueckner and Martin, 2020; Makarova et al., 2005). An autogenous origin of cellular complexity on the basis of an archaeal (host) source alone would predict the opposite but prokaryotes are characterized by mosaic genomes due to horizontal gene transfer (HGT) whose contribution to cellular complexity prior to endosymbiosis is debated (Martin et al., 2017b; Pittis and Gabaldón, 2016). Claims concerning differential loss of genes in extant archaea (Koonin and Yutin, 2014; Eme et al., 2017) are at odds with pangenomes that support a pan-asgard concept (Knopp et al., 2021; Liu et al., 2021). Dynamic genomes and the time passed since eukaryote origin challenge phylogenomic approaches and can skew interpretations including the timing of compartment origin. The estimated timing of gene duplications that depend on molecular clock techniques that are error-prone (Graur and Martin, 2004; Rodríguez-Trelles et al., 2001; Tiley et al., 2020) with respect to the origins of cellular complexity are also debated (Tria et al., 2021; Vosseberg et al., 2021). A reliance purely on relative branch lengths concluded that mitochondrial metabolism and the ER in eukaryogenesis ensued the origin of the nucleus (Pittis and Gabaldón, 2016). The method used has been questioned (Martin et al., 2017a), and the use of unspecific COG (cluster of orthologous genes) annotations in the study is problematic. The few universal proteins listed might operate in the present-day nucleus, but provide little to no evidence for the presence of one prior to endosymbiosis. Proteins of the nuclear pore complex, of which there are about three dozen (Raices and D’Angelo, 2012), were not identified or discussed, nor was the fact that the nucleus is a specialized compartment of the ER from which it forms during cell division (Anderson and Hetzer, 2008). Substitution rates that challenge molecular clock studies vary substantially across species (Baer et al., 2007; Halligan and Keightley, 2009) and the functional unit a protein is associated with (Hartwell et al., 1999). Considering that thousands of new protein families emerged at eukaryote origin that fall into such categories (Preisner et al., 2018) further highlights the caution with which we need to digest molecular clock studies on eukaryogenesis. The distribution of protein families associated with eukaryotic traits across the domains of life is always telling. Seventy percent or more protein families involved in major eukaryotic traits (such as cell cycle, meiosis, autophagy, nucleus) are specific to eukaryotes, 10–15% (e.g. kinases) are universal across all domains of life, 10–15% are bacterial (e.g. aminopeptidases, mTOR interacting proteins, glycosyltransferases), and a small fraction appear to originate from archaea (DNA licensing proteins of cell cycle, ARG GTPases, N-glycan biosynthesis). The distribution of protein families across prokaryotes and eukaryotes (Figure 2) confirms that eukaryotes acquired genes from bacterial or archaeal sources and co-opted them to suit new eukaryotic traits evolving in the FECA to LECA transition, but the majority of protein families involved in eukaryotic cellular complexity are absent across the entire realm of prokaryotic diversity (Brunk and Martin, 2019; Dell et al., 2010; Knopp et al., 2021; Liu et al., 2021; Lombard, 2016). Hence, HGT falls short at explaining the pro- to eukaryote transition with respect to the origin of thousands of eukaryote-unique gene families and a reason for their positive selection in the absence of an endosymbiont. Beyond question, HGT fed into eukaryogenesis – after all, the eukaryotic cell is the product of two prokaryotes – but endosymbiotic partners bring along thousands of genes and many were integrated into the host genome (Timmis et al., 2004). They can explain the pronounced non-alphaproteobacterial signal among proteins supporting eukaryotic traits, especially if we place mitochondrial origin at the root of the FECA and accept HGT to be prevalent. Phylogenetic trees built using concatenated gene sequences boost phylogenetic signals, but under the premise that the individual genes used recapitulate the evolutionary history of the species (Robinson and Foulds, 1981). For incomplete and contaminated metagenomes (including early releases of asgard archaeal ones), the individual ribosomal gene trees were incongruent (Garg et al., 2021). Similar to simulated chimeric genomes containing genes from different species, metagenome assembled genomes are prone to assembly and binning artefacts. The frequent use of automated pipelines and poorly fitting phylogenetic models exacerbates the risk of drawing false conclusions from metagenome data (Williams and Embley, 2014). For instance, the presence of glycerol-3-phosphate lipids in asgard archaea was claimed (with far-reaching implications on the lipid transition during eukaryogenesis) based on the predicted presence of enzymes involved in the synthesis of ester-linked fatty acids (Villanueva et al., 2017). No evidence of such lipids, however, was found in the biochemical analysis of a cultured asgard archaeon (Imachi et al., 2020) and the presence of the set of required enzymes in asgard archaea has yet to be identified. Better assembly methods result in more complete circular genomes from both axenic culture and metagenomic approaches that mitigate issues of tree congruence (Garg et al., 2021), albeit leaving the same room for interpretations. Underpinning studies of evolutionary history are phylogenetic trees and theories behind constructing and interpreting them. While it is well beyond the scope of this manuscript to discuss all the vagaries of the field of cladistics and modern phylogenies, it is increasingly evident that many phylogenetic studies have moved from a field that requires expertise in biology to a field that requires expertise in computation (Fitzhugh, 2016) – hypotheses generated from DNA sequences run the risk of taking precedence over morphological evidence (Wheeler et al., 2013). This is less of a critique than a realization. Although sequencing and computational techniques have made significant progress over the years, for the timescales dealt with in early evolution, most issues and challenges remain. It is critical to remember that phylogenetic trees are hypotheses on the evolutionary relationship between organisms and not an observation on itself (Hennig, 2000). No phylogenetic tree is perfect, few are for eternity, and no tree alone will ever satisfy the need for empirical evidence.
‘In the case of living machinery, the ‘designer’ is unconscious natural selection, the blind watchmaker’ (Richard Dawkins). Evolution is typically understood to progress gradually and randomly through mutations and the selection of beneficial traits vertically across generations (Darwin and Murray, 1859; Futuyma, 1986). Endosymbiosis adds a massive horizontal component to evolution that is, however, still subject to the basic selection and fixation process. In other words, while the emergence of eukaryotic traits was gradual, the selective pressure that demanded their emergence was more radical. It is this duality that stands between eukaryogenesis theories like a firewall. Any hypothesis that pictures an archaeal lineage transitioning from prokaryotic to eukaryotic cell biology – even of an intermediate type – in the absence of an endosymbiont needs to explain why it was a singularity. Microbial syntrophy is the norm and so is the selective pressure to optimize it. Why are intermediate cell types not observed among the many syntrophic prokaryotes studied, if it was not for the lack of an endosymbiotic event? A mitochondrion-lacking but complex FECA explains eukaryotic traits solely from a host perspective and misses to provide a plausible reason for selection and the emergence and fixation of traits we here discuss in more detail (Figure 3).
Glycosyltransferases are promiscuous enzymes and it has been suggested they are separated through ER-Golgi compartmentalization for that reason (Biswas and Thattai, 2020). N- and O-glycosylation are ubiquitous in eukaryotic cells, but not so in prokaryotes. Eukaryotic N-glycosylation is likely derived from the archaeal ancestor, while O-glycosylation is more prevalent among bacteria (Abu-Qarn et al., 2008; Dell et al., 2010; Jarrell et al., 2014). Hence, if each pathway stems from one of the prokaryotic partners, natural selection would foster a spatial separation only upon and not prior to endosymbiosis. The ER lumen and mitochondrial intramembrane space (the former bacterial periplasm) share notable homologies. This includes calcium storage (Dominguez, 2004; Raffaello et al., 2016), disulfide relay systems (Backes et al., 2019), and redox balance (Cardenas-Rodriguez and Tokatlidis, 2017). The contact sites of the ER and mitochondrion are cornerstones for the synthesis and regulation of lipids and a plethora of cellular roles (Booth et al., 2016; Flis and Daum, 2013; Friedman et al., 2011; Hamasaki et al., 2013). This could be a consequence of the ER stemming from mitochondrial-derived vesicles (MDVs) (Gould et al., 2016). MDVs could have provided the necessary endomembrane material for compartmentalization and remain the most plausible source for the lipid transition from ether-linked, archaeal head groups to ester-linked (bacterial) eukaryotic head groups. Much on the origin of the endomembrane system remains a speculation, but not so the existence of MDVs, their role in eukaryotic biology, and how they induce compartment formation (Schuler et al., 2021; Sugiura et al., 2017; Sugiura et al., 2014; Yamashita et al., 2016). Eukaryogenesis models failing to acknowledge their existence miss a biological fact with significant explanatory power.
Vesicle secretion from the plasma membrane into the environment is a common trait of all cells. Unique to eukaryotes are the many ways with which they can internalize membrane vesicles of various sizes, ranging from clathrin-mediated endocytosis (~100 nm) to phagocytosis (>750 nm), using different molecular machineries. Intracellular vesicle trafficking connects the plasma membrane with the endomembrane system and the compartments thereof among each other. All compartments that define the endomembrane system – with the ER at its core – as well as the majority of regulatory and structural proteins are conserved across eukaryotes and absent in prokaryotes (Klinger et al., 2016; Kontou et al., 2022).
The nucleus is a distinctive extension of the ER and forms from the latter after mitosis (Anderson and Hetzer, 2008) using homologs of ESRCT complex (Olmos et al., 2015). It separates transcription from translation and is the site of pre-ribosome assembly (Peña et al., 2017). As with any trait, a selective reason for its presence must outweigh the costs for its maintenance; consider, for example, the exchange of mRNA and effectors with the cytoplasm (Nerurkar et al., 2015; Warner, 1999). A plausible selection could have been imposed by the transfer of group II introns from the endosymbiont that drove the origin of eukaryotic splicing and need for separating transcription slowed by the spliceosome from fast translation (Martin and Koonin, 2006). Mitochondria-early scenarios provide both the problem (group II introns that need to be spliced) and the solution (MDVs that might have given rise to the ER) (Gould et al., 2016).
The prokaryotic solution to prevent mutational overload through Muller’s ratchet is HGT (Muller, 1964). The nucleus renders the eukaryotic cytoplasm almost sterile of DNA (preventing HGT), wherein it plays a regulatory immune function (Abe et al., 2019; Paludan and Bowie, 2013). The eukaryotic solution was ploidy, multinucleated cells and reciprocal recombination through meiosis (Garg and Martin, 2016). A multinucleated state is readily achieved by decoupling nuclear from cell division, a mechanism commonly observed in prokaryotes wherein the DNA replicates independently of the cell before portioning into daughter cells (Haeusser and Levin, 2008). The syncytial theory for eukaryotic origin (Garg and Martin, 2016) posits that by virtue of multinucleated cells within a singular archaeal host, multiplying bacterial symbionts are free to lose genes via endosymbiotic gene transfer to the multiple copies of the host nucleus/nuclear material in the cytoplasm to explore various configurations under the constant onslaught of group II introns, yet retaining fitness by compensating viable mRNA in-trans within the same shared cytoplasm. The explanatory power of this model is twofold: (i) it explains how homologous recombination – which subsequently evolved to meiosis as we understand it today – was necessary to maintain viable copies of undisrupted genes, while simultaneously maintaining the presence of bacterial transferred genes, and (ii), it explains the monophyly of eukaryotes. As long as the FECA to LECA transition continued, the multitude of host nuclei remained within a single confined cytoplasm until the fittest version was optimized via various rounds of endo-meiosis and homologous recombination. Any origin of cell division and/or cell cycle might have given rise to gamete-like spores that separated off the original syncytium. In cases where a successful combination was released through ESCRT-driven scission (see below), a similar process applies for further optimization. In scenarios in which the budded off cell (gamete) was fitter than the syncytium, it would outcompete the original syncytium or alternatively would be outcompeted when it contained aberrant genomes. In either case, the singularity of LECA is well explained by the syncytial model of the FECA to LECA transition (Garg and Martin, 2016; Skejo et al., 2021). Meiosis in itself is ancient, ubiquitous, and the central process that imparts an advantage to sex in eukaryotes (Colnaghi et al., 2022; Colnaghi et al., 2020; Malik et al., 2008; Speijer et al., 2015). Several theories place mito-nuclear interactions, heteroplasmy, and mitochondrial ROS as drivers of eukaryotic sex (Colnaghi et al., 2020; Hörandl and Speijer, 2018; Radzvilavicius and Blackstone, 2015). HGT alone was insufficient for LECA to escape Mullers ratchet in the absence of homologous recombination (Colnaghi et al., 2022), when considering expanding genome size and repeat sequence frequency. Everything points to an origin of sex and meiosis necessitated by the presence of mitochondria. Moreover, sex, as a trait, restricts the number of potential mating partners (by 1/number of sex types), and it is hence less surprising that it did not evolve in groups outside of eukaryotes, but had to in the FECA.
The majority of enzymes of peroxisomal beta-oxidation are of alphaproteobacterial origin (Bolte et al., 2015) and peroxisomes might have evolved to compartmentalize ROS-producing beta-oxidation and protect the mitochondrial genome (Speijer, 2017). De novo biosynthesis of peroxisomes involves MDVs with integrated Pex3 and Pex14 that fuse with ER-derived vesicles containing Pex16 (Sugiura et al., 2017), and the compartment for beta-oxidation appears absent in species lacking respiring mitochondria (Le et al., 2020). Peroxisomes not only make sense in the presence of a mitochondrion, they are also partly a product thereof (Mohanty and McBride, 2013; Sugiura et al., 2017).
Cytosolic protein homeostasis in prokaryotes is performed by proteases and proteasomes, which are common to both archaea and bacteria. Defective membrane proteins and membranes are shed by mechanisms similar to bacterial outer membrane vesicle secretion (Schwechheimer and Kuehn, 2015). Eukaryotes utilize membrane-bound compartments in the form of autophagosomes, also for recycling membranes including their proteins (Nakatogawa, 2020). Mitophagy removes damaged mitochondria and is initiated by ER-mitochondrial contact sites (Hamasaki et al., 2013). It is a trait needed in the presence of large intracellular compartments and the occasional yet inevitable breakdown of organelles that require immediate containment (Anding and Baehrecke, 2017).
The eukaryotic cell cycle is a series of choreographed steps that leads to the correct portioning of genetic material, endomembrane, and organelles to both daughter cells (Harashima et al., 2013). The presence of a nuclear compartment and an endosymbiont are incompatible with binary fission in the absence of orchestrated replication and organelle and compartment division, and cytokinesis. As mentioned in the previous section, in prokaryotes the nuclear material (genome) replicates independently of cell division, which would have facilitated the formation of syncytial populations (Haeusser and Levin, 2008). During this time, however, we speculate that mitochondrial metabolism started playing a more significant role in controlling cell division, given the role of nutrient availability in coordination of cell division. The G1 phase of the eukaryotic cell cycle results in the mitochondria as the master regulator for S/G2 progression (Antico Arciuch et al., 2012; Mitra et al., 2009), suggestive of a deep link between mitochondria and the cell cycle and one that would have been difficult to integrate into a pre-existing one. Eukaryotic cell division employs the use of ESCRT homologs and actin in contrast to bacterial division mechanisms involving FtsZs from which also tubulin evolved (Christ et al., 2016; Goliand et al., 2014; Stoten and Carlton, 2018). This suggests the evolution of an independent pathway for cell division involving ESCRT proteins consistent with their role in archaeal cell division (Tarrason Risa et al., 2020), one that was based on outer membrane vesicle secretion, but this time packaging mitochondria and the nucleus/genetic material, the latter similar to a role of prokaryotic OMVs (Schwechheimer and Kuehn, 2015). Understanding how these pre-existing mechanisms were leveraged in an elaborate checkpoint system of the eukaryotic cell cycle remains to be elucidated. One might consider the cytoskeleton another eukaryotic trait, but this is more involved. The eukaryotic cytoskeleton rests on three main pillars: (i) actin and associated proteins, (ii) tubulin and associated proteins, and (iii) the utterly diverse intermediate filament (IF) proteins. Components of each pillar, sometimes also in combination, can be found in archaea and bacteria alike (Duggin et al., 2015; Larsen et al., 2007; Preisner et al., 2018; van den Ent et al., 2001; Wickstead and Gull, 2011; Zaremba-Niedzwiedzka et al., 2017). As with many things in eukaryogenesis, it is the intricate combination and universal presence of all three cytoskeletal pillars and the dynamic nature which they are used in eukaryotes that is characteristic. The latter is best demonstrated by the rapid switch in motility between actin-based gliding and tubulin-based flagella-driven swimming in many protists, likely also a feature of the LECA (Fritz-Laylin et al., 2010; Kusdian et al., 2013; Preisner et al., 2018). Basic components were derived from the host cell, such as gelsolin-regulated actin filaments (Akıl et al., 2020) and evolution co-opted such mechanisms en route to LECA. It is conceivable that with expanding cell size, increased intracellular complexity and the need of an orchestrated cell cycle, the selection for a dynamic but simultaneously in parts rigid cytoskeleton increased, which triggered the expansion of the IF protein family required for mechanical support, and the origin of additional accessory proteins and regulatory mechanisms that are absent in prokaryotes. The identification and subsequent characterizations of asgard archaea have done the following for eukaryogenesis: (i) They underpin the syntrophic origin of eukaryotes involving two prokaryotic partners and (ii) provide support for a 2D tree of life (i.e. two domains of life, bacteria and archaea, evolved from the origin of life and eukaryotes emerged from within archaea after endosymbiosis of an alphaproteobacterial partner). (iii) They provide no evidence for the presence of bacterial-type ester-linked lipids in asgard archaea, (iv) reject a complex archaeal ancestor necessary to explain the patchy distribution of eukaryogenesis-relevant gene families (Wu et al., 2022), and (v) show that the asgard archaeal set of genes before unique to eukaryotes closes the gap to the number of gene families encoded by eukaryotes by only 0.3% (Knopp et al., 2021) or less (Liu et al., 2021). Hence, with respect to explaining the origin of eukaryotic traits and a rationale for their universal presence in eukaryotes, the asgard archaea and their syntrophic bacterial partners support and place us at scenarios that were submitted some 25 years ago (Martin and Müller, 1998; Moreira, 1998; Vellai and Vida, 1999). Considering syntrophy as a key ecological parameter in eukaryogenesis was an early notion that has stood the test of time (Imachi et al., 2020; López-García and Moreira, 2020; López-García and Moreira, 2019; Sousa et al., 2016; Spang et al., 2019; Wu et al., 2022). Ever since, observations from the field of microbial ecology, genomics, and geology continue to encourage us to picture eukaryogenesis to have occurred within a microbial mat, where multiple species thrive in close proximity and ample syntrophies exchanging substrates such as H2/electrons under limited or no oxygen (López-García and Moreira, 2020). Recent advances in geochemistry added new support to the proposal that eukaryogenesis occurred in anoxic niches with a preferred shift towards aerobic metabolism being a secondarily derived state (Mills et al., 2022) and so do the culturing conditions of Prometheoarchaeon (Imachi et al., 2020). Such prokaryotic consortia can source genes through HGT from pangenomes of other bacteria and (asgard) archaea and the virosphere that also contributed to the birth of the eukaryotic genome (Spang et al., 2022; Wu et al., 2022). Evidently, however, most eukaryotic protein families evolved during the FECA to LECA transition and selective pressures due to endosymbiosis were likely key. To conclude, physiological and phylogenomic studies support a mitochondria-early scenario and so does cell biology (Figure 3). Claiming mitochondria were of little importance in eukaryogenesis contradicts the simultaneous claim that intermediates lacking mitochondria all went extinct – an oxymoron that suggests that the mitochondrial endosymbiont contributed little in the FECA to LECA transition, while its presence was vital for the survival of FECA during eukaryogenesis.
‘The most obvious differences between different animals are differences of size, but for some reason the zoologists have paid singularly little attention to them’ (John BS Haldane). Haldane began his influential essay by addressing a lack of scale bars in zoology books. One can point to a similar issue regarding eukaryogenesis, in which models often depict cells not changing in size up to scale in the course of the FECA to LECA transition (Baum and Baum, 2020; Gould et al., 2016; Spang et al., 2019). Though not intentional, this is important. In eukaryogenesis we are dealing with at least a 10 times increase in cell diameter, with known consequences regarding cell volume, morphology, and molecular diffusion limits among other factors (Young, 2006). Engulfing a proteobacterium with a surface area of 10 µm2 requires 10 times the surface area of the asgard archaeon Prometheoarchaeon. For a typical protist it is only 1% of its surface area. Putting scales on a recent model, the entangle-engulf-endogenize mode (Imachi et al., 2020) brings forth details worthy considering: an observed tubular protrusion requires 12% of cytoplasm for a 50% increase in surface area interacting with syntrophic partners (Figure 4). Four to six protrusions approximately result in the doubling of cytosolic volume, maybe explaining why Prometheoarchaeon has not been observed to produce more than six protrusion per cell. Such protrusions might have been relevant for the uptake of the symbiont, but the surface area of such a protrusion is 3% that of a proteobacterium. Entangling a proteobacterium entirely would take at minimum 50 protrusions, the cost of which is six times the cytoplasmic volume and not considering the multitude of proteins needed for recognition, surface attachment, and the processes thereof. We also note that we know neither of a case in nature where tubular-like extensions (allowing nutrient exchange) fuse to sheets (allowing cell engulfment), nor can we imagine how this would work on a molecular level, respecting membrane biology, and in 3D. Considering scale bars or tubular versus sheet-like membrane biology is not intended to disprove any model, but it highlights potential issues and also reminds us of the question of when and how did the size increase in the FECA to LECA transition.
‘The higher animals are not larger than the lower because they are more complicated. They are more complicated because they are larger’ (John BS Haldane). Haldane noted that an elephant has to be as complicated as an elephant, because it is as large as an elephant. Across eukaryotes, an increase in cell size (1000–10,000 times) and morphological complexity is common, matched by a comparable increase in genome size. The upper end of bacterial genomes is 15 Mb (Land et al., 2015), that of (haploid) eukaryote is around 130 Bb (Pellicer et al., 2010). What drove this increase in cell and genome size during eukaryogenesis (Figure 5)? In prokaryotes ATP production is limited by the available cell surface. This limits the replication rate and imposes negative selection on genome expansion (Lane, 2007). Conversely, in mitochondria powered eukaryotes, energetic efficiency increases with cell size (Hochachka et al., 2003; Lane, 2007), imposing a positive selection. Increased cell size in eukaryotes means increased DNA content to maintain the karyoplasmic ratio (Cavalier-Smith, 2005; Cavalier-Smith, 1985), which is positively selected for (Lane, 2007). Through this, and remembering that the endosymbiont provided the host cell with both problems and solutions (main text), one can speculate on mechanisms and a selective pressures for the emergence of eukaryotic cell biology, cell and genome size during the FECA to LECA trajectory. Endosymbiosis provided an influx of endosymbiotic genes and membrane material. An early endomembrane system with minimal protein content, for example, proteins that are likely to be packed for secretion via OMVs, etc., emerged and the nucleus formed for reasons discussed in the main text. Constant fusion of endosymbiont-derived vesicles with the archaeal host provided a mechanism for the lipid shift and compartment origin (Gould et al., 2016), which might have fostered an increase in cell size. Integration of endosymbiotic DNA provided one early mechanism for why the genome size increased, together with duplication events (Kelly, 2021; Tria et al., 2021; Van de Peer et al., 2009). The ongoing concentration of ATP synthesis to mitochondria imposed positive selection on cell size, which allowed for a further increase in genome size. A feed-forward process of increased cell size stipulating increased genome size and vice versa commenced that was supported by an emerging endomembrane system and intracellular transport that counteracted the molecular diffusion limit (Figure 5). It provided ground for new combinations of genes to emerge and expressed (Lane and Martin, 2010) and increased cell size accommodated experimental expression of new proteins in the cytosol (Dill et al., 2011). This presented an opportunity – almost unlimited in theory – for the origin of new protein families and complex traits. Considering that most of these new inventions revolve around the endosymbiont (Figure 3), suggests it drove the selection for their emergence and fixation. Haldane might have put it this way: the LECA got larger because it was complex and it became complex because it was larger.
‘I have not failed. I’ve just found 10,000 ways that won’t work’ (Thomas Edison). The cost of innovation is significantly higher than the manufacturing of the final product – the COVID vaccine serves as a topical example (Light and Lexchin, 2021). Eukaryotes have innovated several folds higher number of protein families than archaea as evident from genome (Brueckner and Martin, 2020) and proteome data (Müller et al., 2020) alike, underpinning the complexity across the eukaryotic tree of life. Since mutations lack foresight and are more likely to be deleterious than advantageous (Eyre-Walker and Keightley, 2007), inventing new proteins takes a considerable amount of trial and error. Ribosome production and protein biosynthesis consumes the majority of a cell’s energy budget (Harold, 1986; Kafri et al., 2016) and the energy budget of trial and error would be orders of magnitude higher. Mitochondria were key by providing eukaryogenesis with an energetic freedom that supported this unparalleled level of innovative protein evolution and expression (Lane and Martin, 2010). While challenged (Lynch and Marinov, 2017; Schavemaker and Muñoz-Gómez, 2022), we do not see it disproven (Gerlitz et al., 2018; Lane, 2020). Calculations questioning the bioenergetic contribution of mitochondria do not account for the cost of evolving novel proteins. The acquisition of a respiratory electron transport chain through excessive HGT does not make a cell complex ( Nelson-Sathi et al., 2012), because the location of the bioenergetic membrane matters. The ratio of bioenergetic membrane (=energy generation) to genome size is high when harbouring an endosymbiont with internalized energetic membranes and a reduced genome (Lane and Martin, 2010). Also, a bioenergetic plasma membrane is incompatible with phagocytosis and the internalization of the bioenergetic membrane was a prerequisite to evolve phagocytosis (Martin et al., 2017b). A physiological observation that puts a timing on events in the FECA to LECA transition. Eukaryotes that maintain complexity in the absence of respiring mitochondria has prompted some to question the importance of mitochondria and a surplus of ATP at eukaryote origin (Hampl et al., 2019), while missing a critical detail: the examples listed stem from species that are either parasites or commensals of eukaryotes and who are energetically dependent on canonical mitochondria. The same holds true for the only eukaryotic taxon not possessing mitochondria, Monocercomonoides. They secondarily lost mitochondria and can only thrive in the gut of some animals (Karnkowska et al., 2016). Such parasites or commensals have engaged an evolutionary path characterized by energetic dependency. Their complexity might diminish over evolutionary timescale, should they not go extinct with their hosts first. The issue is the origin of eukaryote complexity from prokaryotic ancestors, not the maintenance of eukaryotic complexity from eukaryotic ancestors. An alternative to the energetics argument in explaining the ubiquity of mitochondria and its role in eukaryogenesis is missing, and the papers that question it are no exception. The internalization of energetic membrane – energy production from only 10% of cell volume – decoupled from the genome, as is the case in mitochondria (Fenchel, 2014), provides an optimum for protein innovation and a selection towards a complexity that can maintain a 200 ton blue whale.
‘Everything existing in the universe is the fruit of chance and of necessity’ (Democritus). Evolution is random and selects for fitness. Extinction is the rule and so is the common principle of use it or lose it (Lahti et al., 2009). Traits that remain unchanged across various organisms and through a billion years of evolution are indicative of the fact they are fundamental. Endosymbiosis is absent among prokaryotes (apart from isolated exceptions) and so is morphological complexity comparable to that of eukaryotes (reviewed in Martin et al., 2017b). Is it by chance or necessity? It is unlikely that the endosymbiosis leading into the origin of the eukaryotic domain was the first and only attempt throughout now 4 billion years of prokaryotic evolution. The set of challenges posed by an endosymbiont are generic in nature: (i) there is a constant influx of endosymbiotic DNA which, after integration into the host genome (Allen, 2015; Portugez et al., 2018), is also exposed to an increased accumulation of deleterious mutations (Eyre-Walker and Keightley, 2007). The secretion of outer membrane vesicles by the endosymbiont is inevitable (Deatherage and Cookson, 2012), as well as the removal of irreversibly damaged organelles or the need to supply the endosymbiont with substrate from ions to peptides. Dividing endosymbionts need to be integrated into the division cycle of an archaeal host itself relying on simple binary fission. A lot of solutions are associated with compartmentalization and this is a good time to remember that the mitochondrial endosymbiont not only provided the challenges, but maybe also the material to solve some (Gould et al., 2016). Any attempts at eukaryogenesis are prone to fail, if such challenges are not met by solutions that furthermore require correct timing (Barbrook et al., 2006). An influx of genes via HGT alone does not translate into complexity. Despite the metabolic transformation of haloarchaea via a chunk of some 1000 genes of bacterial origin (Nelson-Sathi et al., 2012) – maybe through a syntrophic partner and failed endosymbiosis – haloarchaea show no intracellular complexity. So while the encounter of the mitochondrial ancestor with an archaeal host occurred by chance, the emergence of a complex cell biology upon endosymbiosis was a necessity. Once a cell biology that can chaperone an endosymbiont is established, however, additional endosymbionts may follow without noticeable changes to the host. The subsequent acquisition of the plastid added no extra compartments to the heterotrophic host that gave rise to the Archaeplastida, despite adding thousands of cyanobacterial genes to the host genome (Timmis et al., 2004). The same is true for an independent plastid acquisition by a rhizarian protist (Lhee et al., 2019) and likely many other endosymbiont-bearing protists (Husnik et al., 2021). Ever since eukaryogenesis, the cellular framework required for housing another prokaryote was in place. Some compartments have experienced physiological remodelling, such as the peroxisome (Islinger et al., 2010), but many components that evolved to service mitochondria during eukaryogenesis were recycled for the plastid: dynamins for fission (Fujimoto and Tsutsumi, 2014), redox balance through thioredoxins (Thormählen et al., 2017), and organelle digestion and recycling through the autophagosome (Ishida et al., 2014). One could add secondary endosymbioses, in which the acquisition of algae by eukaryotic hosts can lead to the stripping of all eukaryotic compartments of the endosymbionts (including their mitochondria), but that otherwise add no additional compartment or complexity to the host.
Morphologically complex life on Earth has a singular origin: eukaryogenesis. The LECA had evolved all canonical traits that we understand separates prokaryotic from eukaryotic life. Closing the gap between a simple FECA and a complex LECA by presupposing a complex FECA opens an equally wide gap between a simple and a complex archaeal host. Picturing FECA without an endosymbiont offers little explanation for the existence or emergence of eukaryotic traits and the lack thereof in prokaryotes (including asgard archaea), apart from the inevitable that all eukaryogenesis models face: the need to script the blueprint of a eukaryotic cell. And should then not all (asgard) archaea with a syntrophic partner be considered FECA in the sense that in principle they have the potential to become eukaryotic? For 4 billion years, prokaryotes have overall remained the same in terms of cellular complexity with some rare exceptions having evolved a single compartment type, but nothing vaguely similar to the conserved nature of the eukaryotic endomembrane system. Reflecting on eukaryotic traits and their cell biological connection to mitochondrial origin lets us conclude they are better understood as being selected for to service an endosymbiont and less so as means of acquiring one. Phylogeny guided models should connect interpretations to a physiological and cell biological rationale, while facing the challenge of resolving the fluid nature of the pangenomes of both host and endosymbiont genome throughout eukaryogenesis – we need to talk to phylogenetic trees, not only about them. Physiology (Imachi et al., 2020; Martin and Müller, 1998; Moreira, 1998; Spang et al., 2019), geochemistry (Mills et al., 2022), phylogenetics (Wu et al., 2022), and culturing and imaging (Imachi et al., 2020) all point to a syntrophic origin of eukaryotes involving two prokaryotic partners. The data suggests that first steps towards endosymbiosis in eukaryogenesis were of prokaryotic nature, that eukaryogenesis likely only solidified upon endosymbiosis, and that hence the definition of FECA should include an endosymbiont. |
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PMC9648968 | 36250630 | Xiaoming Fu,Heta P Patel,Stefano Coppola,Libin Xu,Zhixing Cao,Tineke L Lenstra,Ramon Grima | Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions | 17-10-2022 | stochastic,gene expression,inference,S. cerevisiae | Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging. | Quantifying how post-transcriptional noise and gene copy number variation bias transcriptional parameter inference from mRNA distributions
Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy but in experiments, cells may have two gene copies as cells replicate their genome during the cell cycle. While it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.
Transcription in single cells occurs in stochastic bursts (Suter et al., 2011; Larsson et al., 2019). Although the first observation of bursting occurred more than 40 years ago (McKnight and Miller, 1977), the precise mechanisms behind this phenomenon are still under active investigation (Nicolas et al., 2017; Tunnacliffe and Chubb, 2020). The direct measurement of the dynamic properties of bursting employs live-cell imaging approaches, which allow visualization of bursts as they occur in living cells (Donovan et al., 2019). However, in practice, such live-cell measurements are challenging because they are low-throughput and require genome-editing (Brouwer et al., 2020; Lenstra and Larson, 2016). To circumvent this, one can exploit the fact that bursting creates heterogeneity in a population. In this case, it is relatively straightforward to obtain a steady-state distribution of the number of mRNAs per cell from smFISH or single-cell sequencing experiments. These distributions have been used to infer dynamics by comparison to theoretical models. The simplest mathematical model describing bursting is the telegraph (or two-state) model (Peccoud and Ycart, 1995; Raj et al., 2006). In this model, promoters switch between an active and inactive state, where initiation occurs during the active promoter state. The model makes the further simplifying assumption that the gene copy number is one and that all the reactions are effectively first-order. The mRNA in this model can be interpreted as cellular (mature) mRNA since its removal via various decay pathways in the cytoplasm is known to follow single-exponential (first-order) decay kinetics in eukaryotic cells (Wang et al., 2002; Herzog et al., 2017). The solution of the telegraph model for the steady-state distribution of mRNA numbers has been fitted to experimental mature mRNA number distributions to estimate the transcriptional parameters (Raj et al., 2006; Kim and Marioni, 2013; Suter et al., 2011; Larsson et al., 2019). However, the reliability of the estimates of transcriptional parameters from mRNA distributions is questionable because the noise in mature mRNA (and consequently the shape of the mRNA distribution) is affected by a wide variety of factors. Recent extensions of the telegraph model have carefully investigated how mRNA fluctuations are influenced by the number of promoter states (Zhou and Zhang, 2012; Ham et al., 2020b), polymerase dynamics (Cao et al., 2020), cell-to-cell variability in the rate parameter values (Dattani and Barahona, 2017; Ham et al., 2020a), replication and binomial partitioning due to cell division (Cao and Grima, 2020), nuclear export (Singh and Bokes, 2012) and cell cycle duration variability (Perez-Carrasco et al., 2020). One way to avoid noise from various post-transcriptional sources is to measure distributions of nascent mRNA rather than mature mRNA, and then fit these to the distributions predicted by an appropriate mathematical model. A nascent mRNA (Zenklusen et al., 2008; Larson et al., 2009) is an mRNA that is being actively transcribed, that is it is still tethered to an RNA polymerase II (Pol II) moving along a gene during transcriptional elongation. Fluctuations in nascent mRNA numbers thus directly reflect the process of transcription. Because nascent mRNA removal is not first-order, an extension of the telegraph model has been developed (the delay telegraph model) (Xu et al., 2016). However, nascent mRNA data still suffers from other sources of noise due to cell-to-cell variability. For example in an asynchronous population of dividing cells, cells can have either one or two gene copies. In the absence of a molecular mechanism that compensates for the increase in gene copy number upon replication, cells with two gene copies which cannot be spatially resolved will have a different distribution of nascent mRNA numbers (one with higher mean) than cells with one gene copy. The importance of the cell cycle is illustrated by the finding (Zopf et al., 2013) that noisy transcription from the synthetic TetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. The estimation of transcriptional parameters from nascent mRNA data for pre- and post-replication phases of the cell cycle has, to the best of our knowledge, only been reported in Skinner et al., 2016. Interestingly, all the studies that estimate transcriptional parameters from nascent mRNA data (Skinner et al., 2016; Xu et al., 2015; Zoller et al., 2018; Senecal et al., 2014; Fritzsch et al., 2018) do not compare them with transcriptional parameters estimated from cellular (mature) mRNA data measured in the same experiment. Similarly, a quantitative comparison between inference from cell-cycle-specific data and data which contains information from all cell cycle phases is lacking. Likely, this is because it is considered evident that quantifying fluctuations earlier in the gene expression process and adjusted for the cell-cycle will improve estimates. However, nascent mRNA distributions are technically more challenging to acquire than mature mRNA distributions; and inference from nascent mRNA distributions is substantially more complex (Xu et al., 2016). Thus, it still needs to be shown that acquiring nascent mRNA data is a necessity from a parameter inference point of view, i.e. that it leads to significantly different and more robust estimates than using mature mRNA data. We also note that current studies report parameter inference from organisms where it is possible to label introns to identify mRNA located at the transcription site. This is not possible in many yeast genes and other microorganisms, and in these cases it is unclear how to correct parameter estimates for uncertainty in the transcription site location. In this paper, we sought to understand the precise impact of post-transcriptional noise and cell-to-cell variability on the accuracy of transcriptional parameters inferred from mature mRNA data. The fitting algorithms (for mature and nascent mRNA data) were first tested on simulated data, where limitations of the algorithms were uncovered in accurately estimating the transcriptional parameters in certain regions of parameter space. The algorithms were then applied to four independent experimental data sets, each measuring GAL10 mature and nascent mRNA data from smFISH in galactose-induced budding yeast, conditional on the stage of the cell cycle (G1 or G2) for thousands of cells. Comparison of the transcriptional parameter estimates allowed us to separate the influence of ignoring cell cycle variability from that of post-transcriptional noise (mature vs nascent mRNA data). We found that only fitting of nascent cell-cycle data, corrected for measurement noise (due to uncertainty in the transcription site location), provided good agreement with measurements from live-cell data. Cell-cycle specific analysis also revealed that upon transition from G1 to G2, yeast cells show dosage compensation by reducing burst frequency, similar to mammalian cells (Padovan-Merhar et al., 2015). Our systematic comparison highlights the challenges of obtaining kinetic information from static data, and provides insight into potential biases when inferring transcriptional parameters from smFISH distributions.
To understand the accuracy of the inference algorithms from nascent and mature mRNA data, in various regions of parameter space, (i) we generated synthetic data using stochastic simulations with certain known values of the parameters; (ii) applied the inference algorithms to estimate the parameters from the synthetic data; (iii) compared the true and inferred kinetic parameter values. The generation of synthetic mature mRNA data (mature mRNA measurements in each of 104 cells) using stochastic simulations of the telegraph model (Figure 1a) is described in Methods Sections Mathematical model and Generation of synthetic mature mRNA data. The inference algorithm is described in detail in Methods Section Steps of the algorithm to estimate parameters from mature mRNA data. It is based on a maximization of the likelihood of observing the single cell mature mRNA numbers measured in a population of cells. The likelihood of observing a certain number of mature mRNA numbers from a given cell is given by evaluating the telegraph model’s steady-state mature mRNA count probability distribution. For nascent RNA data, we used stochastic simulations of the delay telegraph model (Figure 1b) to generate the position of bound Pol II molecules from which we constructed the synthetic smFISH signal in each of 104 cells (Methods Section Generation of synthetic nascent mRNA data). An inference algorithm estimates the parameters, based on a maximization of the likelihood of observing the single cell total fluorescence intensity measured in a population of cells (Methods Section Steps of the algorithm to estimate parameters from nascent mRNA data). Note that the likelihood of observing a certain fluorescence signal intensity from a cell is given by extension of the delay telegraph model (but not directly by the delay telegraph model itself) to account for the smFISH probe positions. This extension takes into account that the experimental fluorescence data used in this manuscript was acquired from smFISH of PP7-GAL10 in budding yeast, where probes were hybridized to the PP7 sequences. Because the PP7 sequences are positioned at the 5’ of the GAL10 gene, the fluorescence intensity of a single mRNA on the DNA locus resembles a trapezoidal pulse (see Figure 1 for an illustration). As the Pol II molecule travels through the 14 repeats of the PP7 loops, the fluorescence intensity increases as the fluorescent probes binds to the nascent mRNA (this is the linear part of the trapezoidal pulse). However, once all 14 loops on the nascent mRNA are bound by the fluorescent probes, the intensity of a single mRNA reaches maximal intensity and the plot plateaus as the RNA elongates through the GAL10 gene body before termination and release. The total fluorescent signal density function is hence given by where is the density function of the signal given there are bound Pol II molecules and is the steady-state solution of the delay telegraph model giving the probability of observing bound Pol II molecules for the parameter set . In Methods Section Mathematical model, we show how can be approximately calculated for the trapezoidal pulse. Hence Equation (1) represents the extension of the delay telegraph model to predict the smFISH fluorescent signal of the transcription site. Note that both of these inference algorithms were used to infer the promoter switching and initiation rate parameters. The degradation rate and the elongation time were not estimated but assumed to be known. The inference and synthetic data generation procedures are summarised and illustrated in Figure 1d. The accuracy of inference was first calculated as the mean of the relative error in the estimated parameters , and (for its definition see Methods, Equation (6)); note that this error measures deviations from the known ground truth values. Figure 2a shows, by means of a 3D scatter plot, the ratio of the mean relative error from nascent mRNA data (using delay telegraph model) and the mean relative error from mature mRNA data (using the telegraph model) for 789 independent parameter sets sampled on a grid (for each of these sets, we simulated 104 cells). The overall bluish hue of the plot suggested that the mean relative error from nascent mRNA data was typically less than the error from mature mRNA data. This was confirmed in Figure 2b where the same data was plotted but now as a function of the fraction of ON time (defined as ). Out of 789 parameter sets, for 483 of them () the inference accuracy was higher when using nascent mRNA data. Thus far, we have implicitly assumed that fluctuations in both nascent and mature mRNA are due to transcriptional bursting. However, it is clear that mature mRNA data exhibit a higher degree of noise due to post-transcriptional processing. For example, it has been shown that transcriptional noise is typically amplified during mRNA nuclear export (Hansen et al., 2018). In addition, cell-to-cell variation in the number of nuclear pore complexes has recently been identified as the source of heterogeneity in nuclear export rates within isogenic yeast populations (Durrieu et al., 2022). To take into account these additional noise sources, which we call external noise, we added noise to the initiation rate in the telegraph model since this rate implicitly models all processes between the synthesis of the transcript and the appearance of mature mRNA in the cytoplasm. Specifically, for each of the 789 parameter sets previously used, we changed to where the latter is a log-normal distributed random variable such that its mean is and its standard deviation is equal to 0.05 of the mean (5% external noise). Note that this implies that at the time of measurement, each cell in the population had a different value of the initiation rate. Simulations with this perturbed set of parameters led to a synthetic mature mRNA data set from which we re-inferred parameters using the telegraph model. In Figure 2c we show the ratio of mean relative error from nascent mRNA data and the mean relative error from perturbed mature mRNA data as a function of the fraction of ON time, . The percentage of parameters where nascent mRNA is more accurate is slightly increased compared to the data without noise (64% versus 61% of the parameters; compare Figure 2c and Figure 2b). However, the addition of even more noise (10% external noise added to the initiation rate) increases the inference accuracy for 91% of the parameter sets when the nascent mRNA data is used (Appendix 1 and Appendix 1—figure 1). To obtain more insight into the accuracy of the individual parameters, we next plotted the median relative error of transcriptional parameters , burst size and the inferred fraction of ON time, as a function of the true fraction of ON time (Figure 2d). We compared the results using synthetic nascent mRNA, synthetic mature mRNA data and synthetic mature mRNA with 5% external noise. The median of the relative error for each transcriptional parameter (as given by the second equation of Equation 8) was obtained for the subset of the 789 parameter sets for which the true fraction of ON time falls into the interval where . From the plots, the following can be deduced: (i) the errors in (the burst frequency), and the burst size tend to increase with while the rest of the parameters ( and the estimated value of ) decrease; (ii) for small , the best estimated parameters are the burst frequency and size while for large , it was and the estimated value of . The worst estimated parameter was , independent of the value of ; (iii) the addition of external noise to mature mRNA data had a small impact on inference for small ; in contrast, for large the noise appreciably increased the relative error in and to a lesser extent the error in the other parameters too. Additionally, in Appendices 1 and 2 we show that (i) independent of the accuracy of parameter estimation, the best fit distributions accurately matched the ground truth distributions (Appendix 1 and Appendix 1—figure 2); (ii) the parameters ordered by relative error were in agreement with the parameters ordered by sample variability (Appendix 1 and Appendix 1—table 1) and by profile likelihood error (Kreutz et al., 2013) (Appendix 1, Appendix 1—tables 2 and 3). Since from experimental data, only the sample variability and the profile likelihood error are available, it follows that the results of our synthetic data study in Figure 2 based on relative error from the ground truth have wide practical applicability; (iii) stochastic perturbation of the mature or nascent mRNA data (due to errors in the measurement of the number of spots and the fluorescent intensity) had little effect on the inference quality, unless the gene spent a large proportion of time in the OFF state (Appendix 1—tables 4 and 5); (iv) if one utilized the conventional telegraph model to fit the nascent data generated by the delay telegraph model, it was possible to obtain a distribution fitting as good as the delay telegraph model but with low-fidelity parameter estimation (Appendix 2, Appendix 2—figure 1 and Appendix 2—table 1). Analytically, the telegraph model is only an accurate approximation of the delay telegraph model when the promoter switching timescales are much longer than the time spent by Pol II on a gene or the off switching rates are very small such that gene expression is nearly constitutive. In summary, by means of synthetic experiments, we have clarified how the accuracy of the parameter inference strongly depends on the type of data (nascent or mature mRNA) and the fraction of time spent in the ON state (which determines the mode of gene expression).
Now that we have introduced the inference algorithms and tested them thoroughly using synthetic data, we applied the algorithms to experimental data (see Method Section Experimental data acquisition and processing for details of the data acquisition). Note that in what follows, delay telegraph model refers to the extended delay telegraph model that accounts for the smFISH probe positions that was used to predict the smFISH fluorescent signal of the transcription site.
We have four independent datasets from which we determined mRNA count and nascent RNA distributions. Figure 3a shows an example cell with mature single RNAs in the cytoplasm, and a bright nuclear spot representing the site of nascent transcription. Spots and cell outlines were identified using automated pipelines. Importantly, to obtain an accurate estimation of transcriptional parameters, the experimental input distributions of mRNA count and nascent RNAs require high accuracy. We therefore first determined how technical artifacts in the analysis affects the inference estimates. First, if the number of mRNA transcripts per cell is high, accurate determination of the number of transcripts may be challenging, as transcripts may overlap. To determine if this occurred in our datasets, we analyzed the distributions of intensities of the cytoplasmic spots, which revealed unimodal distributions where ∼90% of the detected spots fell in the range 0.5× median – 1.5× median (Figure 4a). We therefore concluded that overlapping spots are not a large confounder in our data. In fact, in our experiments, the number of detected mature mRNA transcripts per cell was lower than expected, based on the number of nascent transcripts (compare Figure 3 with Figure 4). This discrepancy between nascent and mature transcripts likely arises because the addition of the PP7 loops to the GAL10 RNA destabilizes the RNA, resulting in faster mRNA turnover compared to most endogenous RNAs (Miller et al., 2011; Wang et al., 2002; Holstege et al., 1998; Geisberg et al., 2014). Previously, both shorter and longer mRNA half-lives from the addition of stem loops have been observed, which may be caused because changes in the 5’ UTR length or sequence affect its recognition by the mRNA degradation machinery (Heinrich et al., 2017; Tutucci et al., 2018; Garcia and Parker, 2015). In our case, we note that such high turnover should aid transcriptional parameter estimates, as it closely reflects transcriptional activity. A second possible source of error is cell segmentation. To test how cell segmentation errors contribute to the mature mRNA distribution and the transcriptional bursting estimates, we compared two independent segmentation tools, where segmentation 1 often resulted in missed spots (Figure 3b), resulting in an underestimation of the mean mRNA count and of the variance (compare Figure 3b and c). We inferred the transcriptional parameters using the algorithm described in Methods Section Steps of the algorithm to estimate parameters from mature mRNA data. In the absence of an experimental measurement of the degradation rate, we could only estimate the three transcriptional parameters normalised by . The best fits of dataset 1 are shown in (Figure 3b and c) and the transcriptional parameters (for all four datasets) are summarized in (Figure 3e). Note that the estimated parameters for all four datasets, using both segmentations, are shown in Appendix 3—table 1 and the associated best fit distributions in Appendix 3—figure 1a. Notably the segmentation algorithms led to similar estimates for the burst frequency but considerably different estimates for the rest of the parameters. In particular segmentation 1 suggested that burst expression is infrequent (≈20% of the time) whereas segmentation 2 was consistent with burst expression occurring half of the time. Given that accurate cell segmentation remains challenging, this analysis illustrates that parameter estimation from mature mRNA counts may be affected by technical errors. For the remainder of the mature mRNA analysis, we have used only segmentation 2 data. Lastly, it may be challenging to distinguish the nascent transcription site from a mature RNA, especially if few nascent RNAs are being produced. Either one can decide to include all cellular spots in the total mRNA count, including the transcription site, with the result that the number of mature transcripts is overestimated with one RNA for cells which show a transcription site. Or conversely, one can decide to exclude the transcription site by subtracting one spot from each cell, with the result that the number of mature mRNAs may be underestimated by one RNA for cells that are transcriptionally silent. To understand how this choice affects the accuracy of parameter inference, we compared both options in (Figure 3c, d and e), where seg2 included all spots, and seg2-TS excluded transcription sites (by subtracting 1 from each cell). The estimated parameters for all four datasets are shown in Appendix 3—table 1 and the associated best fit distributions in Appendix 3—figure 1a. Although the mean was lower when transcription sites were excluded, all the parameters except the burst frequency were within the error, indicating that the choice of whether or not to include the transcription site in the mature mRNA count had a small influence on parameter estimation. For the remainder of the analysis, we included all spots, and counted the transcription site as one RNA.
The above analysis was performed using the merged data from all cells, irrespective of their position in the cell cycle. The inferred parameters of all four datasets are shown in Figure 3g (grey). To understand the effect of the cell cycle on these parameter estimates, we compared this inference with cell-cycle-specific data. We used the integrated nuclear DAPI intensity as a measure for DNA content to classify cells into G1 or G2 cells (Figure 3f (left)) to obtain separate mature mRNA distributions for G1 and G2 cells. To infer the transcriptional parameters from mature mRNA data of cells in G1, the inference protocol remained the same. However for cells in the G2 stage, this protocol needed to be altered since G2 cells have two gene copies, whereas the solution of the telegraph model assumes one gene copy. Assuming the transcriptional activities of the two gene copies are independent, the distribution of the total molecule number is the convolution of the molecule number (obtained from the telegraph model) with itself for mature mRNA data. This convolved distribution was used in steps (ii) and (iii) of the inference algorithm in Methods Section Steps of the algorithm to estimate parameters from mature mRNA data. A difference between our method of estimating parameters in G2 from that in the literature (Skinner et al., 2016) is that we do not assume that the burst frequency is the only parameter that changes upon replication, and we estimated all transcription parameters simultaneously. Note that the independence of gene copy transcription has been verified for genes in some eukaryotic cells (Skinner et al., 2016) where the two copies can be easily resolved. For yeast data, as we are analyzing in this paper, it is generally not possible to resolve the two copies of the allele in G2 because they are within the diffraction limit. However, in the absence of experimental evidence, the independence assumption is the simplest reasonable assumption that we could make (see later for a relaxation of this assumption). For both G1 and G2 cells, we performed inference for cell-cycle specific mature mRNA data, the results of which are shown in Figure 3f (centre and right) and Figure 3g – see Appendix 3—table 2 for the confidence intervals of the estimates calculated using profile likelihood. As expected, the mean number of mRNAs in G2 cells was larger than that in G1 cells. For both merged and cell-cycle specific data, the parameters ordered by increasing variability of the estimates from independent samples (the standard deviation divided by the mean) were: , , , burst size and , and the same order was predicted by the relative error (from ground truth values) from our synthetic experiments (compare with and in the middle and right panels of Figure 2d) and by sample variability (Appendix 1). In Appendix 3 and Appendix 3—table 3 we show that the relaxation of the assumption of independence between the allele copies in G2 (by instead assuming perfect state correlation of the two alleles) had practically no influence on the inference of the two best estimated parameters (, ). A comparison of the two types of data predicted different behaviour (Figure 3g bottom): merged data indicated behaviour consistent with the gene being ON half of the time and small burst sizes, while cell-cycle-specific data implied the gene is ON ≈80% of the time with large burst sizes. We note that the burst sizes have considerable sample variability, exemplifying burst size estimates of transcriptional parameters from mature mRNA distributions have to be treated with caution. Nevertheless, in line with this high fraction ON and large burst size, which start to approach constitutive expression, the variation introduced by the transcription kinetics is relatively modest with Fano factors not far from one: for merged data and for cell-cycle data (the slightly higher value for merged data likely was due to heterogeneity stemming from varying gene copy numbers per cell). Comparing the mean rates between the G1 and G2 phases, we found that , , decreased while and the burst size increased upon replication. However, taking into account the variability in estimates across the four datasets, the only two parameters which were well-separated between the two phases were and . These two parameters decreased by 65% and 21%, respectively, which suggests that upon replication, there are mechanisms at play which reduce the expression of each copy to partially compensate for the doubling of the gene copy number (gene dosage compensation) (Skinner et al., 2016). In conclusion, what is particularly surprising in our analysis is the differences in the inference results using merged and cell-cycle specific data: the former suggests the gene spends only half of its time in the ON state while the latter implies the gene is mostly in its ON state.
To determine the number of nascent transcripts at the transcription site, we selected the brightest spot from each nucleus and normalized its intensity to the median intensity of the cytoplasmic spots. As the distribution of intensities of the cytoplasmic mRNAs followed a narrow unimodal distribution, its median likely represents the intensity of a single RNA (orange distribution in the central panel of Figure 4a). The inference of transcriptional parameters using the merged data was done using the algorithm described in Methods Section Steps of the algorithm to estimate parameters from nascent mRNA data. Similar to above, to account for two gene copies in G2 cells, we assumed that the transcriptional activities of the two gene copies are independent. The distribution of the total fluorescent signal from both gene copies was the convolution of the signal distribution (obtained from the extended delay telegraph model, i.e. Equation (1)) with itself. This convolved distribution was then used in steps (ii) and (iii) of the inference algorithm. The inference of transcriptional parameters from nascent RNA data was done using a fixed elongation time, which was measured previously at a related galactose-responsive gene (GAL3) at (Donovan et al., 2019). Since the total transcript length is (see Figure 1c), the elongation time ( in our model) is . The fixed elongation rate enabled us to infer the absolute values of the three transcriptional parameters and . Best fits of the extended delay telegraph model to the distribution of signal intensity of nascent mRNAs at the transcription site are shown in Figure 4a and b for dataset 1; for the other datasets see Appendix 4—figure 1. The corresponding estimates of the transcriptional parameters are shown in Appendix 4—table 1 and also illustrated by bar charts in Figure 4c. The confidence intervals of the transcriptional parameters (computed using the profile likelihood method) are shown in Appendix 4—table 2. Comparing this estimation with that from mature mRNA, we observed that in both cases for merged data and in the range for cell-cycle-specific data. Also in both cases, the Fano factors of merged data were larger than those of cell-cycle-specific data. Hence, we are confident that not accounting for the cell cycle phase leads to an over-estimation of the time spent in the OFF state and of the Fano factor. In addition, comparing the burst sizes in Figure 3g and Appendix 4—table 1, we found that not taking into account post-transcriptional noise (by using mature mRNA data) led to an lower estimation of the burst size (2.6-fold, 2.6-fold, and 1.1-fold lower for inference from merged, G1 and G2 data, respectively). We note that it would be useful to directly compare the absolute estimates of the other transcriptional parameters from mature and nascent mRNA data. However, this was not possible because the telegraph model only estimates the switching rates and the initiation rate scaled by the degradation rate, and the latter is unknown. On the other hand, the estimates from nascent data were rates multiplied by the average elongation time, which is known and hence the absolute rates can be estimated from nascent mRNA data only. The only quantities that could be directly compared were the burst size and the fraction of ON time, since these are both non-dimensional. Comparing the variability of the parameter estimates, we found that and were the parameters with the smallest variability across samples for the nascent data, as for inference from mature data. However, the inferred parameter variability across samples was on average about 2.5-fold lower for nascent data compared to mature mRNA data (this was obtained by computing the standard deviation divided by the mean for each parameter and then averaging over all parameters and over merged, G1 and G2 data). Likely this is because nascent data does not suffer from post-transcriptional noise. Indeed, synthetic experiments suggested that the errors in parameter inference using nascent data are often less than those in mature data when (Figure 2d). In summary, we have more confidence in the parameter estimates from nascent data, in particular those from cell-cycle separated data. To further investigate the hypothesis that estimates from cell-cycle-specific data are more accurate than merged data, we compared the estimates from merged and cell-cycle-specific data to previous live-cell transcription measurements of the same gene (Donovan et al., 2019). Because live-cell traces and simulated traces with the estimated transcriptional parameters are difficult to compare directly, we instead compared their normalized autocorrelation functions (ACFs). Specifically, the live-cell traces displayed cell-to-cell variation in overall fluorescent intensities arising from differences in the PP7 coat protein expression level, precluding a direct comparison of the live-cell intensities with the smFISH distributions. The normalized ACFs are normalized per trace and thus can be used to directly compare the kinetics. For this, we fed the parameter estimates to the SSA to generate synthetic live-cell data and then calculated the corresponding ACF (Appendix 5). We found that the estimates from cell-cycle-specific data produced ACFs that match the live-cell data closer than that from the merged data (Figure 4d). This was also clear from the sum of squared residuals which for each dataset was smaller for the ACF computed using the cell-cycle-specific estimates rather than those from merged data (Figure 4e). Using nascent data, we also reinvestigated the hypothesis that the gene exhibits dosage compensation. Comparing the mean rates between the G1 and G2 phases, we found that , , , decreased while the burst size increased upon replication. However, taking into account the variability in estimates across the four datasets, the only two parameters which were cleanly separated between the two phases were and . These two decreased by 41% and 5%, respectively. These results had some similarity to those deduced from cell-cycle separated mature mRNA data (the decrease of ) but they also displayed differences. Namely, from mature mRNA data it was predicted that decreased upon replication while from nascent data we predicted that did not change and it was rather that decreased by a small degree. The decrease of the burst frequency after replication has also been reported for some genes in mammalian cells (Skinner et al., 2016; Padovan-Merhar et al., 2015), indicating that this could be a general mechanism for gene dosage compensation. Our results are consistent with a population-based ChIP-seq study (Voichek et al., 2016) that showed DNA dosage compensation after replication in budding yeast. We note that our single-cell analysis only revealed partial dosage compensation, where the mean signal intensity of nascent mRNAs in G2 is not the same as in G1, but 1.7-fold higher in G2 than in G1 (Figure 4c).
Although inference on cell cycle separated data outperformed inference on merged data, we noticed that the corresponding best fit distributions did not match well to the experimental signal distributions in the lower bins (Figure 4b and Appendix 4—figure 1). In all cases, the experimental distributions showed high intensities in bins 1, 2, and 3, which was likely an artifact of the experimental data acquisition system. Since we defined the transcription site as the brightest spot, this implies that in the absence of a transcription site, a mature transcript can be misclassified as a nascent transcript. We therefore investigated two methods to correct for this, the ‘rejection’ method and the ‘fusion’ method. The rejection method removed all data associated with the first bins of the experimentally obtained histogram of fluorescent intensities (Figure 5a shows the fits for dataset 1; for the other datasets see Appendix 4—figure 2). We found that the parameter estimates varied strongly when the number of bins from which data was rejected () was changed (Figure 5b; see also Appendix 4—table 3). Although the distributions fit well to the experimental histograms (Appendix 4—figure 1), comparison with the live-cell normalized ACF indicated that the estimates actually became worse than non-curated estimates, with a higher sum of squared residuals (Figure 5c and d). The rejection method therefore does not produce reliable estimates. Next, we considered another data curation method which we call the fusion method. This works by setting to zero all fluorescent intensities in a cell population which were below a certain threshold. In other words, we fused or combined the first bins of the experimentally obtained histogram of fluorescent intensities, thereby taking into account that the true intensity of bin 0 was artificially distributed over some of the first bins. Figure 6 and Appendix 4—table 4 show that the fusion method led to estimates that varied little with which enhanced our degree of confidence in them (note that is the same as the uncurated data). The peak at the zero bin for both G1 and G2 was better captured using the fusion method than using non-curated data (compare Figure 4b and Appendix 4—figure 1, with Figure 6b). Comparison to the autocorrelation function of the live-cell data shows that correction with the fusion method also led to improved transcriptional estimates, as indicated by a reduction in the sum of the squared residuals for all four data sets (Figure 6c). Overall, we conclude that for inferring parameters from the smFISH data, the optimal method is to use nascent cell-cycle-specific data, corrected by the fusion method. The optimally inferred parameters for the four data sets in our study are those given in Appendix 4—figure 2d. The profile likelihood estimates of the 95% confidence intervals of these parameters are shown in Appendix 4—table 5. Note that in line with our synthetic data study in Figure 2, the parameters suffering from the least sample variability were and . The rest of the parameters ( and burst size) suffered more sample variability because the fraction of ON time was high; however since their standard deviation divided by the mean (computed over the four datasets) was not high (in the range of 10-20%), they still can be regarded as useful estimates. Note also that the previous prediction that gene dosage compensation involves regulation of the burst frequency did not change upon correction of the nascent data using the fusion method. All these results were deduced assuming that the two copies in G2 are independent from each other. Inferring rates under the opposite assumption of perfectly synchronized copies (Appendix 4—table 6) gave very similar estimates for and (to be expected since according to the synthetic data study, these two are the most robustly estimated parameters for genes spending most of their time in the active state) but different estimates for the rest of the parameters. While such perfect synchronization of alleles is unlikely, some degree of synchronization is plausible and further improvement of the transcriptional parameters in the G2 phase will require its precise experimental quantification.
In this study, we compared the reliability of transcriptional parameter inference from mature and nascent mRNA distributions, with and without taking into account the cell cycle phase. Although these distributions come from the same experiment, we found that the different fits produced very different parameter estimates, ranging from small bursts to very large bursts. Comparison to live-cell data revealed that the optimal inference method is to use nascent mRNA data that is separated by cell cycle. Our findings illustrate the risk of inferring transcriptional parameters from fitting of mRNA distributions. First of all, as we have shown, these fits are sensitive to the segmentation method which can lead to large errors in the estimates. Secondly, the most common method of parameter inference in the literature is fitting of mature mRNA distributions that are not separated by cell cycle (Larsson et al., 2019; Raj et al., 2006; Zenklusen et al., 2008). Obtaining such distributions is straightforward using methods such as smFISH, where one can directly count the number of mRNAs per cell. Additionally, with the advance of single-cell mRNA sequencing technologies, it is possible to obtain mRNA distributions for many genes simultaneously and it is tempting to use these to estimate bursting behaviour across the genome (Kim and Marioni, 2013; Larsson et al., 2019). However, our comparisons on the same dataset show that the values obtained from mature mRNA fits (using merged data) can be significantly different from the optimal values (using nascent cell-cycle separated data corrected using the fusion method), with underestimation of the burst sizes of almost 10-fold and underestimation of the active fraction of more than 1.5-fold. These results indicate that parameter inference from merged mature mRNA data should be treated with caution. There were smaller differences between the burst size and the active fraction inferred from cell-cycle separated mature and nascent data (only these two can be directly compared because these are non-dimensional); however the relative errors in the estimates (computed over the four datasets) were more than twofold higher for mature data likely due to post-transcriptional noise which nascent data is free from. It is more common to fit mature distributions rather than nascent distributions because nascent distributions are technically more challenging to obtain. As nascent single-cell sequencing methods are still in the early phase (Hendriks et al., 2019), the only method available so far for nascent measurements is smFISH (Patel et al., 2021). In such smFISH experiments, intronic probes can be used to specifically label nascent RNA, although there may be some effects of splicing kinetics on the distribution (Wan et al., 2021). If introns are not present, like for most yeast genes, one can use exonic probes instead (Zenklusen et al., 2008). Since exonic probes label both nascent and mature mRNA transcript, it may be challenging to identify the nascent transcription site unambiguously, especially at lower transcription levels. We show in this manuscript that the fusion method can correct for this bias by combining bins below k RNAs, which results in an improvement of the parameter estimates. Our analysis also emphasizes the importance of separately analyzing G1 and G2 cells (Skinner et al., 2016). It is important to note that for cell-cycle-specific analysis, experimental adjustments or cell-cycle synchronized cultures are not required. Although asynchronous cultures consist of a mix G1, S and G2 cells, the integrated DNA intensity of the nucleus of each cell, for example from a DAPI signal, can be used to separate these cells by cell cycle phase in silico (Skinner et al., 2016; Roukos et al., 2015). As most smFISH experiments already include a DNA-labelled channel, adding an extra analysis step should in principle not limit the incorporation of this step in future smFISH fitting procedures. Even with our optimal fitting strategy, there is a residual error of the simulated ACF and the measured ACF from live-cell measurements. This difference may be the result of different experimental biases of the two measurements. For example, live-cell measurements have a detection threshold below which RNAs may not be detected. In addition, live-cell measurements include cells in S phase, which are not analyzed in smFISH. There could also be differences in the exact percentage of G1 and G2 cells, or other noise sources between live-cell and smFISH experiments. Alternatively, the fit may be imperfect because there might be parameter sets, others than the ones which our inference algorithm found, which provide an accurate fit of the nascent mRNA distribution and perhaps an even better fit to the ACF than we found. We cannot exclude this possibility because we estimated to be and using synthetic data we showed that the accuracy of some parameters ( and the burst size) deteriorated as approached 1 (Figure 2d). Another factor which could explain the residual error between the simulated ACF and the measured ACF is that perhaps the two-state model may be too simplistic to cover the true promoter states in living cells and may therefore not be able to describe the true in vivo kinetics. The promoter may switch between more than 2 states, or there may be sources of extrinsic noise other than the cell cycle that contribute to the heterogeneity. Previous studies have for example identified extrinsic noise on the elongation rate (Fritzsch et al., 2018). However, these more complex transcription models also have more parameters, which in practice often means that very few will be identifiable with the current set of experimental observations. To fit these models, one requires temporal data on the transcription kinetics (Fritzsch et al., 2018; Rodriguez et al., 2019), or simultaneous measurements of various sources of extrinsic noise, such as single-cell transcription factor concentration and RNA polymerase number measurements, cellular volume, local cell crowding, etc, which are often not available in standard smFISH experiments (Battich et al., 2015; Foreman and Wollman, 2020). Nevertheless, given that there is no explicit time component in smFISH data, the closeness of the simulated ACF to the measured ACF provides confidence we are close to the real values. The optimal parameter set (Figure 6d) indicates long ON promoter times of 75 s, during which almost 50 RNAs are produced in a burst. Large burst sizes (>70) have been previously reported for mouse embryonic stem cells (Skinner et al., 2016, mouse hepatocytes Bahar Halpern et al., 2015 and human fibroblasts Larsson et al., 2019). The large burst size and high active fraction of 0.78 suggests that GAL10 expression is reaching its limit of maximal expression, which may not be surprising as it is already one of the most highly expressed genes in yeast. It is also interesting to note that the ON time of 75 s is longer than the residence time of a single transcript (47 s), which means that RNA polymerases in the beginning of a burst have already left the locus before the burst has finished. The optimal parameter set (Figure 6d) also indicates partial gene dosage compensation. Specifically the burst frequency per gene copy () in the G2 phase is 0.66 that in the G1 phase; the other transcriptional rates are not significantly different between the two cell cycle phases. The fold change in the burst frequency per gene copy was previously estimated for the and genes to be 0.63 and 0.71 respectively, in mouse embryonic stem cells (Skinner et al., 2016). The similarity of our estimate of the fold change to those previously measured could be explained by the results of a recent study (Jia et al., 2021); using a detailed model of gene expression, it was shown that in the absence of a dependence of the initiation rate on cell volume, gene dosage compensation optimally leads to approximate mRNA concentration homeostasis when the fold change in the burst frequency upon DNA replication is . In conclusion, obtaining kinetic information from static distributions can introduce biases. However, we show that it is possible to obtain reasonable estimates that agree with live-cell measurements, if one infers parameters from nascent mRNA distributions that are accounted for cell cycle phase.
The steady-state solution of the telegraph model of gene expression (Peccoud and Ycart, 1995) gives mature mRNA distributions. The reaction steps in this model are illustrated in Figure 1a. Next we describe the generation of synthetic mature mRNA data and the algorithm used to infer parameters from this data.
We generate parameter sets on an equidistant mesh grid laid over the space: where the units are inverse minute. Furthermore we apply a constraint on the effective transcription rate In each of the three dimensions of the parameter space, we take 10 points that are equidistant, leading to a total of 1000 parameter sets which reduce to 789 after the effective transcription rate constraint is enforced. We additionally fix the degradation rate min-1. Note that we choose not to vary the degradation rate (as we did for the other three parameters) since it is not possible to infer all four rates simultaneously – this is because the steady-state solution of the telegraph model is a function of the non-dimensional parameter ratios and (Raj et al., 2006). Once a set of parameters is chosen, we use the stochastic simulation algorithm (SSA Gillespie, 2007) to simulate the telegraph model reactions in Figure 1a and generate 104 samples of synthetic data. Note that each sample mimicks a single cell measurement of mature mRNA.
The inference procedure consists of the following steps: (i) select a set of random transcriptional parameters; (ii) use the solution of the telegraph model to calculate the probability of observing the number of mature mRNA measured for each cell; (iii) evaluate the likelihood function for the observed data; (iv) iterate the procedure until the negative log-likelihood is minimized; (v) the set of parameters that accomplishes the latter provides the best point-estimate of the parameters of the telegraph model that describes the measured mature mRNA fluctuations. For step (i), we restrict the search for optimal parameters in the following region of parameter space The degradation rate is fixed to min-1. Step (ii) can be obtained either by computing the distribution from the analytical solution (Peccoud and Ycart, 1995 or by using the finite state projection (FSP) method Munsky and Khammash, 2006). Here, for the sake of computational efficiency, we use the FSP method to compute the probability distribution of mature mRNA numbers. For step (iii) we calculate the likelihood of observing the data given a chosen parameter set where is the probability distribution of mature mRNA numbers obtained from step (ii) given a parameter set , nj is the total number of mature mRNA from cell and is the total number of cells. Steps (i) and (iv) involve an optimization problem. Specifically we use a gradient-free optimization algorithm, namely adaptive differential evolution optimizer (ADE optimizer) using BlackBoxOptim.jl (https://github.com/robertfeldt/BlackBoxOptim.jl; Feldt and Stukalov, 2022) within the Julia programming language to find the optimal parameters The minimization of the negative log-likelihood is equivalent to maximizing the likelihood. Note the optimization algorithm is terminated when the number of iterations is larger than 104; this number is chosen because we have found that invariably after this number of iterations, the likelihood has converged to some maximal value. Note that the inference algorithm is particularly low cost computationally, with the optimal parameter values estimated in at most a few minutes. Once the best parameter set is found, we calculate the mean relative error (MRE) which is defined as where and represent the -th estimated and true parameters respectively, and denotes the number of the estimated parameters. Thus, the mean relative error reflects the deviation of the estimated parameters from the true parameters.
The steady-state solution of the delay telegraph model (Xu et al., 2016) gives the distribution of the number of bound Pol II. In Appendix 6, we present an alternative approach to derive the steady-state solution. The reaction steps are illustrated in Figure 1a. The position of a Pol II molecule on the gene determines the fluorescence intensity of the mRNA attached to it. In particular for fluorescence data acquired from smFISH PP7-GAL10, the fluorescence intensity of a single mRNA on the DNA locus looks like a trapezoidal pulse (see Figure 1b for an illustration). This presents a problem because although we can predict the distribution of the number of bound Pol II using the delay telegraph model, we do not have any specific information on their spatial distribution along the gene. However, since the delay telegraph model implicitly assumes that a Pol II molecule has fixed velocity and that Pol II molecules do not interact with each other (via volume exclusion), it is reasonable to assume that in steady-state, the bound Pol II molecules are uniformly distributed along the gene. This hypothesis is confirmed by stochastic simulations of the delay telegraph model where the position of a Pol II molecule is calculated as the product of the constant Pol II velocity and the time since its production. By the uniform distribution assumption and the measured trapezoidal fluorescence intensity profile, it follows that the signal intensity of each bound Pol II has the density function defined by where as defined in Figure 1b. The indicator function if and only if and is the Dirac function at 1. The probability of the signal being between 0 and 1 is due to the first part of the trapezoid function and hence is multiplied by which is the probability of being in this region if Pol II is uniformly distributed. Similarly, the probability of being 1 is due to the L2 part of the trapezoid and hence the probability is by the uniform distribution assumption. Note that the signal from each Pol II is at most 1 because in practice, the signal intensity from the transcription site is normalized by the median intensity of single cytoplasmic mRNAs (Zenklusen et al., 2008). The total signal is the sum of the signals from each bound Pol II. Hence, the density function of the sum is given by the convolution of the signal densities from each bound Pol II. Defining as the density function of the signal given there are bound Pol II molecules, we have that is the –th convolution power of , that is where is the Dirac function at. Finally we can write the total fluorescent signal density function as where is the steady-state solution of the delay telegraph model giving the probability of observing bound Pol II molecules for the parameter set . Hence Equation (8) represents the extension of the delay telegraph model to predict the smFISH fluorescent signal of the transcription site. Comparison to the algorithm in Xu et al., 2016. Both algorithms take into account the fact that the signal intensity depends on the position of Pol II on the gene, albeit this is done in different ways. In Xu et al., 2016 a master equation is written for the joint distribution of gene state and the number of nascent mRNA. In this case the number of nascent RNAs can have non-integer values since it represents the experimentally measured signal from the (incomplete) nascent RNA. Solution of this master equation proceeds by (a) a discretization of the continuous nascent mRNA signal into bins which are much smaller than one; (b) solution using finite state projection (FSP). This approach can lead to a large state space which incurs a large computational cost. In contrast, in our method, we use FSP to solve for the delay telegraph model, i.e. the distribution of the discrete number of bound Pol II from which we construct (using convolution) the approximate distribution of the continuous nascent mRNA signal by assuming the Pol II is uniformly distributed on the gene. Since the state space of bound Pol II is typically not large, our method will typically be more computationally efficient than the one described in Xu et al., 2016.
We generated synthetic smFISH signal data by using the SSA, modified to include delay to simulate the delay telegraph model (Fu et al., 2022). Specifically, we use Algorithm 2 described in Barrio et al., 2006. One run of the algorithm simulates the fluctuating number of bound Pol II molecules in a single cell. The total fluorescence intensity (mimicking smFISH) is obtained as follows. When a particular bound Pol II is produced by a firing of the transcription reaction , we record this production time; since the elongation rate is assumed to be constant, given the production time we can calculate the position of the Pol II molecule on the gene at any later time and hence using Figure 1b we can deduce the fluorescent signal due to this Pol II molecule. Specifically we normalize each transcribing Pol II’s position to and map the position to its normalized signal by where is the normalized position on the gene. Thus at a given time, the total fluorescent signal from the -th cell (the -th realization of the SSA) equals where is the number of bound Pol II molecules in the -th cell, and with is the vector of all Pol II positions on the gene. The total signal from each cell is a real number but it is discretized into an integer. The kinetic parameters are chosen from the same region of parameter space as in (2), on the same equidistant mesh grid and with the same constraint on the effective transcription rate. Unlike the mature mRNA case, here there is no degradation rate; instead we have the elongation time, which we fix to . Note that fixing this time is necessary since it is not possible to infer the three transcriptional parameters rates and the elongation time simultaneously because the steady-state solution of the delay telegraph model is a function of the non-dimensional parameter ratios and . Once a set of parameters is chosen, we use the modified SSA (as described above) to simulate the signal intensity in each of 104 cells.
The inference procedure is essentially the same as steps (i)-(v) described in mature mRNA inference except for the following points. In step (ii), the probability of observing a total signal of intensity from a single cell is obtained by integrating in Equation (8) on an interval for which, in our numerical scheme, means Note that the integration over the interval of length 1 is to match the discretization of the synthetic data and . Intuitively, one can always choose a positive integer such that for any . The computation of the solution of the delay telegraph model can be done either using the analytical solution (evaluated using high precision) or using the finite state projection algorithm (FSP) Munsky and Khammash, 2006. In Appendix 6—figure 1 and Appendix 6—table 1, we show that the two methods yield comparable accuracy and CPU time. For step (iii) we calculate the likelihood of observing the data given a chosen parameter set where qj is the discretized total signal intensity from cell and is the total number of cells. In the optimization, we aim to find The whole procedure (for both mature and nascent mRNA inference) is summarized by a flow-chart in Figure 1c.
A diploid yeast strain of BY4743 background with a single integration of 14xPP7 loops at the 5’UTR of GAL10 (strain YTL047 Donovan et al., 2019) was used in this study. Four replicate yeast cultures were grown in synthetic complete media with 2% galactose to early mid-log (OD 0.5), fixed with 5% paraformaldehyde (PFA) for 20 min, permeabilized with 300 units of lyticase and hybridized with 7.5 pmol each of four PP7 probes labeled with Cy3 (Integrated DNA Technologies) as described in Trcek et al., 2012 and Lenstra et al., 2015; Patel et al., 2021, resulting in four technical replicates. The PP7 probe sequences are: atatcgtctgctcctttcta, atatgctctgctggtttcta, gcaattaggtaccttaggat, aatgaacccgggaatactgc. Coverslips were mounted on microscope slides using mounting media with DAPI (ProLong Gold, Life Technologies). The coverslips were imaged on a Zeiss AxioObserver (Zeiss, USA) widefield microscope with a Plan-Apochromat 40x1.4 NA oil DIC UV objective and a 1.25 x optovar. For Cy3, a 562 nm longpass dichroic (Chroma T562lpxr), 595/50 nm emission filter (Chroma ET595/50 m) and 550/15 nm LED excitation at full power (Spectra X, Lumencor) were used. For DAPI, a 425 nm longpass dichroic (Chroma T425lpxr) and a 460/50 nm emission filter (Chroma ET460/50 m) and LED excitation at 395/25 nm at 25% power (Spectra X, Lumencor) were used. The signal was detected on a Hamamatsu ORCA-Flash4.0 V3 Digital CMOS camera (Hamamatsu Photonics, Japan). For each sample and each channel, we utilized the Micro-Manager software (UCSF) to acquire at least 20 fields-of-view based on the DAPI channel. Each field-of-view consisted of 13 z-stacks (with a z-step of 0.5 µm) at 25ms exposure for DAPI and 250ms exposure for Cy3. A custom python pipeline was used for analysis (https://github.com/Lenstralab/smFISH; Pomp, 2022). Maximum intensity projected images were used to segment the cell and nucleus using Otsu thresholding and watershedding (segmentation 1). In addition, we segmented cells using CellProfiler (segmentation 2). The diffraction-limited Cy3 spots were detected per z-slice using band-pass filtering and refined using iterative Gaussian mask localization procedure (Crocker and Grier, 1996; Thompson et al., 2002; Larson et al., 2005; Larson et al., 2011 and Coulon et al., 2014). Cells in which no spots were detected were excluded from further analysis since a visual inspection indicated that these cells were not properly segmented or were improperly permeabilized. Spots were classified as nuclear or cytoplasmic and the brightest nuclear spots were classified as transcription sites. The intensity of the brightest nuclear spot in a cell was normalized with the median fluorescence intensity of all the cytoplasmic spots in all cells. This is due to the fact that 90% of cytoplasmic mRNAs are isolated (Figure 4a), thus the median of the fluorescence signal of cytoplasmic mRNAs can be considered as the normalizing value. The distribution of the normalised intensity of the brightest nuclear spot, calculated over the cell population, is the experimental equivalent of the total fluorescent signal density function as given by the solution of the modified delay telegraph model, Equation (8). The number of mature mRNA in each cell is given by counting the number of spots in the entire cell, that is nuclear plus cytoplasmic. The transcription site is counted as 1 mRNA, regardless of its intensity. We show in Figure 3c that this has negligible influence on the estimated parameters since the mean number of mature mRNA is much greater than 1. The distribution of the number of spots is the experimental equivalent of the solution of the telegraph model, that is the marginal distribution of mature mRNA numbers in steady-state conditions. The integrated nuclear intensity of each cell was calculated by summing the DNA content intensity (DAPI) of all the pixels within the nucleus mask. The distribution of the intensities was fit with a bimodal Gaussian distribution. Those cells whose intensity was within a standard deviation of the mean of the first (second) Gaussian peak was classified as G1 (G2) (see Figure 3e left). This gave similar results to a different cell cycle classication method using the Fried/Baisch model (Johnston et al., 1978) which was recently employed in Skinner et al., 2016. See Appendix 7—figure 1 for a comparison of the two methods. We note that cells in late G2 may contain two separate transcription sites, one in the mother and one in the bud. When the nucleus moves into the bud, buds often contain less DNA than G1 cells, and mothers contain more DNA than G1 cells, both of which are excluded from the analysis. When the DNA content of the mother and daughter is similar, both mother and daughter are counted separately as G1 cells. We note that this late G2 subpopulation is very small. We did four independent experiments with a total number of cells equal to 2510, 6411, 4592, 3181, respectively. After classification, the numbers of G1 cells are 766, 2111, 1495, 904 and the number of G2 cells are 683, 1657, 1209, 1143, whereas the rest were classified as undetermined.
The four smFISH datasets are available from https://osf.io/d5nvj/. These datasets include the maximum intensity projected images, the spot localization results, the nuclear and cellular masks used for merged, G1 and G2 cells and the analyzed results of the mature and nascent data. The analysis code of the smFISH microscopy data is available at https://github.com/Lenstralab/smFISH; Pomp, 2022. The code for the the synthetic simulations and the parameter inference is available at https://github.com/palmtree2013/RNAInferenceTool.jl; Fu, 2022. |
PMC9648977 | The Lancet Infectious Diseases | Why hybrid immunity is so triggering | 10-11-2022 | Why hybrid immunity is so triggering
It is becoming clear that hybrid immunity, that is immunity provided by a combination of infection and vaccination, provides better protection against subsequent COVID-19 than either vaccination or infection alone – higher antibody levels, less frequent and less severe infection. However, the picture is complex due to a chequered pattern of immunity in the population. People differ not only in their history of infection timing and infecting variant, but also in the type of vaccine they received, how many doses and finally, how well their immune system responded. Immunologically, it makes sense that hybrid immunity provides better protection. Irrespective of whether an antigen is introduced as a vaccine or due to pathogen replication, repeated exposure stimulates B cell responses and antibody production. Most people with hybrid immunity will have encountered SARS-CoV-2 antigens more often than people who were only vaccinated or only infected. Additionally, the quality of the immune responses differs. Infection exposes the body to a whole range of antigens coming from different parts of the virus; mRNA and virus-vectored vaccines express only spike, which is the most important vaccine target on the virus surface and exposed to secreted antibodies. However, other antigens are also important for T cell responses. Furthermore, most vaccines given so far target the ancestral spike from SARS-CoV-2 circulating early during the pandemic. Currently circulating variants have accumulated spike changes that enable them to evade antibody recognition. Infection with one of these newer variants stimulates B cells with antigenically divergent spikes, broadening the immune response. The balance between recall of ‘ancestral’ immune responses and development of ‘novel’ responses is not entirely clear yet. There is some evidence of imprinting, that is, preferential recall of old responses; however, the superiority of hybrid immunity tempers concerns somewhat, as the strength of hybrid immunity tends to depend on how closely the first infecting variant matches the subsequent one (although results are complex to interpret due to waning of immune responses). Finally, injection of antigens will provoke a qualitatively different immune response than infection of respiratory epithelial cells. Innate immune responses and inflammatory stimuli ‘orchestrate’ the following adaptive immune response, although most viruses can dampen this response. The site of exposure also influences the quality of responses. While SARS-CoV-2 infection of the upper respiratory tract induces mucosal IgA, current COVID-19 vaccines induce systemic IgG. Systemic IgG is also produced after infection and it is effective at targeting virus in the lung, but generally much less so in the upper respiratory tract. Immunologically it makes sense to favour hybrid immunity, however, we would like to strongly caution against the conclusion that hybrid immunity should be a public health measure and people should not protect themselves from infection or even be encouraged to acquire infection to gain superior hybrid immunity. Infection comes with risks, both during the acute phase and long-term, such as an increased cardiovascular risk or Long Covid. Unfortunately, the concept of hybrid immunity has become highly polarised, with some groups using it to argue against non-pharmaceutical interventions, such as mask wearing or isolation during active COVID-19. Such conclusions are misleading and risky, in particular, for people at high risk due to age or co-morbidities. Importantly, it also alienates the large group of people in low- and middle-income countries who have no access to any vaccines yet. So, where do we go from here? Chequered immunity patterns in the population, waning of immune responses and the rise of immune-evasive variants such as BQ.1.1 or XBB, which might threaten protection afforded by hybrid immunity, require a multi-layered approached. First, we need an agile, scalable and fast infrastructure to develop and approve new vaccines that either target newly emerging variants, are pan-variant, and/or provide mucosal protection. However, we do not have the same will and funding as earlier during the pandemic for new vaccines, nor to track the variant landscape. While these are urgent needs, we should not forget non-pharmaceutical interventions, which can be adapted to the current local risk level. Although these interventions can have societal and economic consequences, a wildfire of COVID-19 will cost us too, causing disruption, disability, and death. If one must make any political arguments with hybrid immunity, it should be that people who had no access to vaccines yet must urgently get them. |
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PMC9648984 | Xiaocheng Shi,Wei Wei,Yichun Zou,Lixin Dong,Hengping Wu,Jiazhi Jiang,Xiang Li,Jincao Chen | LncRNA Taurine Up-Regulated 1 plays a proapoptotic role by regulating nuclear-cytoplasmic shuttle of HuR under the condition of neuronal ischemia | 27-10-2022 | apoptosis,inflammatory factor,ischemic stroke,RNA-binding protein,taurine upregulated gene 1 | The study aimed to identify TUG1 as an essential regulator of apoptosis in HT22 (mouse hippocampal neuronal cells) by direct interaction with the RNA-binding protein HuR. In order to study the role of TUG1 in the context of ischemia, we used mouse hippocampal neuronal cells treated with oxyglucose deprivation to establish an in-vitro ischemia model. A bioinformatic analysis and formaldehyde RNA immunoprecipitation (fRIP) were used to investigate the biological functions. A Western blot assay and reverse transcription polymerase chain reaction were used to explore the expression of the molecules involved. A cell proliferation and cytotoxicity assay was performed to detect neuronal apoptosis. TUG1 exhibits a localization-specific expression pattern in HT22 cells under OGD treatment. The bioinformatics analysis showed a strong correlation between the TUG1 and HuR as predicted, and this interaction was subsequently confirmed by fRIP-qPCR. We found that HuR was translocated from the nucleus to the cytoplasm after ischemia treatment and subsequently targeted and stabilized COX-2 mRNA, which led to elevated COX-2 mRNA levels and apoptosis of the HT22 cells. Furthermore, nuclear-specific disruption of TUG1 prevented the translocation of HuR to the cytoplasm and decreased COX-2 mRNA expression, resulting in increased cell viability and partially reversed apoptosis. In conclusion, it was demonstrated that TUG1 accelerates the process of apoptosis by promoting the transfer of HuR to the cytoplasm and stabilizing COX-2 mRNA. These results provide useful information concerning a therapeutic target for ischemic stroke. | LncRNA Taurine Up-Regulated 1 plays a proapoptotic role by regulating nuclear-cytoplasmic shuttle of HuR under the condition of neuronal ischemia
The study aimed to identify TUG1 as an essential regulator of apoptosis in HT22 (mouse hippocampal neuronal cells) by direct interaction with the RNA-binding protein HuR. In order to study the role of TUG1 in the context of ischemia, we used mouse hippocampal neuronal cells treated with oxyglucose deprivation to establish an in-vitro ischemia model. A bioinformatic analysis and formaldehyde RNA immunoprecipitation (fRIP) were used to investigate the biological functions. A Western blot assay and reverse transcription polymerase chain reaction were used to explore the expression of the molecules involved. A cell proliferation and cytotoxicity assay was performed to detect neuronal apoptosis. TUG1 exhibits a localization-specific expression pattern in HT22 cells under OGD treatment. The bioinformatics analysis showed a strong correlation between the TUG1 and HuR as predicted, and this interaction was subsequently confirmed by fRIP-qPCR. We found that HuR was translocated from the nucleus to the cytoplasm after ischemia treatment and subsequently targeted and stabilized COX-2 mRNA, which led to elevated COX-2 mRNA levels and apoptosis of the HT22 cells. Furthermore, nuclear-specific disruption of TUG1 prevented the translocation of HuR to the cytoplasm and decreased COX-2 mRNA expression, resulting in increased cell viability and partially reversed apoptosis. In conclusion, it was demonstrated that TUG1 accelerates the process of apoptosis by promoting the transfer of HuR to the cytoplasm and stabilizing COX-2 mRNA. These results provide useful information concerning a therapeutic target for ischemic stroke.
Ischemic stroke (IS) is an acute cerebrovascular disease caused by brain tissue damage resulting from the sudden rupture of blood vessels in the brain or blocked blood vessels flowing into the brain. Under the condition of ischemia and hypoxia, nerve cells produce a series of biochemical cascade reactions leading to irreversible brain damage. Stroke results in substantial social and economic burdens around the world. Globally, age-standardized stroke mortality decreased between 1990 and 2010, but age-standardized stroke incidence did not change significantly, and the absolute number of strokes, stroke survivors, and related deaths each year continued to increase [1]. However, as a result of the lack of understanding surrounding the mechanisms involved in ischemic brain injury, there is no specific clinical drug in use. A growing body of evidence suggests that genetic factors can be used to explore new drug targets and treatments for stroke, and even to improve stroke diagnosis and preidentification of those at risk [2]. Sequences longer than 200 nucleotides that do not encode proteins are called lncRNA and can function independently as transcripts [3]. Although lncRNA was initially regarded as simple transcription ‘noise’ by researchers, subsequent studies show that lncRNA participates in various pathophysiological situations, such as apoptosis, cycle progression, differentiation, and inflammation, by regulating the stability and nuclear retention of target genes [4]. LncRNAs normally interact with one or more RNA-binding proteins (RBPs) to perform a variety of cellular functions. The majority of these processes involve various information carriers other than mRNA to exert biological effects, for example, they may utilize splicing, polyadenylation, transport, stability, and translation [3]. Studies have shown that lncRNA whose expression level changes during cerebral IS can regulate gene expression at the transcriptional and posttranscriptional level; thus, it may have potential as a biomarker and therapeutic target [5]. Taurine upregulated gene 1 (TUG1) is observed when genomic screening for upregulated genes after taurine treatment and is highly expressed in the mammalian brain. TUG1 is a key signal molecule in the development of the retina, and its deletion seriously damages the formation of mouse retinae [6]. In clinical trials focusing on the correlation between TUG1 polymorphism and the risk of IS, a more effective overexpression promoter may be combined with globin transcription factor 1 (GATA-1) to increase the level of TUG1, demonstrating that TUG1 may represent an independent risk of IS [7]. At present, studies regarding the proapoptotic effect of TUG1 in ischemia–hypoxia experiments almost exclusively focus on the competing endogenous RNA mechanism of LncRNA. However, TUG1’s potential role and molecular mechanism in promoting neuronal apoptosis in cerebral ischemia need to be further explored. The RBP, human antigen R (HuR, also known as ELAV), regulates proliferation, senescence, differentiation, apoptosis, and stress and immune responses by controlling the splicing, localization, stability, and translation of intracellular transcripts, including coding and noncoding transcripts [8]. Among the different proteins that specifically bind to AU-rich elements (AREs), members of the ELAV protein family, especially the ELAV-like protein HuR (HuA), regulate the half-life and stability of target mRNAs by binding to U- or Au-rich regions [9]. As one of the most well-studied RBPs, HuR binds to the 3′-untranslated region (UTR) transcripts of mRNAs, including p53, p27, Caspase-9, and BCL2 [10]. The HuR protein is mainly located in the nucleus, and it performs its mRNA stabilizing function by shuttling between the nucleus and the cytoplasm [9]. Therefore, the cytoplasmic role of HuR in neuronal cells under ischemia may be vital in terms of deciphering the mechanism of apoptosis in diseased cells. Moreover, the effect of specific lncRNAs on nucleocytoplasmic HuR shuttling in this state has not been described. In this study, we hypothesized that lncRNA and RBP directly bind to each other in the nucleus and regulate the subcellular spatial positioning of RBP, enabling it to play its role as a key mediator of apoptosis. In order to verify this hypothesis, we knocked down TUG1 in the nucleus and cytoplasm separately to observe the changes in the subcellular localization of RNA-binding proteins. These findings may provide insight into lncRNA TUG1 as an important regulator of the pathophysiology of mouse hippocampal neuronal cell line (HT22) cell necrosis through direct interaction with the RNA-binding protein HuR (a key mediator of apoptosis).
HT22 was purchased from Procell, Wuhan, China. The company recommended the ‘HT22 cell special medium’ (Procell, Wuhan, China, CM-0697), which contains DMEM (Procell, Wuhan, China, PM150210), 10% fetal bovine serum (FBS) (Procell, Wuhan, China, 164210-500), and 1% P/S (Procell, Wuhan, China, PB180120). The cell gas phase culture conditions were as follows: air: 95%; CO2: 5%; temperature: in an incubator at 37 °C. When the cell confluence reached 80%, the cells were passaged at a ratio of 1:6. Generally, the medium was changed, or the cells were passaged after about 2 days. According to the instructions of the Lipofectamine 2000 Reagent transfection kit (Invitrogen, California, USA 11668-027), the product was mixed with siRNA-TUG1 (Sangon, Shanghai, China) or antisense oligonucleotides (ASO)-TUG1 (Sangon, Shanghai, China) in order to transfect HT22, and the transfection efficiency was assessed using qPCR 24 h after transfection.
The medium was aspirated in the HT22 cell culture dish and washed with PBS several times. Then, an equal volume of glucose-free DMEM (Procell, PM150270) was added, and it was placed in a tri-gas incubator (Heraeus, Hanau, Germany) at 37 °C, 0.5% O2, 94.5% N2, and 5% CO2 with oxygen-glucose deprivation (OGD) culture. Thereafter, the experimental group removed the sugar-free DMEM and washed it with PBS before proceeding to the next step of the experiment. The same conditions were used for the control group, except that it was not exposed to OGD and the sugar-free DMEM was replaced with complete medium.
In order to measure the relative content of HuR protein in different samples, we strictly followed the manufacturer’s instructions using Western and IP cell lysis buffer (Beyotime, Nanjing, China, P0013) to extract total protein from cell samples, and a Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, Nanjing, China, P0027) to extract and separate proteins from the cytoplasm and nucleus. Herein, we used a BCA Protein Assay Kit (Beyotime, Nanjing, China, P0012) for protein quantitative determination according to the instructions. The same amount of protein sample was electrophoresed in 12% SDS-PAGE, and then transferred to a PVDF membrane. After blocking with a TBS-T (NCM Biotech, Nanjing, China, WB21000) solution containing 5% skimmed milk powder (Servicebio, Wuhan, China, G5002) at room temperature for 1 h, the membrane was completely immersed in the primary antibody, diluted at 1:1000 in the primary antibody diluent, and incubated overnight at 4 °C. Anti-β-actin (ABclonal, AC026) and HuR (ABclonal, A19622) are both Rabbit mAbs. The membranes were washed with TBS-T three times for 10 min each, and then the membranes were incubated with the secondary antibody for 2 h at room temperature. Subsequently, the excess secondary antibody was removed by TBS-T washing three times for 10 min, and then the enhanced chemiluminescence detection kit (catalog number 32209; Thermo Fisher Scientific, Inc.) was used with a chemiluminescence system [Tanon Chemidoc Apparatus (Tanon-Bio, Shanghai, China 2500)] to observe the blot.
In order to determine cell viability, 8000 cells per mixed with 100 ul of complete medium and seeded in 96-well plates. Before measuring the absorbance of cells at 450 nm using a microplate reader, the original medium was removed and the cells in each well were washed with PBS. Then, the cell HT22 cell special medium (Procell, CM-0697) containing 10% Cell Counting Kit-8 (Vazymy, Nanjing, China, A311-01) was mixed in a 15-ml tube and added to a 96-well plate at 100 ul per well. The product was then placed in a 37 °C cell incubator for 1 h, according to the manufacturer’s instructions. Each treatment was performed in at least six replicate wells.
The speed was set to 1000 rpm and the mixture was spun gently for 4 min to collect the cells. Then, the cells were resuspended in room-temperature phosphate-buffered saline (PBS, Servicebio, G4202), and the cells were washed to 5 mol/l cells/ml, before being centrifuged at 2000 rpm for 2 min. The cells were immediately resuspended in DMEM (Servicebio, G4510) medium without fetal bovine serum (FBS) or P/S to 5 mol/l cells/ml at room temperature, formaldehyde was added without methanol to a final concentration of 0.1%, and the mixture was spun slowly at room temperature for 5 min. Then, we added filtered glycine to a final concentration of 125 mmol/l, centrifuged it at 2000 rpm for 2 min, and then washed it twice with PBS and PIC at a concentration of 10 mol/l cells/ml at 4 °C. Finally, the mixture was rotated at 700 rpm for 8 min at 4 °C before being rotated at 2000 rpm for 2 min. Then, the supernatant was removed and the cell pellet was quick-frozen in liquid nitrogen and stored at −80 °C.
In order to determine the interaction between TUG1 and HuR protein, we used HT22 cells to perform the formaldehyde RNA immunoprecipitation (fRIP) experiment for RNA-binding protein HuR. We modified the existing RNA IP (RIP) and chromatin immunoprecipitation (ChIP) protocols to optimize RNA and protein recovery. We combined 0.5 ul/ml 1 mol/l dithiothreitol (DTT), 10 ul/ml Protease Inhibitor Mix (Thermo Scientific, Massachusetts, USA, PI-87785), and 2.5 ul/ml RNaseOUT (Thermo Scientific, Massachusetts, USA) with 1 ml RIPA lysis buffer (Servicebio, G2002) per 10 mol/l cells, resuspended the crosslinked cryoprecipitate and incubated the product at 4 °C for 10 min with rotation. The samples were sheared using the Covaris protocol (Peak power-75, Duty Factor-10, Cycles/Burst-100) for 10 min, and the lysate was immediately spun at a maximum speed at 4 °C for 10 min to collect the supernatant. We added an equal volume of NP-40 buffer to the supernatant, which contained 0.5 mmol/l DTT, 1 × PIC (Roche, 4693132001), and 100 U/ml RNaseOUT (Thermo Scientific, Massachusetts, USA 10777019). A 0.45 μmol/l syringe filter was used for the mixed solution and 50 μl of the lysate was removed as an input sample. Then, 25 μl of transfer protein G beads (MCE, HY-K0202) was added per 5 mol/l cells to each 1.5-ml tube, and they were placed on a magnet to remove the bead buffer. A total of 1 ml of the newly prepared NP-40 mixing buffer was added to wash the surface of the magnetic beads twice, then the sample was resuspended to 25 ul. Thereafter, we ‘precleared’ the filtered lysate by incubating with the above-mentioned protein G beads and rotating for 30 min at 4 °C. The sample was placed on the magnet, and the cleared lysate was transferred to a new 1.5-ml tube for each IP condition. After the lysate was thawed on ice and 5 μg of HuR antibody (HuR/ELAVL1 Rabbit mAb, A19622) was added to each sample, the lysate was rotated at 4 °C for 2 h. A total of 50 μl of protein G beads was processed according to the previous steps into the lysate and the beads were rotated at 4 °C for 1 h, before being placed on the magnet. The supernatant was removed, and 1 ml of the previously prepared NP-40 mixed solution buffer was added to wash the magnetic beads twice, rotating at 4 °C for 10 min each time. After the last wash, we removed the supernatant and froze the beads at −20 °C.
We added 56 μl of RNase-free water and 33 μl of freshly prepared 3× reverse-crosslinking buffer, which consisted of 3× PBS (without Mg or Ca), 6% N-lauroyl sarcosine, 30-mmol/l EDTA, 15-mmol/l DTT, 10-μl proteinase K, and 1-μl RNaseOUT, in order to resuspend the beads, and reverse crosslinking was performed at 42 °C for 1 h, and then at 55 °C for 1 h. We followed the instructions of RNA Clean & Concentrator TM5 (cat. No: R1014) to clean 100 ul of RNA solution, and we finally added 15 μl of enzyme-free water directly to the column matrix before centrifugation.
The sample could be completely lysed by Trizol reagent (Takara, Tokyo, Japan, T9108). Following the instructions, chloroform was added to the lysate, and then mixed and centrifuged to form a supernatant layer, an intermediate layer, and an organic layer. The supernatant layer was collected with the RNA distribution and precipitated with isopropanol to recover the total RNA extracted into the cells. A cytoplasmic and nuclear RNA purification kit (Norgen, Ontario, Canada, 21 000) was used to isolate and purify the cytoplasmic and nuclear RNA. According to the HiScript Q RT SuperMix for qPCR (Vazymy, Nanjing, China, R123) kit’s instructions, RNA (1 μg each) was used to remove genomic DNA and reverse transcribed into cDNA. In order to use cDNA for quantitative PCR, following the instructions, we used a ChamQ Universal SYBR qPCR Master Mix (Vazymy, Nanjing, China, Q711) to remove cDNA (1 μl) and diluted it 20 times with dd-H2O for PCR amplification. The reaction conditions were predenaturation at 95 °C for 30 s, and then cycling (denaturation at 94 °C for 10 s, annealing and extension at 60 °C for 30 s) 40 times. The experiment was repeated three times and each sample provided two replicate wells. The internal reference for COX-2mRNA and TUG1 was phosphoglycerate kinase (PGK). Moreover, 2−ΔΔct was used to analyze the data. Primer sequence TUG1 F: GAGACACGACTCACCAAGCA R: GAAGGTCATTGGCAGGTCCA PGK F: TGCACGCTTCAAAAGCGCACG R: AAGTCCACCCTCATCACGACCC COX-2 F: TTCAACACACTCTATCACTGGC R: AGAAGCGTTTGCGGTACTCAT
A bioinformatics analysis was performed to investigate potential HuR-binding sites in the lncRNAs sequences and three different in-silico approaches were used (Table 1). First, the RNA-protein interaction prediction (RPISeq, http://pridb.gdcb.iastate.edu/RPISeq/) was used to test the possible interaction between the TUG1 and HuR, with random forest (RF) values and support vector machine (SVM) values as the results for the likelihood of mutual binding. A score of more than 0.5 was considered ‘positive’ (following the guidelines provided by the website) for possible interaction. Second, the RBPmap database (http://rbpmap.technion.ac.il) was used to identify the number of potential interaction sites obtained with a ‘high stringency’ filter. The RBPmap database (only compared to human proteins, but protein sequences were conserved across species) was used to identify and score the obtained potential interaction sites. From this, we derived all the highest-scoring potential protein binding sites. Finally, the lncRNAs sequences were aligned to the RNAbp database (RBPDB, http://rbpdb.ccbr.utoronto.ca/index.php). This database allows for the identification of all potential RNA-binding sites, using a default threshold score of 0.8 (indicated by the website as the optimal cutoff in order to have a ‘confident’ score of the lncRNAs-protein interaction). All the RNAs analyzed showed confident HuR-binding sites, which were analogous to the U-rich sequences bound by HuR. Together, the three approaches demonstrated a real potential interaction between the TUG1 and HuR. The CatRAPID fragments module is introduced to identify TUG1 regions involved in protein binding. The RNALFold algorithm from the Vienna package is employed to select RNA fragments in the range 100–200 nt with predicted stable secondary structure. Secondary structure stabilities are segments that have lower free energy for the higher number of bases that can be paired, the choice of segments in the range of 100–200 nt is optimal because it allows simultaneously: (a) selection of secondary structures with comparable free energy and (b) high sequence coverage (>90%) for long transcripts such as TUG1. The interaction fragments algorithm is a variant of the RNA interaction strength algorithm that allows the identification of putative binding areas in long sequences. CatRAPID graphic module enables a quick assessment of the interaction propensity of a protein-RNA pair. The interaction propensity measured the interaction probability between 1 protein (or region) and 1 RNA (or region). This measure is based on the observed tendency of the components of ribonucleoprotein complexes to exhibit specific properties of their physicochemical profiles that can be used to make a prediction. The concept of interaction strength is introduced to compare the interaction propensity of a protein-RNA pair with a reference set that has little propensity to bind (random associations between polypeptide and nucleotide sequences). Reference sequences have the same lengths as the pair of interest to guarantee that the interaction strength does not depend on protein and RNA lengths. For each protein-RNA pair under investigation, we use a ‘reference set’ of 102 protein and 102 RNA molecules (a total of 104 nonredundant protein-RNA pairs), randomly associated between peptide and nucleotide sequences, to determine potential interaction strengths. CatRAPID strength module allows for evaluating the significance of the interaction of a protein-RNA pair by comparing the result with a reference set of 104 interactions. The graphical representation of the CDF distribution value indicates the significance of the interaction propensity.
All experiments were independently repeated at least three times and analyzed using the GraphPad Prism software, Santiago, USA. Statistical differences were calculated using a two-tailed Student’s t-test, and all values are expressed as the mean ± SD. When P < 0.05, we consider the difference to be statistically significant. Ns: not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
The expression of TUG1 is highly induced in the nucleus by OGD but decreases in the cytoplasm. In order to detect the TUG1 expression pattern during OGD, we isolated the total RNA of mouse HT22 cell line treated by OGD and then identified the expression level of TUG1 using real-time quantitative polymerase chain reaction (qRT-PCR). We found that the expression of TUG1 in the HT22 cell line was significantly decreased in vitro after 4, 6, and 12 h of OGD treatment as compared with the control group (Fig. 1a). The cell viability was also assessed using a cholecystokinin-octopeptide-8 (CCK-8) assay, and, similar to previous findings, it was observed that OGD treatment significantly inhibited cell growth at 12 h as compared with the control group, which indicates that TUG1 expression may be associated with cell viability during OGD (Fig. 1b). In order to analyze the changes in TUG1 in different subcellular localizations in OGD, we divided the HT22 cell line into nuclear and cytoplasmic fractions and examined the changes in the subcellular localization of TUG1 during OGD. The results showed that TUG1 expression is highly induced in the nucleus (Fig. 1c), but significantly decreased in the cytoplasm after 6 h of OGD (Fig. 1d), indicating that TUG1 may have different functions in the nucleus and cytoplasm, or TUG1 that may shuttle between the nucleus and cytoplasm, leading to the different TUG1 distribution.
Protein-RNA interactions are ubiquitous and are critical aspects of many cellular processes, such as splicing, polyadenylation, trafficking, stability, and translation. Moreover, they are implicated in pathologies including autoimmune, metabolic, neurological, and muscular diseases [11]. Long noncoding RNAs typically exert their functions by binding to one or more proteins, are key to many cellular processes, and their dysregulation has been implicated in various pathologies. Studies show that TUG1 interacts with TRAF5 in rat diabetic nephropathy, and TUG1 overexpression promotes the degradation of TRAF5 protein and affects podocyte apoptosis [12]. To determine which protein interacts with TUG1 during OGD, we first searched online to predict the RBP-RNA interactions using the Tartaglia lab tool. The top 10 targets that potentially interact with TUG1 are shown in Table 1, with HuR returning the highest score. First, the TUG1 sequences were aligned to the RBPDB. This database allows for the identification of all potential RNAbp-binding sites, using a default threshold score of 0.85 (indicated by the website as the optimal cutoff in order to have a ‘confident’ score of the lncRNAs-protein interaction). Second, TUG1-protein interaction prediction (RPISeq was used to test the possible interaction between TUG1 and the protein. The RF and SVM values were obtained by analyzing the TUG1 sequence and the possible protein sequence, which could be used as to predict the possibility of mutual binding. A score of more than 0.5 was considered ‘positive’ (following the guidelines provided by the website) for a possible interaction, with all proteins indicating a possible interaction with TUG1. Finally, the RBPmap database was used to identify and score potential interacting sites obtained through the ‘high stringency’ filter. All potential protein binding sites with high scores were analyzed, and the scores were calculated according to the reliability. The aforementioned database only allows for comparisons with human proteins, but this is conserved across species. The interaction fragments algorithm is a variant of the RNA interaction strength algorithm that allows identification of putative binding areas in long sequences. The interaction profile represents the interaction score (y-axis) of the protein along the TUG1 sequence (x-axis), giving information about the transcript regions that are most likely to be bound by the protein. (Fig. 2a). To identify potential interactions between TUG1 and the HuR receptor, we used the CatRAPID online algorithm, which can rapidly predict RNA-protein interactions and domains to evaluate the interaction tendency of TUG1 and the HuR receptor based on the secondary structure, hydrogen bonding, and molecular interatomic forces. The DP values above 50% indicate that the interaction is likely to take place. Interestingly, the CatRAPID revealed that there existed an interaction between TUG1 and the HuR receptor with a DP value of 100% (Fig. 2b). We eliminated the length dependence by introducing a ‘reference set’ composed of protein and RNA sequences that have exactly the same lengths under investigation. The interaction fragments algorithm was used to predict TUG1’s ability to interact with HuR. In our calculations, we used a reference set of 102 protein and 102 RNA molecules, randomly associated between polypeptide and nucleotide sequences, to identify potential interaction strength. In particular, we predicted HuR to have a strong propensity to bind to TUG1 (protein interaction strength = 96%) (Fig. 2c). After determining that HuR has the highest possibility of interacting with TUG1, we further verified this using an fRIP assay, followed by qPCR with the primers targeting TUG1. There were significantly more TUG1 bound to the HuR protein during OGD treatment, compared with the controls (Fig. 2d). These data suggest that the HuR protein can directly bind to TUG1 and TUG1 exerts its function during OGD through interacting with HuR.
As the functional activity of HuR is regulated by dynamic subcellular localization, this underlies the contribution of HuR to many disease states. For example, under normal cellular physiology, HuR is primarily located in the nucleus, and when exposed to intrinsic and extrinsic stress, HuR can translocate from the nucleus to the cytoplasm, where it stabilizes and increases translation of target mRNAs [8]. Therefore, we examined the expression and subcellular localization of HuR after OGD in the mouse HT22 cell line in WB experiments. The results demonstrate that the expression of total HuR protein in HT22 cells treated with OGD for 6 h was not significantly different from that of the control group (Fig. 3a and b). When the nuclear and cytoplasmic portions of HT22 cells were separated after OGD treatment for 6 h, we observed that the concentration of HuR protein in the nucleus was downregulated to 62% of that observed in the control group (Fig. 3c and d), whereas the concentration of HuR protein in the cytoplasm was significantly elevated (Fig. 3e and f). These results indicate that translocation of HuR protein from the nucleus to the cytoplasm may occur in HT22 cells under the stimulation of ischemia and hypoxia.
In order to verify if TUG1 can regulate HuR protein translocation during OGD, we utilized two different methods to knockdown TUG1, that is ASO, which more effectively target nuclear TUG1 and downregulate expression levels in HT22 (Fig. 4a); and small interfering RNA (siRNA), which target TUG1 within the cytoplasm and silence TUG1 (Fig. 4b). Thus, the HT22 cells were divided into four groups for specific nuclear knockdown of TUG1: NC+con; NC+OGD; ASO-TUG1+OGD; and ASO-TUG1+con; and four groups for knockdown in the cytoplasm: NC+con; NC+OGD; si-TUG1+OGD; and si-TUG1+con. OGD can significantly reduce the accumulation of HuR in the nucleus; however, TUG1 ASO can reverse this effect and prevent the decrease in HuR protein levels in OGD cells (Fig. 4c and d). Correspondingly, OGD can significantly elevate HuR protein levels in the cytoplasm, and knockdown TUG1 in the nucleus can reduce the accumulation of HuR in the cytoplasm during OGD (Fig. 4e and f). Moreover, decreasing TUG1 in the cytoplasm during OGD using siRNA was not able to reverse the reduction in HuR in the nucleus (Fig. 4g and h) or the increase in HuR in the cytoplasm (Fig. 4i and j). All these results shed light on the mechanisms through, which nuclear TUG1 facilitates the shuttle of HuR protein from the nucleus to the cytoplasm under OGD experimental conditions.
In order to explore the effects of TUG1 knockdown in the nucleus and cytoplasm on cell survival and apoptosis, a CCK-8 assay was used for HT22 cells under OGD conditions with TUG1 ASO or siRNA, separately. We observed that ASO-TUG1 was able to significantly reduce the proportion of apoptotic cells after 6 h-OGD, whereas si-TUG1 had no significant effect on the proportion of apoptotic cells under the same conditions (Fig. 5a and b). HuR shuttles to the cytoplasm and stabilizes its targets in response to various stimuli. COX-2 is a proapoptosis gene and potential HuR target gene, which possesses a long 3′-UTR containing multiple AREs that can be stabilized by HuR in various cell types [8]. In this study, fRIP-qPCR was used to further confirm that HuR can directly interact with COX-2 mRNA. The qRT-PCR results indicated a significant increase in COX-2 mRNA bound to HuR protein under OGD treatment, compared with the control group (Fig. 5c). The expression of COX-2 mRNA in the HT22 cell line was significantly increased in vitro after 4, 6, and 12 h of OGD treatment (Fig. 5d), which is associated with the decreased cell viability that we see in Fig. 1b. Since the HuR protein, shuttled by TUG1 from the nucleus to the cytoplasm, is responsible for the increase in COX-2 mRNA, the knockdown TUG1 from the ASO method should, therefore, also inhibit the increased expression of COX-2 in the cytoplasm. Indeed, we found that TUG1 ASO was able to partly reverse the increase in the COX-2 expression level (Fig. 5e). However, knockdown of cytoplasmic TUG1 with siTUG1 exhibited no significant change (Fig. 5f). These data suggest that LncRNA TUG1 may play a role in promoting neuronal apoptosis by facilitating the shift of the HuR protein from the nucleus to the cytoplasm.
Previous data showed that in the thromboembolic stroke model, infarctions most frequently take place in the cerebral cortex, hippocampus, and thalamostriate. People surviving episodes of cerebral ischemia often show a persistent memory deficit and cognitive decline. Therefore, investigating the pattern of ischemic injury in the hippocampus may provide insights into pathogenetic mechanisms and may help develop new therapeutic strategies. It is well known that the hippocampus is one of the brain regions most sensitive to ischemic damage and plays important roles in learning, memory, and epilepsy and is known to have a high susceptibility to ischemic damage compared with other brain structures in animals and humans. However, because hippocampal are most vulnerable to ischemia, cell death has been thought to represent a sensitivity of the neurons to injuries. To understand the role of hippocampal neurons in ischemia, we adopted a widely used cell model derived from immortalized parental HT4 cells to simulate the construction of a stroke model. Many existing studies show that lncRNA can regulate cell processes and gene expression in cerebral IS injury. Thus, it could potentially play an important role in new treatment methods [5]. New technologies, such as RNA-seq, deep sequencing, and microarray analysis, have been used to screen out a large number of abnormally expressed lncRNAs in IS patients or animals with ischemic injury. These play an important role by regulating cell survival, inflammatory processes, and angiogenesis [13]. TUG1 polymorphism studies show that specific promoters can bind to the transcription factor globin transcription factor 1 (GATA-1) to increase TUG1 expression at the transcriptional level, and high TUG1 expression has been shown to indicate a similar risk in clinical studies to other known IS factors, including total cholesterol, triglycerides, HDL-cholesterol, and LDL-cholesterol [7]. TUG1 was reported to be upregulated in the caudate nucleus in Huntington’s disease and has recently been identified as a P53 target gene [14]. In the mouse hippocampal cell line OGD, wherein knocking down TUG1 increased the level of brain-derived neurotrophic factor (BDNF) and reduced apoptosis. Contrarily, TUG1 overexpression reversed the therapeutic effect associated with aerobic exercise [15]. In addition, in the HuR transgenic astrocyte mouse stroke model, the significant enhancement of angiogenic brain edema of astrocytes around the lesion represents a worse short-term functional node [16]. Therefore, the expression and activity of RBP may affect the treatment of IS, but whether it is regulated by lncRNA and participates in the pathophysiological mechanism of IS is unclear. Studies have shown that, in a mouse model of ulcerative colitis, TUG1 positively regulates the upregulation of c-myc by interacting with HuR to regulate cell apoptosis [17]. In this study, we propose that TUG1 may affect the inflammatory response in OGD through its interaction with HuR. Furthermore, we used the experimental method of fRIP to verify this hypothesis, showing that more TUG1 was bound to the HuR protein during the OGD process. As compared with mRNA, lncRNA has a wide range of variation and a shorter half-life; in particular, nuclear-localized lncRNA is more unstable than other subcellular localized lncRNA [18], suggesting that lncRNA has a complex metabolism and diverse functions. Interestingly, we found a decrease in TUG1 mainly occurs in the cytoplasm of HT22 cells under ODG conditions. We then further tested the relative distribution level of HuR in the cytoplasm/nucleus after ischemia and hypoxia treatment. Our data show that both TUG1 and HuR distributions change significantly with OGD damage. Notably, under OGD conditions, TUG1 forms a complex with HuR and translocates from the nucleus to the cytoplasm. In addition, we confirmed that TUG1 knockdown in the nucleus with ASO decreased the apoptosis rate under hypoxic ischemia conditions. When TUG1 is inhibited in the nucleus, the transfer of HuR from the nucleus to the cytoplasm is significantly weakened, but there is no such phenomenon when TUG1 is inhibited in the cytoplasm. In addition, we confirmed that TUG1 is able to interact with HuR and carries it to the cytoplasm under OGD conditions, and then, HuR further increases the expression of COX-2 by binding to the UTR region of COX-2 mRNA. HuR is a well-studied RNA-binding protein, which functions in the cytoplasm to promote the stability of ARE-mRNA. A number of studies have demonstrated that stroke is a comprehensive, multifactorial disease with multiple biological pathways. RNA-binding proteins are able to directly or indirectly affect cell apoptosis induced by ischemia and hypoxia. The RNA-binding protein QKI can inhibit cell apoptosis under the induction of myocardial ischemia by binding to the proapoptotic transcription factor FoxO1 and negatively regulating its downstream target genes [19]. In hypoxia-cultured cortical neurons and astrocytes, the expression of the TIAR protein (T-cell restricted intracellular antigen-related) was increased and colocalized with DNA damage in neuronal cells, suggesting that TIAR may be associated with brain ischemia [20]. MAARS has a preference for binding to HuR and assists in nucleocytoplasmic shuttling and regulates targeted apoptotic proteins, such as p53, p27, Caspase-9, and BCL2 in the cytoplasm, which is of great significance for atherosclerosis and a wide range of vascular disease states [10]. In our study, we found that COX-2mRNA increased significantly when HuR was enriched in the cytoplasm. However, after preventing HuR accumulation in the cytoplasm, COX-2mRNA expression declined significantly. In summary, our data indicate that knocking down TUG1 in the nucleus can inhibit HuR in the nucleus, weakening the stability of COX-2mRNA, which may further regulate AMPA glutamate receptors to inhibit inflammation. An increase in COX activity can lead to the increased release of prostaglandins and to ischemic neuron damage by free radicals. COX inhibitors, which are effective anti-inflammatory drugs, have been used after transient global cerebral ischemia in rodents to improve the delayed death of hippocampal CA1 neurons [21]. In addition, COX-2 gene expression-deficient mice exhibited a protective effect in terms of reducing brain damage in a middle cerebral artery occlusion experiment, indicating that inhibiting COX-2 may be an important factor in the reduction of glutamate neurotoxicity [22]. Indeed, previous studies reported COX-2 to be the upstream factor of glutamate excitotoxicity, affecting cell fate by regulating AMPA glutamate receptors [23]. We observed that COX-2–mediated apoptosis was not completely reversed when TUG1 in HT22 nucleus was knocked down under OGD conditions. On the one hand, many factors lead to apoptosis after ischemia and a cascade reaction occurs. Various complex factors contribute to apoptosis after IS and further aggravate brain injury, including oxidative stress, the toxicity of excitatory amino acids, excess calcium ions, and inflammation. Thus, the apoptosis observed in our research may not be solely mediated by COX-2. On the other hand, the transcription and translation of COX-2 could be regulated by factors other than HuR. Transactivators, including CREB, ATF, C/EBP, C-Jun, C-Fos, and USF are demonstrated to bind to the promoter region of COX-2 and regulate its expression in human fibroblasts and endothelial cells. p300 is essential for COX-2 transcriptional activation by proinflammatory mediators in fibroblast. Though the TUG1 ASO almost completely blocked the translocation of HuR from the nucleus to the cytoplasm and showed a significant effect on regulation of COX-2 expression level, there could be other factors or pathways involved in COX-2 regulation in cytoplasm. In BV-2 microglia and SH-SY5Y human neuroblastoma cells, TUG1 expression was shown to be elevated after OGD in vitro [24], which is different to the results from our study. Moreover, TUG1 increased in the nucleus only and reduced in the total cell fraction. These differences may be due to the cell type, that is both BV-2 and SH-SY5Y are tumor cells, and their response to external stimuli differs from neuronal cell HT22. HT22 cells have been used by many laboratories for neuronal cell injury models, including an OGD model, a high glucose injury model, and an L-glutamate injury model, which leads us to believe that HT22 is a superior cell model for the study of brain ischemia. The conservation level of its nucleotide sequence in humans and mice reaches 77%, ranking second out of all lncRNAs [25]. This makes TUG1 a promising therapeutic target or new epigenetic intervention for the treatment of IS. Previous studies have demonstrated that antisense oligonucleotides (ASO) targeting TUG1 are widely and effectively used to inhibit TUG1 expression. Furthermore, intravenous treatment with ASO-targeting TUG1 coupled with a potent drug delivery system could efficiently repress glioma stem cell growth in vivo. This drug delivery system used cyclic Arg–Gly–Asp (cRGD) peptide-conjugated polymeric micelle and was first designed to targeted delivery drugs for brain tumors. A similar method was applied to treat pancreatic ductal adenocarcinoma and could enhance the effects of chemotherapy in pancreatic cancer. Moreover, successes on the development of ASO therapeutics for spinal muscular atrophy and Duchenne muscular dystrophy predict a robust future for ASOs in medicine. Indeed, existing pipelines for the development of ASO therapies for spinocerebellar ataxias, Huntington's disease, Alzheimer's disease, amyotrophic lateral sclerosis, Parkinson's disease, and others strengthen the outlook for using ASOs on human diseases. Therefore, using ASO we designed in this manuscript targeting TUG1, together with the drug delivery system mentioned above is a feasible way for the treatment of IS. In summary, we first demonstrated that HuR is directly regulated by TUG1 and confirmed the functional interaction between TUG1 and HuR. As an RNA-binding protein, HuR can directly promote OGD/R-induced inflammatory damage by regulating the stability of COX-2 mRNA. We found that, under the condition of cell ischemia and hypoxia, TUG1 enhances the expression of the inflammatory gene COX-2 by binding and transporting the HuR protein, leading to the promotion of cell apoptosis. Therefore, TUG1 and HuR demonstrated proapoptotic effects in neuronal ischemia and hypoxia, and the antiapoptotic effect can be achieved by inhibiting their expression or preventing their subcellular translocation. As regards the limitations of this study, these conclusions are all drawn from an in-vitro HT22 cell culture system, which may not perfectly simulate the process in other nerve cells and neurons in an in-vivo condition. In the future, more in-vivo experiments and clinical trials are necessary to explore the biological mechanism of the TUG1/HuR axis in ischemic injury.
LncRNA TUG1 exhibits subcellular distribution change after cerebral ischemic injury, which facilitates the HuR protein’s transfer from the nucleus to the cytoplasm. HuR protein in the cytoplasm was able to further stabilize the expression of COX-2 and exacerbate cell apoptosis. Knockdown of lncRNA TUG1 in the nucleus specifically was shown to significantly reduce the apoptosis of HT22 cells under OGD conditions.
The authors would like to thank all participants in the study. The authors disclosed receipt of the following financial support for the research, authorship, and publication of this article: this work was supported by the National Natural Science Foundation of China (grant number 81771280). Data availability: the data used to support the findings of this study are available from the corresponding author upon request.
There are no conflicts of interest. |
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PMC9648992 | Prajakta Kalkar,Gal Cohen,Tal Tamari,Sagie Schif-Zuck,Hadar Zigdon-Giladi,Amiram Ariel | IFN-β mediates the anti-osteoclastic effect of bisphosphonates and dexamethasone 10.3389/fphar.2022.1002550 | 14-10-2022 | IFN-β,resolution of inflammation,macrophages,osteoclast differentiation,dexamethasone,zoledronic acid,multiple myeloma | Zoledronic acid (Zol) is a potent bisphosphonate that inhibits the differentiation of monocytes into osteoclasts. It is often used in combination with dexamethasone (Dex), a glucocorticoid that promotes the resolution of inflammation, to treat malignant diseases, such as multiple myeloma. This treatment can result in bone pathologies, namely medication related osteonecrosis of the jaw, with a poor understanding of the molecular mechanism on monocyte differentiation. IFN-β is a pro-resolving cytokine well-known as an osteoclast differentiation inhibitor. Here, we explored whether Zol and/or Dex regulate macrophage osteoclastic differentiation via IFN-β. RAW 264.7 and peritoneal macrophages were treated with Zol and/or Dex for 4–24 h, and IFN-β secretion was examined by ELISA, while the IFN stimulated gene (ISG) 15 expression was evaluated by Western blotting. RANKL-induced osteoclastogenesis of RAW 264.7 cells was determined by TRAP staining following treatment with Zol+Dex or IFN-β and anti-IFN-β antibodies. We found only the combination of Zol and Dex increased IFN-β secretion by RAW 264.7 macrophages at 4 h and, correspondingly, ISG15 expression in these cells at 24 h. Moreover, Zol+Dex blocked osteoclast differentiation to a similar extent as recombinant IFN-β. Neutralizing anti-IFN-β antibodies reversed the effect of Zol+Dex on ISG15 expression and partially recovered osteoclastic differentiation induced by each drug alone or in combination. Finally, we found Zol+Dex also induced IFN-β expression in peritoneal resolution phase macrophages, suggesting these drugs might be used to enhance the resolution of acute inflammation. Altogether, our findings suggest Zol+Dex block the differentiation of osteoclasts through the expression of IFN-β. Revealing the molecular pathway behind this regulation may lead to the development of IFN-β-based therapy to inhibit osteoclastogenesis in multiple myeloma patients. | IFN-β mediates the anti-osteoclastic effect of bisphosphonates and dexamethasone 10.3389/fphar.2022.1002550
Zoledronic acid (Zol) is a potent bisphosphonate that inhibits the differentiation of monocytes into osteoclasts. It is often used in combination with dexamethasone (Dex), a glucocorticoid that promotes the resolution of inflammation, to treat malignant diseases, such as multiple myeloma. This treatment can result in bone pathologies, namely medication related osteonecrosis of the jaw, with a poor understanding of the molecular mechanism on monocyte differentiation. IFN-β is a pro-resolving cytokine well-known as an osteoclast differentiation inhibitor. Here, we explored whether Zol and/or Dex regulate macrophage osteoclastic differentiation via IFN-β. RAW 264.7 and peritoneal macrophages were treated with Zol and/or Dex for 4–24 h, and IFN-β secretion was examined by ELISA, while the IFN stimulated gene (ISG) 15 expression was evaluated by Western blotting. RANKL-induced osteoclastogenesis of RAW 264.7 cells was determined by TRAP staining following treatment with Zol+Dex or IFN-β and anti-IFN-β antibodies. We found only the combination of Zol and Dex increased IFN-β secretion by RAW 264.7 macrophages at 4 h and, correspondingly, ISG15 expression in these cells at 24 h. Moreover, Zol+Dex blocked osteoclast differentiation to a similar extent as recombinant IFN-β. Neutralizing anti-IFN-β antibodies reversed the effect of Zol+Dex on ISG15 expression and partially recovered osteoclastic differentiation induced by each drug alone or in combination. Finally, we found Zol+Dex also induced IFN-β expression in peritoneal resolution phase macrophages, suggesting these drugs might be used to enhance the resolution of acute inflammation. Altogether, our findings suggest Zol+Dex block the differentiation of osteoclasts through the expression of IFN-β. Revealing the molecular pathway behind this regulation may lead to the development of IFN-β-based therapy to inhibit osteoclastogenesis in multiple myeloma patients.
Immune cells and cytokines are critical effectors in bone remodeling during inflammation and its resolution, as well as in cancer-associated osteopathologies (Tai et al., 2018; Alvarez et al., 2019; Plemmenos et al., 2020). Zoledronic acid (Zol), a nitrogen-containing bisphosphonate (BP), together with the glucocorticoid dexamethasone (Dex), is commonly used for the treatment of MM (Ishikawa et al., 1990). The beneficial action of Zol in MM is mostly attributed to the induction of osteoclast death that limits the formation of lytic lesions (Takayanagi et al., 2002; Lee and Kim, 2011; Schett, 2011). At the cellular level, Zol is taken up by ostoclasts and inhibits the enzyme farnesyl diphosphate synthase. As a result, there is a reduction in cholesterol synthesis, which is required for cytoskeletal reorganization and vesicular trafficking in the osteoclast, leading to osteoclast inactivation (Reszka and Rodan, 2003). The mechanism of action of Dex in MM is not completely elucidated. Dex reduces IL-6 mRNA levels in myeloma cells and induces plasma cell apoptosis by blocking IL-6 (Alexanian et al., 1992). The combined effect of Zol and Dex on osteoclast formation has not been extensively studied. Nevertheless, clinical evidence showed this drug combination increase the risk for a severe side effect called medication-related osteonecrosis of the jaw (MRONJ) (Hüni and Fryar, 1981). MRONJ is characterized by formation of a necrotic jawbone usually after tooth extraction, in patients taking antiresorptive drugs, like BPs, or anti-receptor activator of nuclear factor kappa-B ligand (RANK-L) antibodies alone or in combination with immune modulators or anti-angiogenic medications (Ruggiero et al., 2022). The interplay between immune cells and osteoclasts was previously reported. Immune cells secrete pro and anti-inflammatory cytokines that balance bone resorption and apposition (Roodman, 1993; Van Dyke et al., 2015). Cytokines that stimulate bone resorption include IL-1, TNF-α, IL-6, IL-11, IL-15, and IL-17. Inhibitors of resorption include IL-4, IL-10, IL-13, IL-18, GM-CSF, and IFN-γ. TGF-β and prostaglandins can have either stimulatory or inhibitory effects on resorption, depending on the experimental setting (Martin et al., 1998). The role of cytokines in hematological malignancies, including MM, revealed dysregulation of various cytokines that uncouple the balance between bone resorption and bone apposition, leading to the development of lytic bone lesions (Guise and Mundy, 1998). Interferon β (IFN-β) belongs to the type 1 interferon (IFN) family, representing the first line of endogenous defense mechanisms in response to viruses and bacterial infections. These cytokines are secreted by many cell types, including lymphocytes, macrophages, and endothelial cells (Pertsovskaya et al., 2013). IFN-β promotes bacterial clearance, neutrophil apoptosis, and efferocytosis, as well as macrophage reprogramming to resolution-promoting phenotypes (Kumaran Satyanarayanan et al., 2019). IFN-β is produced in response to M-CSF stimulation of macrophage progenitors as part of the osteogenic process (Yamashita et al., 2012). Similarly, RANKL induces the production of IFN-β in macrophages during osteoclast differentiation. Interestingly, recombinant mouse IFN-β strongly inhibits osteoclastogenesis from bone marrow macrophages stimulated by RANKL in the presence of M-CSF. These results suggest that IFN-β interferes with RANKL signaling, thereby inhibiting osteoclastogenesis (Stark et al., 1998). The combined therapy of Zol+Dex delays the progression or occurrence of bone lesions in MM patients (Tosi et al., 2006). We hypothesized that this drug combination increases IFN-β expression and secretion in macrophages, thereby reducing osteoclastogenesis. The current study aimed to improve our understanding of the molecular mechanism executed by Zol and Dex in the blocking of osteoclastogenesis, focusing on IFN-β. Revealing the aforementioned molecular pathway may perpetuate the development of new biological treatments to inhibit osteoclastogenesis and prevent the worsening of osteolytic lesions following chemotherapy.
RAW 264.7 macrophage cells (ATCC, TIB-71, Virginia) were cultured in Minimum Essential Medium-α (MEM-alpha, Biological Industries, Israel) containing 10% fetal bovine serum (FBS, Biological Industries, Israel), 100 μg/ml penicillin and streptomycin (Biological Industries, Israel) at 37°C in a humidified atmosphere of 5% CO2. The culture medium was changed every 3 days. Cells (1.5 × 106 cells) were seeded in a small flask (25 cm2, Corning, Israel) for expansion for 3 days, and transferred to a big flask (175 cm2, Corning, Israel) with culture medium.
Male C57BL/6 mice were injected intraperitoneally with freshly prepared zymosan A in PBS (1 mg/ml/mouse). After 66 h, the peritoneal exudates were collected. Macrophages were labeled with PE-conjugated rat anti-F4/80 and isolated using EasySep PE selection magnetic beads following the manufacturer’s instructions (Stem-Cell Technology). All animal experiments were approved by the ethics committee for animal experimentation at the University of Haifa (no 597/18).
Peritoneal macrophages (1*106 cells per ml per treatment) were treated with Zol and/or Dex (5–10 µM and 1–10 µM, respectively, as in (Ural et al., 2003) in RPMI, respectively, for 4 h or 24 h. RNA extraction and cDNA synthesis were performed (Applied BioSystem, California). Then, qPCR was performed in triplicates using specific primers for IFN-β. IFN-γ and IFN-α were analyzed as reference genes and HPRT as a housekeeping gene. The reactions were normalized to mHPRT using the ΔΔ threshold cycle (Ct) method. Mouse primer sequences were as follows: mHPRT- Forward 5′- TTGCTCGAGATGTCATGAAGGA -3′, and Reverse 5′- AGCAGGTCAGCAAA GAACTTATAGC -3′, m-IFN-γ: Forward:5′-GCGTCATTGAATCACACCTG-3′ and Reverse:5′- TGAGCTCATTGAATG CTTGG-3′, m-IFN-α-Forward:5′-CCTGAGAGA GAAGAAACACAGCC-3′ and Reverse: 5′-TCTGCT CTGACCACTCCCAG -3′, mIFN-β-Forward:5′-AACCTCACAGGGCGGACTT-3′ and Reverse: TCCCACGTCAATCTTTCCTCTTG-3′ (Sigma Aldrich, Israel). Quantitative RT-PCR analysis was performed using a SyberGreen system on a Step One Plus (Thermo Fisher, Israel).
The expression of IFN-β, ISG15, or GAPDH proteins by macrophages (peritoneal or RAW 264.7) treated with vehicle, Zol, Dex, or Zol+Dex (1.5*106 cells per ml per treatment, 4 or 24 h) was determined. To this end, the protein content of lysed cells was extracted and run using 10% SDS-PAGE (40 µg/lane). Next, separated proteins were transferred to nitrocellulose or PVDF membranes and immunoblotted with rabbit anti-IFNβ, mouse anti-ISG15, or rabbit anti-GAPDH, respectively (Santa-Cruz Biotechnology). The membranes were washed and incubated with appropriate HRP-conjugated secondary antibodies. Then, the membranes were washed, developed using WesternBright ECL (Advansta, CA), and analyzed using Amersham Imager 600. Our analysis focused on the high molecular weight isoforms of IFN-β that are non-secreted intracellular proteins (higher molecular weight than 33 kDa), while the secreted forms (25–33 kDa) were excluded. Densitometry analysis was performed using the ImageJ software.
Culture media from macrophages treated with Zol and/or Dex or vehicle for 4 h were collected and evaluated for their IFN-β content by custom-made ELISA as in. Briefly, MaxiSorp plates were coated with purified anti-mouse IFNβ capture antibody (1 mg/ml) (BioLegend 519202) and incubated overnight at 4°C. Plates were washed 4 times with 0.05% PBS-Tween-20 and blocked at room temperature for 1 h with 1% B.S.A. in PBS. Plates were washed 4 times before 100 µl of standard (BioLegend 581309), or culture supernatants were plated in duplicate and incubated overnight at 4°C. Plates were washed 4 times and incubated with biotinylated anti-IFN-β detection antibody (BioLegend 508105) at 1 mg/ml at room temperature for 1 h. Plates were washed 5 times and incubated with HRP-Avidin for 30 min at room temperature and then developed using TMB substrate and stopped using 2 N sulfuric acid. Plates were read using BioTek PowerWave Plate reader at 450 nm and 540 nm. Results were calculated using a 4-parameter curve-fitting with Gen5 software (BioTek).
Osteoclastogenesis assay was performed with RAW 264.7 cells (1.5*104 cells per well in a 24-well plate) that were incubated with 30 ng/ml RANKL (Peprotech, Israel) for 5 days. RANKL-treated cells were also treated with Zol and/or Dex, recombinant mouse IFN-β (0.25 or 2.5 ng/ml, Biolegend), or anti-IFN-β antibodies (2 μg/ml, Abcam, United Kingdom) for the first 2 days of incubation and then washed. RANKL was supplemented after washing.
To characterize RAW 264.7 cells after differentiation, TRAP and immune-staining were performed. The cells were fixed with 4% paraformaldehyde (PFA)/PBS for 10 min at R.T. Immunocytology was used to detect cells that differentiated into osteoclasts (TRAP+CD11b− cells). The cells were stained with a TRAP kit (387A-1KT, Sigma, United States) for 1 h at 37°C. Then, cells displaying deep purple staining (indicating high TRAP staining) were enumerated as cells that differentiated into osteoclasts. In addition, the cells were stained with anti-CD11b (ab52478, Abcam, United Kingdom) to indicate non-differentiated macrophages. The staining was performed as follows: After fixation, the cells were blocked with 1% BSA for 1 h, washed 3 times with PBS, and stained with Rabbit anti-CD11b for 1 h at R.T. After 3 washes with PBS, the cells were stained with a secondary antibody, HRP-conjugatedanti-Rabbit IgG (ZytoChem Plus HRP Polymer anti Rabbit, Zytomed, Berlin, Germany), then incubated with DAB (SuperPicture™ Polymer Detection Kit, DAB, rabbit, Thermo Fisher Scientific, MA, United States) for 15 min and then washed with distilled water. The cell cultures from both staining methods were captured by a digital camera (Olympus DP70, Olympus, Tokyo, Japan) with a calibration scale, 10 fields from each treatment by ×40 magnification were analyzed by shade using ImageJ software (NIH., Bethesda, MD, United States). The percentage of osteoclasts in the culture was calculated.
Statistical Packages for the Social Sciences (SPSS) or GraphPad Prism were used to analyze all experiments. Descriptive statistics, including means and standard deviation (SD), are shown for each data point. Comparisons between 2 groups were done using unpaired t-test and for more than 2 groups, using one-way ANOVA or mixed-designed ANOVA analysis. The level of statistical significance was set at 5%, and p values are indicated between treatments that showed statistically significant differences.
Combined therapy using Zol+Dex has shown activity in MM. However, the synergy between the drugs leads to reduced skeletal-related events with unclear mechanisms (Tosi et al., 2006). We hypothesized that Zol+Dex treatment blocks osteoclast differentiation via changes in IFN-β levels. Therefore, we analyzed changes in IFN-β secretion from RAW 264.7 macrophages following treatment with Zol (10 µM), Dex (1 µM), and Zol+Dex or vehicle. After 4 h of incubation, IFN-β levels were evaluated by ELISA. The results showed the combined treatment with Zol and Dex for 4 h, but not with each drug alone, induced an increase in IFN-β secretion (Figure 1A). This regulation was specific for IFN-β as neither IFN-α nor IFN-γ transcription was upregulated by Zol+Dex (Supplementary Figure S1). Notably, the increase in IFN-β secretion was associated with a corresponding increase in the expression of ISG15 by macrophages exclusively following Zol+Dex treatment (Figures 1B,C). Thus, the combined treatment with Zol and Dex seems to induce the secretion of biologically-active IFN-β by macrophages.
Next, we determined the effect of Zol and/or Dex on osteoclastic differentiation of RAW 264.7 macrophages. To this end, we first determined whether RAW 264.7 macrophages differentiate into osteoclasts upon exposure to the osteoclastogenic cytokine RANKL as in (Kats et al., 2016). Two staining methods were used to identify the cells in the culture: 1) TRAP staining, which stains osteoclasts, and 2) CD11b staining, which identifies undifferentiated macrophages. Our results showed that treatment with RANKL (30 ng/ml) for 5 days resulted in macrophage differentiation to osteoclasts manifested by an increase in the TRAP+ cells (from 1.02 ± 0.67% to 30.7 ± 4.81% of the cells) and a concomitant decrease in CD11b+ cells (from 92.03 ± 5.1% to 60.1 ± 11.47% of cells). Overall, these results suggest that ∼30.7% of the macrophages differentiated into osteoclasts when cultured with RANKL. The differences between RANKL and control treatments were significant for both staining methods (P*** = 0.0001, Figure 2). Since these results indicate that both staining methods provide similar levels of osteoclastic differentiation, we exclusively used TRAP staining in the following experiments.
Our previous findings showed osteoclastic differentiation of 30.7% of macrophages when cultured with RANKL. Next, we determined the effect of Zol and/or Dex or IFN-β on osteoclastic differentiation. To this end, macrophages were cultured with RANKL for 5 days. In the first 48 h, the cells were supplemented with RANKL and Zol, and/or Dex (10 μM each) or IFN-β (0.25–2.5 ng/ml). After 5 days, the percentage of TRAP+ cells was quantified. As previously, 30.7% ± 4.81 of macrophages underwent osteoclastic differentiation (n = 5) when cultured with RANKL compared to 1.02 ± 0.67% in the control treatment (p < 0.0001). Treatment with Zol+Dex decreased osteoclastic differentiation to 7.12 ± 2.31% (n = 5, P** = 0.002 compared to RANKL + group). Moreover, treatment with Zol or Dex alone gave similar results (6.3 ± 4.1% and 7.8 ± 2.5% of cells, respectively; **p = 0.005 and *p = 0.04, respectively) to the Zol+Dex treatment. As expected, treatment with 2.5 ng/ml of IFN-β antibodies reduced osteoclastic differentiation to 13.5% and was statistically significant compared to RANKL alone or with 0.25 ng/ml IFN-β (***p = 0.007, **p = 0.006, respectively). Notably, Zol+Dex treatment decreased osteoclastic differentiation to a similar extent as IFN-β (Figure 3, n = 4). Next, we determined whether RANKL affects Zol+Dex-induced IFN-β production. Our results show IFN-β levels were reduced following RANKL exposure compared to control treatment. However, higher levels of IFN-β were found when macrophages were treated with Zol+Dex or IFN-β, and RANKL (2.13 ± 0.12 and 2.4 ± 0.42 DU, respectively; p < 0.05). In addition, Zol+Dex treatment without RANKL (3.32 ± 0.36 DU) resulted in the highest intracellular levels of IFN-β compared to controls (1.67 ± 0.16 DU). Overall, these results suggest that treatment of macrophages cultured with RANKL with Zol+Dex or IFN-β reduced osteoclastic differentiation and increased intracellular IFN-β levels.
Since Zol+Dex elevated IFN-β levels in RANKL-treated macrophages and IFN-β reduce osteoclastogenesis in these cells, we examined the role of IFN-β in Zol+Dex induced blockade of osteoclastic differentiation of macrophages. To this end, macrophages were treated with RANKL and Zol+Dex or IFN-β (2.5 ng/ml, as control) as well as anti-IFN-β neutralizing antibodies for 48 h. Then, the medium was replaced and resupplemented with RANKL. After additional 3 days, osteoclastic differentiation was measured by TRAP staining. Our results in Figure 4A indicate that Zol and Zol+Dex reduced macrophage numbers, whereas Dex did not. Notably, IFN-β neutralization did not affect Zol-induced cell death but did promote it in Dex-treated macrophages. Importantly, IFN-β neutralization also significantly restored osteoclastic differentiation following Dex or Zol+Dex treatment (***p < 0.001) but not following Zol alone (Figure 4B). As expected, treatment with anti-IFN-β antibodies did not affect RANKL-induced osteoclastogenesis (data not shown). Notably, neither STAT1 nor STAT3 inhibition reversed the anti-osteoclastogenic actions of IFN-β or Zol+Dex (Supplementary Figure S2), suggesting that other STAT family members mediate the activity of the Zol+Dex-IFN-β axis. Thus, the abrogation of osteoclast differentiation from macrophages induced by Zol+Dex is mediated, at least in part, by early production of IFN-β.
Dex was previously shown to promote macrophages conversion to the pro-resolving satiated/CD11blow phenotype and enhance IL-10 production by these cells (Schif-Zuck et al., 2011), whereas IFN-β was shown to promote the same events (Kumaran Satyanarayanan et al., 2019). Therefore, we sought to determine whether Dex and/or Zol can promote IFN-β expression in resolution phase macrophages. To this end, we recovered macrophages 66 h post zymosan A-induced peritonitis and cultured them for 4–24 h with the indicated drugs. Our results in Figure 5 show a robust increase in IFN-β expression in vehicle and Zol treatments that significantly declined at 24 h. Dex and, to a higher degree, the Zol+Dex treatment induced a much lesser induction of IFN-β at 4 h, but this response ascended at 24 h. Thus, Dex seems to induce IFN-β production by resolution phase macrophages, which is enhanced by treatment with Zol.
Skeletal-related events are a common complication of hematological malignancies and cause severe pain, increased risk of death, and reduced quality of life. The impact of zoledronic acid in the prevention of pain and bone fractures in MM was confirmed in a meta-analysis that evaluated 20 randomized clinical trials with nearly 7,000 patients (Alegre et al., 2014). The direct suppression of osteoclast function by BPs and its consequences on bone remodeling has been reported in a few in vivo studies (Sharma et al., 2013; Alvarez et al., 2019). These effects are perceived to be caused by the inhibition of the intracellular mevalonate (Mev) pathway and the loss of farnesyl pyrophosphate (FPP) and geranygeranyl pyrophosphate (GGPP) synthesis (Gibbs and Oliff, 1997). Glucocorticoids, such as dexamethasone, play an important role in MM treatment. While glucocorticoids have single-agent activity in MM, their combination with other drugs induces higher clinical responses (Burwick and Sharma, 2019). Here, we investigated a potentially new mechanism of action for combined therapy with BPs and Dex in limiting bone resorption, a likely basis for medication-related osteonecrosis of the jaw. Our results showed that the combination of Zol and Dex increased IFN-β secretion as well as the expression of ISG15. We also found that treatment with Dex, Zol+Dex, or IFN-β alone limited osteoclastogenesis in an IFN-β-dependent manner, irrespective of STAT1 or STAT3 activation. Dex has been previously shown to limit inflammation and promote its resolution by limiting neutrophil accumulation (Perretti et al., 2002) and enhancing apoptosis of inflammatory (M1) macrophages while promoting the survival of anti-inflammatory macrophages through the adenosine A3 receptor (Barczyk et al., 2010; Achuthan et al., 2018). Dex was also found to enhance the ability of macrophages to engulf apoptotic cells, a key event in the resolution of inflammation (Maderna et al., 2005). In murine peritonitis, Dex was found to promote the uptake of apoptotic cells and limit inflammatory cytokine production while enhancing IL-10 secretion (Schif-Zuck et al., 2011). Notably, we have recently shown elevated levels of IFN-β in peritoneal exudates during the resolution phase of peritonitis and pneumonia in mice, particularly following the uptake of apoptotic cells by resolution-phase macrophages (Kumaran Satyanarayanan et al., 2019). IFN-β, in turn, promotes macrophage efferocytosis and reprogramming to anti-inflammatory phenotypes (Kumaran Satyanarayanan et al., 2019). Thus, we hypothesized that Dex alone or combined with Zol would induce IFN-β expression and secretion from macrophages. Unexpectedly, our results (Figure 1A) showed that only the combination of Zol+Dex, and not each drug alone, induced a rapid secretion of IFN-β. This secretion did not sustain through 24 h (data not shown). However, it was sufficient to result in a significant increase in the expression of the IFN-β triggered gene ISG15 in Zol+Dex treated macrophages (Figures 1B,C). The fast secretion of IFN-β upon treatment with both drugs suggests that this response does not involve the uptake of apoptotic macrophages but rather the rapid release of internal stores of IFN-β, and could be a result of drug interaction. Thus, Zol and Dex induce a biologically active form of IFN-β from RAW 264.7 macrophages. Recent publications have shown that type I IFNs decreased Mev lipid synthesis during inflammation (York et al., 2015) and inhibited osteoclast differentiation (Takayanagi et al., 2002). Notably, it was previously shown that BPs induce high levels of IFN-β in osteoclasts, which in turn promotes osteoblast maturation and bone formation (Ma et al., 2018). Moreover, Type I IFN signaling was recently found to limit age-related bone loss and osteoclastogenesis through the induction of guanylate-binding protein (GBP) 5 (Ho and Ivashkiv, 2006; Place et al., 2021). In the current study we have shown only the combined treatment with Zol + Dex, but not with each compound alone, increased IFN-β secretion and the expression of ISG15 in an IFN-β dependent manner in RAW 264.7 macrophages. Moreover, IFN-β, at low concentrations (2.5 ng/ml) inhibited macrophage differentiation to osteoclasts, and IFN-β blockage significantly abrogated either Dex or Zol+Dex inhibition of osteoclastogenesis but did not affect the control treatment. Altogether, these results support our hypothesis that Zol+Dex block macrophage osteoclastogenesis through the secretion of IFN-β and its action on macrophages that might also involve attenuation of Mev synthesis. In macrophages, IFN-β activates signal transducers and activators of transcription (STAT) 1 and STAT3, mediating the antiviral and inflammatory effects of IFN-β (Ho and Ivashkiv, 2006; Kumaran Satyanarayanan et al., 2019). To detect whether STAT1 or STAT3 mediates the inhibitory effect of Zol+Dex or IFN-β on osteoclastogenesis, specific inhibitors of these transcription factors were used in the aforementioned differentiation assay. Our results indicate that neither the STAT1 nor the STAT3 inhibitor restored the differentiation of osteoclasts upon inhibition by Zol+Dex (Supplementary Figure S2). Nevertheless, the STAT1, but not the STAT3 inhibitor, restored osteoclastogenesis (47.18% recovery) upon inhibition by IFN-β. Notably, this recovery did not reach statistical significance, probably due to the low concentration of fludarabine. Recent publications have shown that STAT3 inhibitors down-regulate the expression of T-bet, GATA3, IL12Rb2, and IFN-γ, as well as the formation of osteoclasts (Holland et al., 2007; Li, 2013). On the other hand, another report has shown that STAT3 deficiency causes skeletal and connective tissue disorders. Notably, Zol treatment increases bone density in these patients by inhibiting the protein suppressor of cytokine signaling 3 (SOCS3), which results in a switch from IL-6 to IL-10 production in macrophages and a decrease in bone loss. The transcription of SOCS3 is regulated by nuclear accumulation of phosphorylated STAT3, and STAT3 is downregulated by SOCS3 (Staines Boone et al., 2016). These results support our conclusion that inhibition of STAT3 does not promote osteoclast differentiation. STAT1 is essential for gene activation in response to interferon stimulation. Recent publications showed high levels of osteoclasts in bone marrow macrophages from STAT1-deficient mice treated with IFN-β and RANKL (Takayanagi et al., 2002). This manuscript has suggested a signaling cross-talk between RANKL and IFN-β via ISGF3, which is composed of STAT1, 2, and IRF-9, and that inhibition of STAT1 impairs the osteogenesis processes by enhancing osteoclast differentiation. Another publication showed that STAT1 protein levels decreased over time after Zol treatment (Muratsu et al., 2013). Our results have shown that STAT1 inhibition did not affect the drug treatment but partially restored osteoclastogenesis upon treatment with IFN-β, albeit without statistical significance. These results suggest STAT1 is not involved in drug-induced IFN-β expression. However, the inhibitory effect of IFN-β on osteoclastogenesis might be dependent, at least in part, on STAT1 activity. The bone destruction in MM is mediated by osteoclasts, specialized bone-resorbing cells engaged in normal bone remodeling. Myeloma cells and marrow stromal cells produce factors that induce osteoclast formation and activation, thus changing the balance between bone apposition and bone resorption (Muratsu et al., 2013). Combinational therapy of Zol+Dex is clinically effective in preventing and managing myeloma-induced bone disease (Mhaskar et al., 2012). Additional osteoclasts targeting drugs such as: Cyclosporin A (Orcel et al., 1991), Revoremycin A, RANKL antibodies (Ding et al., 2021), Idelalisib (Yeon et al., 2019) and Compactin (Woo et al., 2000) were found to inhibit different satges in osteoclastogenesis and may be usefull to treat MM. Our findings suggest a new pathway for suppressing bone resorption that involves IFN-β. Within the limits of this study, it can be concluded that Zol+Dex promotes IFN-β secretion. Consequently, IFN-β limits macrophage differentiation into osteoclasts downstream of the Zol+Dex treatment. Notably, IFN-β based therapies are used to treat multiple sclerosis patients with no evidence of MRONJ (Jongen et al., 2011). Thus, IFN-β therapy might be used to inhibit osteoclastogenesis in MM patients, with minimal risk to develop osteonecrosis of the jaw. |
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PMC9648997 | Brian E. Dixon,William F. Fadel,Thomas J. Duszynski,Virgina A. Caine,Joeseph F. Meyer,Michele Saysana | Mitigation of COVID-19 at the 2021 National Collegiate Athletic Association Men’s Basketball Tournament | 10-11-2022 | COVID-19 pandemic,SARS-CoV-2 virus,Public Health Surveillance,Communicable disease control,Infectious disease transmission,Basketball,Sports,Athletes | Background Data are lacking regarding the risk of viral SARS-CoV-2 transmission during a large indoor sporting event involving fans utilizing a controlled environment. We sought to describe case characteristics, mitigation protocols used, variants detected, and secondary infections detected during the 2021 National Collegiate Athletic Association (NCAA) Men’s Basketball Tournament involving collegiate athletes from across the U.S. Methods This retrospective cohort study used data collected from March 16 to April 3, 2021, as part of a closed environment which required daily reverse transcription-polymerase chain reaction (RT-PCR) testing, social distancing, universal masking, and limited contact between tiers of participants. Nearly 3000 players, staff, and vendors participated in indoor, unmasked activities that involved direct exposure between cases and noninfected individuals. The main outcome of interest was transmission of SARS-CoV-2 virus, as measured by the number of new infections and variant(s) detected among positive cases. Secondary infections were identified through contact tracing by public health officials. Results Out of 2660 participants, 15 individuals (0.56%) screened positive for SARS-CoV-2. Four cases involved players or officials, and all cases were detected before any individual played in or officiated a game. Secondary transmissions all occurred outside the controlled environment. Among those disqualified from the tournament (4 cases; 26.7%), all individuals tested positive for the Iota variant (B.1.526). All other cases involved the Alpha variant (B.1.1.7). Nearly all teams (N = 58; 85.3%) reported that some individuals had received at least one dose of a vaccine. Overall, 17.9% of participants either had at least one dose of the vaccine or possessed documented infection within 90 days of the tournament. Conclusion In this retrospective cohort study of the 2021 NCAA Men’s Basketball Tournament closed environment, only a few cases were detected, and they were discovered in advance of potential exposure. These findings support the U.S. Centers for Disease Control and Prevention (CDC) guidelines for large indoor sporting events during the COVID-19 pandemic. | Mitigation of COVID-19 at the 2021 National Collegiate Athletic Association Men’s Basketball Tournament
Data are lacking regarding the risk of viral SARS-CoV-2 transmission during a large indoor sporting event involving fans utilizing a controlled environment. We sought to describe case characteristics, mitigation protocols used, variants detected, and secondary infections detected during the 2021 National Collegiate Athletic Association (NCAA) Men’s Basketball Tournament involving collegiate athletes from across the U.S.
This retrospective cohort study used data collected from March 16 to April 3, 2021, as part of a closed environment which required daily reverse transcription-polymerase chain reaction (RT-PCR) testing, social distancing, universal masking, and limited contact between tiers of participants. Nearly 3000 players, staff, and vendors participated in indoor, unmasked activities that involved direct exposure between cases and noninfected individuals. The main outcome of interest was transmission of SARS-CoV-2 virus, as measured by the number of new infections and variant(s) detected among positive cases. Secondary infections were identified through contact tracing by public health officials.
Out of 2660 participants, 15 individuals (0.56%) screened positive for SARS-CoV-2. Four cases involved players or officials, and all cases were detected before any individual played in or officiated a game. Secondary transmissions all occurred outside the controlled environment. Among those disqualified from the tournament (4 cases; 26.7%), all individuals tested positive for the Iota variant (B.1.526). All other cases involved the Alpha variant (B.1.1.7). Nearly all teams (N = 58; 85.3%) reported that some individuals had received at least one dose of a vaccine. Overall, 17.9% of participants either had at least one dose of the vaccine or possessed documented infection within 90 days of the tournament.
In this retrospective cohort study of the 2021 NCAA Men’s Basketball Tournament closed environment, only a few cases were detected, and they were discovered in advance of potential exposure. These findings support the U.S. Centers for Disease Control and Prevention (CDC) guidelines for large indoor sporting events during the COVID-19 pandemic.
Mass gatherings, including indoor sporting events, are associated with the transmission of SARS-CoV-2 [1], the virus that causes coronavirus disease 2019 (COVID-19). Not only are attendees of these events exposed to others with active infection, especially given that many individuals are asymptomatic [2], but individuals infected at large events often cause secondary infections in the community, dubbing mass gatherings as ‘super-spreader’ events. Because of their potential impact on community spread, public health guidelines and policies across the United States restricted or prohibited mass gatherings for most of 2020, resulting in cancellations of many large sporting events [3]. Cancelled events for 2020 included the National Collegiate Athletic Association (NCAA) Men’s and Women’s college basketball tournaments, known as March Madness, as well as most conference basketball tournaments and the remaining winter and spring NCAA tournaments [4]. When large sporting events and tournaments resumed, event organizers followed various mitigation protocols based on recommendations from the Centers for Disease Control and Prevention (CDC) as well as local public health departments. For example, the National Basketball Association (NBA) implemented a controlled campus environment in which all games were played in a single sports complex without fans and where players were confined to a limited set of facilities for the duration of the multi-day event [5]. Major League Baseball did not use a controlled environment. Although fans were not allowed in the stadiums, teams still travelled to and played games in multiple cities while following mitigation protocols involving testing and quarantining [6]. Beyond models that estimate secondary infections [7], there exists no evidence from large indoor sporting events in which fans were allowed. Moreover, there is little evidence on the impact of prior infections and vaccination on risk of infection due to large indoor sporting events where fans are present. In this analysis, we examine the impact of SARS-CoV-2 infection mitigation protocols implemented by the NCAA for its 2021 Men’s Basketball Tournament held in Indianapolis, Indiana in late March and early April 2021. We outline the mitigation protocols in place to keep involved athletes, coaches, and others safe, detail positive cases and clusters identified during the tournament, examine the influence of genetic variants of SARS-CoV-2, and explore the impact of prior infection and vaccination on risk for secondary infection.
The NCAA hosted its 2021 March Madness tournament using a layered set of mitigation strategies. Informed by CDC guidelines, recommendations from its medical advisory board, local public health agencies, and host venues, the NCAA developed and implemented a comprehensive plan involving a controlled environment, regular screenings, social distancing, and masks for everyone involved in the tournament. The controlled environment required that players, coaches, NCAA officials, and support staff remain in regulated, designated areas (including living areas, transportation, and basketball venues) that adhered to mitigation protocols including rigorous cleaning regimens. Teams could not leave the environment once they arrived at the tournament, and movement into and out of the environment was dictated by the mitigation protocols, especially multiple negative tests prior to entry. Protocols were developed in late 2020 with final sign off from the Marion County Public Health Department, the jurisdiction responsible for the main site of the tournament, on February 1, 2021, when final capacity (25%) for fans was determined based on local epidemiology of SARS-CoV-2. This retrospective cohort study was approved by the Indiana University (IU) institutional review board and followed Strengthening the Reporting of Observational Studies in Epidemiology guidelines for cohort studies.
The NCAA categorized individuals into tiers based on its COVID-19 Guidance on Multiple Teams in the Same Location [8], derived from DiFiori et al. [9] Tier 1 involved individuals for whom physical distancing and face coverings were not used, including athletes, coaches, medical staff, trainers, and officials. Individuals in Tier 2 came into close contact with Tier 1 individuals but generally maintained physical distance and used face coverings. This included bus drivers, NCAA administrative staff, and event security. Tier 3 individuals provided event services, but they did not come into close contact with Tier 1 individuals. These individuals included housekeeping, catering, and the media. Tier 4 represented fans admitted to the venue but restricted from interacting with Tier 1 individuals. For example, the parents of players were not allowed to have any in-person contact during the tournament. Table 1 details the mitigation strategies observed during the tournament. Tier 1 individuals were required to test negative for seven consecutive days prior to arrival in Indianapolis. These individuals were isolated upon arrival until they screened negative for SARS-CoV-2 virus twice. Tier 2 individuals were tested upon arrival and placed into quarantine until a single negative result. All individuals were expected to observe universal masking and social distancing throughout the tournament. All Tier 1 individuals were housed in Marion County, Indiana, within the controlled environment. Tournament buses transported Tier 1 individuals to venues. Tier 1 individuals were not allowed to enter any facilities outside tournament venues, with buses returning to designated hotels in Indianapolis after competition. Each team was housed on a separate floor of a designated hotel, and workout and practice venues were staggered and cleaned between uses, limiting interaction and exposure when not in competition. Players were socially distanced when in transport. Within teams, individuals could interact during meals, practices, workouts, games, and on their hotel floor. When not practicing or competing, individuals observed universal masking. Indiana University Health, the state’s largest health system with a state-of-the-art clinical laboratory facility and academic medical center in downtown Indianapolis, was responsible for screening and testing individuals in Tiers 1 and 2 daily. All Tier 1 individuals received a daily self-administered, observed anterior nasopharyngeal (NP) swab. Specimens were pooled for analysis in small (N = 5) batches and run 6–8 h after collection. If any specimen returned positive, then all samples in the pool would be tested individually. If the confirmatory test was positive, the individual was presumed positive and placed into isolation. If the individual was asymptomatic, a second test was performed to confirm active infection. Should the second test come back negative, then a third test was performed. The individual remained in isolation until both second and third tests could be analyzed to confirm negative status. Individuals confirmed to be positive were excluded from the rest of the tournament and sent home for the remainder of their quarantine period. Laboratory diagnostics were performed using assays with emergency use authorization to detect viral RNA via RT-PCR by IU Health on either the Roche cobas® 8800 System or Roche cobas® Liat® PCR System. Anterior NP swabs were placed in single tubes before being assayed. Most tests were conducted on the Roche cobas® 8800 System. If a screening test was positive, a second test was performed on the Roche cobas® Liat® PCR System. The cobas® SARS-CoV-2 Test performed on the cobas® 8800 System is a single-well dual target assay, which includes both specific detection of SARS-CoV-2 and pan-sarbecovirus detection for the sarbecovirus subgenus family that includes SARS-CoV-2. The test detects the genetic signature (RNA) of the SARS-CoV-2 virus in nasal, nasopharyngeal, and oropharyngeal swab samples. Individuals at least two weeks post-infection and within 90 days of the first known date of infection were excluded from the daily testing regimen. Any individual with a documented positive SARS-CoV-2 infection after December 5, 2020, was excused from pre-arrival testing requirements.
Our data include longitudinal laboratory tests collected before and during the NCAA tournament for all participating players, staff, and vendors (e.g., bus drivers). History of prior infection and vaccination records were documented using team attestations completed by university health officials, including team doctors. These records were linked to laboratory data using name and date of birth. Out of 2,738 individuals tested on site, 2,660 (97.2%) were matched to attestation records. Individuals were grouped into their respective Tiers based on designations from the NCAA. Data further included the results of genotyping performed by IU Health for each positive PCR result.
Descriptive statistics (counts and proportions) were calculated for (1) individuals partially vaccinated (1 dose of an mRNA vaccine), (2) individuals fully vaccinated (2 doses of an mRNA vaccine or 1 dose of the Ad26.COV2.S vaccine), and (3) individuals who reported an active infection within 90 days (December 8, 2020 or later) of the start of the Tournament. Analyses were conducted using R version 4.0 (R Core Team).
A total of 28,311 tests were conducted over 26 days for individuals involved in the tournament. Out of the matched cohort with 2,660 people, a total of 15 individuals (0.56%) screened positive. Figure 1 summarizes laboratory testing volumes by day of the tournament along with the date of positive cases, stratified by participant tier. Tournament milestones, such as the start of the first round, are marked on the figure. The volume of testing declined over time as teams were eliminated from the tournament, resulting in the eliminated teams leaving Indianapolis.
Eight cases (53.3%) occurred among Tier 1 participants, and all cases were detected before any individual played in or officiated a game. Prior to the first round of play, three players, one school administrator, and one official tested positive. Two of the players were detected while in quarantine upon arrival in Indianapolis, so they were sent home and found to have no close contacts. The school administrator returned home before their team played in the first round, and their contact with players was limited. The official tested positive before officiating any games. Furthermore, contact tracing linked the official to five individuals with whom he had close contact outside of the controlled environment. These individuals, including other officials, were excluded from the tournament. The third player was detected one day after quarantine ended, which enabled him to eat and practice with his team. Two days later, three additional individuals associated with the third player’s team also tested positive. Because multiple cases were detected within the same team, the team was disqualified for tournament play and sent home before their first-round game, limiting exposure to other Tier 1 individuals. The three subsequent cases are not suspected to be caused by the first player given their timing. Contact tracing revealed the team was most likely exposed while playing in a conference tournament prior to arrival in Indianapolis. Also linked to the conference tournament was the official who tested positive. Exposure likely occurred in a hotel where multiple teams and individuals co-mingled in common spaces and elevators, which was prohibited in Indianapolis.
Three cases (20%) occurred among Tier 2 individuals, and no Tier 2 individuals were linked to any other cases. The individual who tested positive at the start of the tournament was a digital media company employee. Moreover, they originally tested positive on March 3, 2021; therefore, retesting was not required according to NCAA protocols. They were asked to quarantine until March 17, 2021, and then they could come back to work inside the controlled environment. The second case involved a bus driver who did not socially distance when in the community. Contact tracing revealed that this driver engaged in eating and drinking with other bus drivers, although none of those individuals tested positive. The third case involved an NCAA administrative staff person who did not interact with teams.
There were four cases (26.7%) among Tier 3 individuals. Two cases identified on March 16 and one identified on March 25, were travel company employees who provided logistical support. These individuals attended a bourbon tasting event in the community prior to the start of the tournament where they were in close contact with one another for an extended period of time. The third individual, a close contact of the other two cases, tested positive on Day 10 of quarantine. The final case involved a NCAA Men’s Basketball Committee member who travelled to Indianapolis for the final four games and tested positive before attending any games.
Sequencing results are summarized in Fig. 2. All individuals associated with the team disqualified from the tournament (4 cases; 26.7%) tested positive for the Iota variant (B.1.526), a lineage predominantly circulating in New York starting in November 2020 monitored by CDC as a variant of interest [10]. All other sequenced cases (8 cases; 53.3%) involved the Alpha variant (B.1.1.7), a lineage predominantly from the United Kingdom first detected in the U.S. at the end of 2020 and classified during the tournament by CDC as a variant of concern [11]. Three cases could not be sequenced due to relatively low presence of the virus in the specimens. At the start of the tournament, the Alpha and Iota variants accounted for 21.3% and 5.4% of cases in the U.S., respectively. By the end of the tournament, Alpha and Iota variants accounted for 51.9% and 11.5% of cases, respectively. These estimates come from GISAID [12], a global initiative that maintains a repository of virus sequence data. Neither variant is associated with more severe disease. The Alpha variant (B.1.1.7) is associated with more efficient transmission than ancestral strains (e.g., those from 2020) and was predominant in the U.S. by the end of March 2021 {McCormick, 2022 #4285}.
Just under 1-in-5 participants in Tier 1 (18.9%) had some level of immunity against SARS-CoV-2 at the start of the tournament. As summarized in Table 2, about 1-in-10 individuals across tiers were fully vaccinated ahead of the tournament. Immunity levels were highest among Tier 1 participants and lowest among Tier 3 participants. Recent infection was highest among Tier 2 participants and lowest among Tier 3 participants. The mean vaccination (partial or full) rate among participating teams was 15.7% (Median 9.2%; SD 19.3%; Range 0%-97%). Full information reported from the 68 teams (anonymized) is summarized in Table 3. Most teams (N = 58; 85.3%) reported that some individuals had received at least one dose of a vaccine. Nineteen teams (27.9%) had full vaccination rates higher than 13.0%, which was the U.S. mean rate at the start of the NCAA tournament. Thirteen teams (19.1%) reported that no one on the team was fully vaccinated, and 12 teams (17.6%) reported that no one possessed any dose of the vaccine. Most vaccinations were reported among coaching and training staff, who likely met age-based criteria used by states to rollout vaccinations. However, one team had every player and most coaches vaccinated. Nearly one third (N = 22; 32.4%) of teams reported that at least one individual had a prior infection within 90 days of the tournament. The mean prior infection rate of 2.8% (Median 0.0%; SD 7.1%; Range 0%-47.1%) was much lower than the full vaccination rate. Five teams (7.4%) reported that somewhere between 15 and 47% of their Tier 1 individuals were infected recently. Infections were clustered in early February, about 6 weeks before the NCAA Tournament.
Infection rates per 100,000 population for Indianapolis, the State of Indiana, and the United States during the March Madness timeframe are summarized in Fig. 3. The national rate rose gradually from just above 16 per 100,000 to 20 per 100,000 just prior to the Final Four. Then the U.S. rate remained flat through one week after the tournament. The rate climbed for the State of Indiana from just above 10 per 100,000 to 18 per 100,000 one week after the tournament ended. The rate for Indianapolis, which encompasses nearly all of Marion County, Indiana, also climbed from just below 10 per 100,000 to nearly 19 per 100,000 one week after the tournament ended. The steepest increases occurred just after the end of the tournament.
The controlled environment with robust mitigation strategies used by NCAA and its host city provides a model for conducting large sporting events in the middle of a global pandemic for an emerging infectious disease. Universal masking and distancing combined with limited interaction and daily testing within a controlled environment reduced exposure while enabling maximal sensitivity in the detection of positive cases. Just 0.5% of individuals in the controlled environment tested positive during the tournament when nationally the CDC reported a positivity rate of 4.1%. Moreover, protocols to limit non-play interaction further protected others involved with the event. All cases among Tier 1 individuals directly involved in tournament play were identified upon arrival, prior to first-round games. Frequent testing prior to arrival in Indianapolis also identified individuals before entry into the controlled environment. Secondary transmission occurred outside the controlled environment, either in the community at a restaurant or private venue, or prior to arrival in Indianapolis. This is in stark contrast to open sporting events like major league baseball, in which players come and go from facilities. During a single three-game series in Philadelphia involving 146 individuals, 20 players (Tier 1) tested positive and exposures were linked to non-play activities on site [6]. The NCAA tournament saw fewer than 20 individuals test positive out of 2,849 individuals involved in the controlled environment. This rate is similar to the one observed by the NBA [5], which also employed a controlled environment. An additional factor that likely contributed to the safety of the athletes and staff, above and beyond the controlled environment and protocols, was the fact roughly 1-in-5 individuals possessed some level of immunity to SARS-CoV-2. Several teams had vaccination rates higher than the U.S. rate at the time of the tournament (13.0%). Moreover, several individuals (2.8%) reported a recent infection. Although reinfections have been shown to occur in individuals with infection-induced as well as vaccine-induced immunity [13–15], prior studies suggest that immunity from natural infection can last up to 8 months [16]. Bozio et al. [17] found that vaccination provides 5-times protection compared with natural infection. Antibodies in 20% of participants likely helped protect some athletes and staff. The impact of the tournament to Indianapolis, Indiana, and the Nation was likely negligent at best. Despite the observed rise in the infection rate per 100,000 population one week after the tournament ended, similar to O'Donoghue [18], concluding community spread is attributable to the NCAA tournament would be an ecological fallacy. The tournament concluded during western orthodox Easter weekend, when many large churches held indoor gatherings and many families likely had close contact indoors with extended relatives. Moreover, the final weekend occurred at the end of Passover, a week-long religious celebration. The study by Vest et al. [19] suggests around 75% of spectators were correctly masked inside NCAA tournament venues. Given a very low rate of infections within the environment and limited secondary transmissions linked to active cases, it is unlikely the NCAA tournament contributed meaningfully to community spread.
The primary strengths of this study include the availability of detailed epidemiologic and daily quantitative results in a large, closed cohort with measured engagement in regular contact without masking and physical distancing among a subset of individuals. Importantly, this study was able to control for sources of virus exposure because of the nature of the Indianapolis campus and the multiple tests prior to travel. Further, to our knowledge, this study is the first to include observations from repeated unmasked exposures between recovered SARS-CoV-2 positive individuals with prolonged viral shedding, vaccinated individuals, and presumed SARS-CoV-2-naïve individuals. We did not observe evidence of viral transmission among these individuals within the controlled environment. This finding provides additional evidence that individuals are unlikely to have replication-competent virus as a recovered individual. Like the NBA study [5], this study reports findings among ambulatory immunocompetent individuals who were being tested daily within a closed cohort. The limitations of this retrospective cohort study include limited ability to confirm whether any secondary infections occurred once infected individuals returned home. Although we contacted local health authorities outside Indiana, few provided any response. Second, data on vaccination and prior infection relied upon attestation by each team’s medical doctor. Vaccination cards and prior test results were not confirmed, potentially underreporting individuals with at least one dose of a vaccine or asymptomatic infection. Finally, data reflect infections with the predominant strains during the study period and may not be applicable to other variant strains of SARS-CoV-2.
Our study suggests that a controlled environment with limited capacity for spectators minimizes transmission while allowing resumption of large sporting events during a pandemic. |
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PMC9648999 | Maneesha Murali,Bhagyalakshmi Nair,V. R. Vishnu,T. P. Aneesh,Lekshmi R. Nath | 2,4-Dihydroxycinnamic acid as spike ACE2 inhibitor and apigenin as RdRp inhibitor in Nimbamritadi Panchatiktam Kashayam against COVID-19: an in silico and in vitro approach | 10-11-2022 | Nimbamritadi Panchatiktam Kashayam,Spike ACE2,Viral replication,Pseudovirus inhibition assay | Nimbamritadi Panchatiktam Kashayam (NPK) is an ayurvedic formulation composed of ingredients with potent anti-viral activities. We studied the interaction energy of 144 phytoconstituents present in NPK against spike receptor-binding domain (RBD) complexed with ACE2 protein (PDB ID: 6LZG) and RNA-dependent RNA polymerase protein (PDB ID: 7BTF) using Biovia Drug Discovery studio. The result indicated that 2,4-hydroxycinnamic acid exerts more significant binding affinities (28.43 kcal/mol) than Umifenovir (21.24 kcal/mol) against spike ACE2. Apigenin exhibited the highest binding affinities (54.63 kcal/mol) compared with Remdesivir (24.52 kcal/mol) against RdRp. An in vitro analysis showed a reduction in the number of lentiviral particles on transfected HEK293T-hACE2 cells as assessed by pseudovirus inhibition assay. At the same time, the tested compounds showed non-toxic up to 100 µg/ml in normal cells by MTT assay. The study highlights the plausible clinical utility of this traditional medicine against SARS CoV2. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s11030-022-10552-z. | 2,4-Dihydroxycinnamic acid as spike ACE2 inhibitor and apigenin as RdRp inhibitor in Nimbamritadi Panchatiktam Kashayam against COVID-19: an in silico and in vitro approach
Nimbamritadi Panchatiktam Kashayam (NPK) is an ayurvedic formulation composed of ingredients with potent anti-viral activities. We studied the interaction energy of 144 phytoconstituents present in NPK against spike receptor-binding domain (RBD) complexed with ACE2 protein (PDB ID: 6LZG) and RNA-dependent RNA polymerase protein (PDB ID: 7BTF) using Biovia Drug Discovery studio. The result indicated that 2,4-hydroxycinnamic acid exerts more significant binding affinities (28.43 kcal/mol) than Umifenovir (21.24 kcal/mol) against spike ACE2. Apigenin exhibited the highest binding affinities (54.63 kcal/mol) compared with Remdesivir (24.52 kcal/mol) against RdRp. An in vitro analysis showed a reduction in the number of lentiviral particles on transfected HEK293T-hACE2 cells as assessed by pseudovirus inhibition assay. At the same time, the tested compounds showed non-toxic up to 100 µg/ml in normal cells by MTT assay. The study highlights the plausible clinical utility of this traditional medicine against SARS CoV2.
The online version contains supplementary material available at 10.1007/s11030-022-10552-z.
COVID 19 is a global pandemic caused by a dreadful pathogen SARS CoV2 (severe acute respiratory syndrome coronavirus - 2), which resembles highly contagious fatal pneumonia with severe acute respiratory distress, which leads to a wide mortality ratio [1]. It is a highly enveloped, positive single-stranded RNA virus with four structural proteins: M protein, N protein, E protein, S protein, and sixteen non-structural proteins [2, 3]. SARS CoV2 mediates endocytosis by adhering the spike protein homotrimers with the ACE2 receptors expressed on the surface of host cells, thereby aiding receptor recognition and membrane fusion [4, 5]. Following the viral entry into the host cell, another critical event involved in the viral lifecycle process is the Replication/transcription complex (RTC). The formation of RTC helps in viral genome replication and subgenomic mRNA synthesis [6]. RNA-dependent RNA polymerase enhances a functional catalytic activity upon the formation of genome replication machinery; thus, it stimulates the frequent addition of hundreds to thousands of nucleotides and enhances elongation [7]. Traditional sources of Ayurveda have significantly contributed to averting the progression of almost all severe illnesses with potential effectiveness. Nimbamritadi Panchatiktam Kashayam (NPK) is an Ayurvedic herbal decoction mainly suggested during the infectious spread of Dengue and Malaria constituted with ingredients of superior anti-viral activities. It includes several herbal constituents such as Azadirachta indica (Neem), Tinospora cordifolia (Amruta), Adhatoda zeylanica (Vasaka), Trichosanthes dioica (Pointed guard), Solanum Xanthocarpum (Kantakari,), etc. which are well known for its anti-viral activities. Among the former mentioned herbal components, Solanum Xanthocarpum (Yellow nightshade) constitutes 2,4-dihydroxycinnamic acid and apigenin. Also, apigenin is present in another herbal plant named Azadirachta indica (Neem) [8–13]. Targeting entry and replication of coronavirus with traditional formulation may be an ideal strategy, which can help further develop effective therapeutic interventions against SARS CoV2 [14–17]. The present study identifies the most active phytochemicals within the formulation targeting the entry and replication pathway of the virus in silico and further confirms with in vitro studies.
Molecular docking is a crucial platform for computer-aided drug design and structure-based biology that helps to predict drug–target interaction concerning binding geometry. Molecular docking favors the interpretation of the behavioral nature of drugs at the binding site and the characterization of drug–target complex [18]. We analyzed the ADMET properties of 90 phytoconstituents, among which 50 passed the ADMETox with critical properties. Finally, around 25 ligands were selected for further molecular docking, and the most active ligands were further subjected to in vitro studies (Figure S1 and Table S4). Lipinski’s rule of five indicates that 2,4-dihydroxycinnamic acid and apigenin possess drug-like properties, and there is no violation of the law (Supporting information Table S1) [19, 20]. In our present work, we determine the binding affinity of various phytoconstituents constituted within Nimbamritadi Panchatiktam Kashayam (NPK) toward spike RBD-ACE2 complex and RdRp compared with standard drugs. Several clinically available repurposed drugs like Chloroquine, Hydroxychloroquine, Umifenovir, and Remdesivir with existing literature were selected as standard drugs for comparison [21–24]. Results reveal that 2,4-dihydroxycinnamic acid interacts with nine amino acid residues: Asparagine 90 (ASN A:90), Lysine 26 (LYS: 26), Threonine 92 (THR: 92), Proline 389 (PRO: 389), Glutamine 96 (GLU A:96), Asparagine 33 (ASN: 33), Leucine 29 (LEU: 29), Aspartate 30 (ASP: 30), and Valine 93 (VAL: 93). Reports indicate that the amino acids present at the spike RBD-ACE2 interface undergo glycosylation and favor the modifications for binding with the spike receptor-binding domain of SARS CoV2, enhancing the virion tropism [25, 26]. Additionally, 2,4-dihydroxycinnamic acid possesses a strong interaction bond with these amino acids due to two conventional hydrogen bonds. Also, the conventional hydrogen bond of 2,4-dihydroxycinnamic acid with spike ACE2 is at a short distance, making the bond more vital and stable [27]. Conventional hydrogen bonding is reported to be crucial in all types of biomolecular interactions. Thus, it is termed the “master key of molecular recognition.” Moreover, conventional hydrogen bonds are more specific and offer stereo chemical orientation. It is breakable under thermally activated reactions but offers long-term stability toward target–ligand interaction [28]. Similarly, apigenin possesses more amino acid interaction with RdRp and includes three conventional hydrogen bonding at the site of LYS: 545, ARG A: 553, and ARG A: 555. Hydrogen bonding of apigenin with these amino acid residues at the nucleoside triphosphate entry channel makes the bond more robust and can modulate the interaction. Thus, the binding of apigenin may enhance interaction with these amino acid residues and may help introduce the clinical activity of apigenin at the interaction site by inhibiting the activity of RdRp [29]. Physiochemical characterization and secondary structure analysis were done using online bioinformatics tools like ProtParam and SOPMA to identify the best PDB. The results revealed that 6LZG (spike RBD-ACE 2) and 7BTF (RdRp) are suitable PDB IDs for the selected active sites. Selected phytoconstituents docked with determined PDB IDs (6LZG and 7BTF), and the docking scores obtained compared with standard drugs Umifenovir and Remdesivir, respectively [30, 31]. Results indicate that 2,4-dihydroxycinnamic acid and apigenin are the best candidate molecules with PDB ID: 6LZG and 7BTF, respectively, based on docking score and number of amino acid interactions. Additionally, 2,4-dihydroxycinnamic acid and apigenin interact with identical hot spot amino acid residues as in the case of standard drugs like Umifenovir and Remdesivir, respectively. This indicates that they may be clinically promising in combating viral entry and replication of SARS CoV 2 by showing significant interactions with these hot spot amino acid residues present at spike RBD- ACE2 interface and at the RdRp site [25, 26, 29]. (Figs. 1, 2).
The biological safety of the two most active compounds, apigenin, and 2,4-dihydroxycinnamic acid, was analyzed on Vero cells by MTT. Both the compounds exhibited a dose-dependent cytotoxic effect with IC50 of 87.55 µg/ml and 118.4 µg/ml for apigenin and 2,4-dihydroxycinnamic acid, respectively. However, the low-to-medium concentration range (1–50 µg/ml) did not exhibit a considerable cytotoxic effect. Apigenin and 2,4-dihydroxycinnamic acid exhibited cell viability of 76.92% and 81.16% on Vero cells after 48-h treatment at 50 µg/ml. Finally, based on the cell viability assay, we have selected a non-toxic concentration range of 1–50 µg/ml for both compounds for further in vitro Pseudovirion analysis Figs. 3 and 4 [32].
Pseudovirion assay offers a safe-effective protocol to study highly infectious and pathogenic viruses like SARS CoV2. SARS CoV2-spike-pseudotyped lentiviral particles produced in transfected HEK293T-hACE2 cells have green fluorescence due to Zsgreen traceable marker. After 48 h treatment with NPK (1: 500 dilutions) upon Pseudovirion infected cell line, it exhibited a significant reduction in the fluorescent marker activity, which denotes its anti-SARS CoV2 effect. Importantly, this pseudovirion neutralization effect is almost similar to the standard virion entry inhibitor, Umifenovir, at 41.89 µM concentration. We evaluated the most active candidates within the NPK as evidenced by in silico. We observed a prominent inhibitory effect on the SARS CoV2 virus after apigenin and 2,4-dihydroxycinnamic acid treatment at 74.0083 µM and 111.01 µM, respectively, Fig. 5. Several plant-based bioactive compounds exhibit pharmacological activities against various disease ailments [33]. Cinnamic acid derivatives and apigenin are associated with anti-viral effects. In a study by Zhang et al. and his team, apigenin blocked the EV71 infection by disrupting the viral RNA association with hnRNP (heterogeneous nuclear ribonucleoproteins) A1 and A2 proteins. Also, an estimated EC50 value of 10.3 µM and CC50 value of 79 µM for apigenin was found to block the EV71 infection. From the study, Zhang et al. and his team identified that apigenin selectively suppressed the GFP expression. Thus, apigenin was identified as an effective anti-viral agent against EV71 infection [34]. Several scientific reports state that cinnamic acid derivatives have been reported to elicit anti-viral activities. Amano et al. and his research team synthesized seventeen cinnamic acid derivatives and screened them to identify an effective anti-viral compound against the hepatitis C virus. Among the 17 selected compounds, compound 6 was found to suppress the viral replication of genotypes 1b, 2a, 3a, and 4a with an EC50 value of 1.5–8 µM and SI values of 16.2–94.2. Compound 6 is also phosphorylated f Tyr705 in signal transducer and activator of transcription 3 (STAT3). Compound 6 suppressed anti-viral activity but did not inhibit JAK1/2-dependent phosphorylation of STAT3 Tyr705. Furthermore, results suggest that compound-6-dependent elevation of ROS is associated with the inhibition of HCV replication. The induction of oxidative stress by treatment with compound 6 may impair HCV replication by promoting lipid peroxidation. Thus, from the data obtained, the authors suggest that compound 6 significantly inhibits HCV replication via the induction of oxidative stress [35]. Based on the observation, we suppose that NPK and its active phytoconstituents such as apigenin and 2,4-dihydroxycinnamic acid can be developed as a significant anti-SARS CoV2 if further validated with detailed in vitro and in vivo [36].
The results showed that 2,4-dihydroxycinnamic acid from Solanum Xanthocarpum (Yellow nightshade) showed better binding affinities than Umifenovir in case of viral entry. Additionally, apigenin constituted within Solanum Xanthocarpum and Azadirachta indica (Neem) showed marked binding affinities more than Remdesivir toward blocking RNA-dependent RNA polymerase. In vitro SARS CoV2 assay confirmed that apigenin is more potent than 2,4-dihydroxycinnamic acid. Using phytoconstituents as an adjunct medication is another important question of herb–drug interactions requiring detailed experimental and clinical evidence. Another advantage with phytoconstituents is that the generation of adverse effects profiles is very low compared to other clinically available drugs.
The natural compound library of approximately 144 ligands constituted in Nimbamritadi Panchatiktam Kashayam (NPK) was taken from ZINC (https://zinc.docking.org) and IMPAAT (Indian Medicinal Plants, Phytochemicals and Therapeutics) (https://cb.imsc.res.in/imppat/home) databases for the best ideal ligands against the reference targets. NPK is a marketed ayurvedic formulation that belongs expectorant category with a wide range of pharmacological activities. Additionally, Ayurvedic physicians advise the usage of NPK for psoriasis and dermatitis. NPK is also known for its blood-purifying action. Anti-viral ingredients mainly constituted within NPK formulation include Azadirachta indica (neem), Tinospora cordifolia (amruta), Adhatoda zeylanica (vasaka), Trichosanthes dioica (pointed guard), Solanum Xanthocarpum (Kantakari), etc. (https://ayurvedapc.blog/2021/02/09). The literature-based survey was conducted to identify the constituents exhibiting the best anti-viral activity within Nimbamritadi Panchatiktam Kashayam. The 2D structures and chemical information of these phytoconstituents were obtained from PubChem, an open chemistry database at the National Institutes of Health (NIH) (https://pubchem.ncbi.nlm.nih.gov/) [37, 38]. These ligands' drug-likeness properties were evaluated using the Lipinski rule to confirm molecular characteristics. The docking studies of selected phytoconstituents against SARS CoV2 with desired protein were performed using the BIOVIA Discovery studio (client version 17.2.0.1.16347). The method includes the identification of active proteins involved in the pathogenesis of SARS CoV2 from RCSB, ligand identification, ligand preparation, and molecular docking study [39].
Estimation of drug-likeness features with Lipinski’s rule states that the characteristics like electronic distribution, lipophilic nature, size of the molecule, and flexibility and hydrogen bond properties have a more significant impact on the mechanistic nature of the molecule within a living organism, such as protein affinity and transport, reactivity and toxicity, and metabolic stability. Following Lipinski’s rule, a molecule is considered to possess drug-like activity or is predicted to be membrane-permeable and easily absorbed into the body; it should obey five criteria for drug-likeness mainly molecular weight, AlogP, hydrogen donor and acceptor, and several rotatable bonds [40].
Lipinski's rule analyzed ADMET properties such as absorption, distribution, metabolism, and excretion for the ligands that passed the drug-likeness properties. The AMDET properties are generally predicted by deeply analyzing physicochemical characteristics such as molecular weight (MW), polar surface area (PSA), lipophilicity (AlogP), Plasma protein binding (PPB), and aqueous solubility (logS) of selected compounds, closely related to certain features of drug molecules like absorption and bioavailability. ADMET studies are performed with ADMET Descriptors and Toxicity Prediction methods in Discovery Studio 2018. The information for establishing and validating these modules was obtained from various literature reports and experimental results [41].
Proteins employed for this study are mainly involved within the critical pathways possessing the mechanism of action of novel coronavirus SARS CoV 2. Experimentally illustrated 3D structures of the proteins like Spike RBD complexed with ACE2 and RNA-dependent polymerase (RdRp) were downloaded RCSB PDB (www.rcsb.org) [42, 43].
PDB IDs representing spike RBD complexed with ACE2 and RdRp were set and subjected to ProtParam for computing physical and chemical parameters of proteins entered in Swiss-Prot or TrEMBL like pI value, instability index, half-life period, aliphatic index, and GRAVY. SOPMA is indicated for secondary structure prediction, which identifies the properties like the percentage of different forms of protein, such as alpha-helix, beta-turn, random coil, and so on. Finally, PDB ID: 6LZG representing spike – ACE2 and 7BTF stands for RdRp was selected [43].
Proteins were prepared using the ‘prepare protein’ module implemented under the ‘Macromolecules’ module of the drug discovery studio. Water molecules and heteroatoms in the co-crystallized structure were removed, and the missing hydrogen atoms were added to the system. Protonation pH of 6.5 to 7.5 within the range of ionization was identified from the protein report. Possible tautomers and conformers for the proteins were predicted and the protein was thus prepared. Prediction of active sites in a protein is an essential strategy before docking. In this study, the accurate prediction of functional sites was done using Biovia Drug discovery studio visualizer 2020 [44].
Analysis of Drug likeness property by applying the Lipinski rule helped to filter all the enlisted phytoconstituents concerning factors similar to bioavailability by using a drug discovery studio. The selected ligands were subjected to evaluate ADMEtox properties (Absorption, Distribution, Metabolism, Excretion, and Toxicity parameters) with Biovia drug discovery studio and SWISS ADME http://www.swissadme.ch. Around 25 ligands that have passed the ADMEtox test were further estimated with molecular docking procedures. Filtered ligands and reference ligands, mainly chloroquine, hydroxychloroquine, and umifenovir were prepared by applying the designed ligands module from small molecule implemented in Biovia drug discovery studio [45].
The protein–ligand docking mechanism of the chosen protein–ligand complexes was performed using BIOVIA Discovery studio (client version 17.2.0.1.16347). We evaluated the interactions of crystal structures of spike RBD ACE2 complex and RdRp, respectively. The receptor structure was typed with the CHARMM force field before docking. Natural ligands, water molecules, and heteroatoms attached to selected proteins were removed as a part of the cleaning protein. Polar hydrogen atoms were added and subjected to the purification process before docking. The pH of the protein was adjusted to almost neutral 7.4 using a protein preparation module. Selected ligands were typed with a CHARMM force field and minimized by applying the smart minimizer minimization algorithm of Discovery Studio 2018, which constitutes about 2000 steps of steepest descent with an RMS gradient tolerance of 3 and conjugate gradient minimization. The active sites were determined from the PDB IDs 6LZG, and 7BTF, and the sphere radius was adjusted. CDOCKER module implemented under receptor–ligand interaction tool within DS is preferred for performing molecular docking. The prepared ligands were docked in the discovered pockets of the PDBs (6LZG and 7BTF) to evaluate the conformational flexibility during the refinement step and to predict CDOCKER ENERGY and CDOCKER INTERACTION ENERGY. In the study, each ligand was subjected to 10 runs in the docking software. Also, the active site of ligands was selected for the study, and in this site, we generated the grid box and further performed molecular docking analysis. The molecules with high docking scores were investigated to identify the molecules compared to the standard drugs hydroxychloroquine, chloroquine, and umifenovir [30, 46–48].
Chemicals: DMEM, FBS, and antibiotic solution (Gibco, USA), DMSO and MTT (Sigma, USA), 1X PBS (Himedia, India), 96-well tissue culture plates (Himedia, India). Cell line: African Green Monkey Kidney cells and Vero cells were purchased from NCCS, Pune. The Vero cells were cultured in DMEM supplemented with 10% FBS, 100 µg/ml penicillin, and 100 µg/ml streptomycin, and maintained under an atmosphere of 5% CO2 at 37 °C.
The phytoconstituents selected from the molecular docking studies namely, 2,4-dihydroxycinnamic acid and apigenin were subjected to in vitro cell proliferation assay followed by trypsinization and pooling of cells into a 15-ml test tube. The cells were seeded [cell density: 1 × 105 cells/ml cells/well (200 µL)] into 96-well plates containing DMEM medium with 10% FBS and 1% antibiotic solution for 24–48 h at 37 °C. The wells were washed with sterile PBS and treated with various concentrations of the samples in a serum-free DMEM medium. Individually, the samples were replicated three times and the cells containing well plates were incubated at 37 °C using a 5% CO2 incubator (24 h). Followed by incubation, MTT (20 µL of 5 mg/ml) was added to the individual well plate. The cells containing well plates were again incubated for another 2 to 4 h until purple-colored precipitates were visible under an inverted microscope. At last, the medium along with MTT (220µL) was detached from the wells and washed with 1X PBS (200 µL). In addition, the formazan crystals in the wash well plate were dissolved by adding DMSO (100µL) and were agitated for 5 min. The absorbance for each well was measured at 570 nm using a microplate reader (Thermo Fisher Scientific, USA), and the percentage cell viability and IC50 value were calculated using Graph Pad Prism 6.0 software (USA) [32].
The assay is based on the lentiviral backbone expressing ZsGreen as a traceable marker. We have utilized stable HEK293T expressing human ACE2 as the SARS permissive cells. The procedure involves transfection of HEK Lenti Cells (Invitrogen) with the expression vector encoding ZsGreen, a plasmid expressing spike, and plasmids expressing the minimal set of lentiviral proteins necessary to assemble viral particles (Gag/Pol, Rev). The cells were transfected with the expression vectors prepared via Quiagen Midi prep using lipofectamine 2000 as per the manufacturer’s instruction. After 6 h, the cells were replaced with fresh medium containing serum. SARS CoV2- spike- pseudotype lentiviral particles from the transfected cells were collected at 48 h, filtered using a 0.45-micron filter, and infecting the HEK293T–hACE2 cells using polybrene as per the standard [36, 49].
Below is the link to the electronic supplementary material.Supplementary file1 (DOC 1518 kb) |
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PMC9649012 | Pengtao Bao,Yang Liu,Xiaoai Zhang,Hang Fan,Jie Zhao,Mi Mu,Haiyang Li,Yanhe Wang,Honghan Ge,Shuang Li,Xin Yang,Qianqian Cui,Rui Chen,Liang Gao,Zhihua Sun,Lizhen Gao,Shuang Qiu,Xuchun Liu,Peter W. Horby,Xiubin Li,Liqun Fang,Wei Liu | Human infection with a reassortment avian influenza A H3N8 virus: an epidemiological investigation study | 10-11-2022 | Influenza virus,Viral infection,Viral epidemiology | A four-year-old boy developed recurrent fever and severe pneumonia in April, 2022. High-throughput sequencing revealed a reassortant avian influenza A-H3N8 virus (A/Henan/ZMD-22-2/2022(H3N8) with avian-origin HA and NA genes. The six internal genes were acquired from Eurasian lineage H9N2 viruses. Molecular substitutions analysis revealed the haemagglutin retained avian-like receptor binding specificity but that PB2 genes possessed sequence changes (E627K) associated with increased virulence and transmissibility in mammalian animal models. The patient developed respiratory failure, liver, renal, coagulation dysfunction and sepsis. Endotracheal intubation and extracorporeal membrane oxygenation were administered. H3N8 RNA was detected from nasopharyngeal swab of a dog, anal swab of a cat, and environmental samples collected in the patient’s house. The full-length HA sequences from the dog and cat were identical to the sequence from the patient. No influenza-like illness was developed and no H3N8 RNA was identified in family members. Serological testing revealed neutralizing antibody response against ZMD-22-2 virus in the patient and three family members. Our results suggest that a triple reassortant H3N8 caused severe human disease. There is some evidence of mammalian adaptation, possible via an intermediary mammalian species, but no evidence of person-to-person transmission. The potential threat from avian influenza viruses warrants continuous evaluation and mitigation. | Human infection with a reassortment avian influenza A H3N8 virus: an epidemiological investigation study
A four-year-old boy developed recurrent fever and severe pneumonia in April, 2022. High-throughput sequencing revealed a reassortant avian influenza A-H3N8 virus (A/Henan/ZMD-22-2/2022(H3N8) with avian-origin HA and NA genes. The six internal genes were acquired from Eurasian lineage H9N2 viruses. Molecular substitutions analysis revealed the haemagglutin retained avian-like receptor binding specificity but that PB2 genes possessed sequence changes (E627K) associated with increased virulence and transmissibility in mammalian animal models. The patient developed respiratory failure, liver, renal, coagulation dysfunction and sepsis. Endotracheal intubation and extracorporeal membrane oxygenation were administered. H3N8 RNA was detected from nasopharyngeal swab of a dog, anal swab of a cat, and environmental samples collected in the patient’s house. The full-length HA sequences from the dog and cat were identical to the sequence from the patient. No influenza-like illness was developed and no H3N8 RNA was identified in family members. Serological testing revealed neutralizing antibody response against ZMD-22-2 virus in the patient and three family members. Our results suggest that a triple reassortant H3N8 caused severe human disease. There is some evidence of mammalian adaptation, possible via an intermediary mammalian species, but no evidence of person-to-person transmission. The potential threat from avian influenza viruses warrants continuous evaluation and mitigation.
In recent years, many subtypes of avian influenza viruses (AIVs) have been found to be infectious to mammals and to pose a threat to the health of humans and other animals. So far, 11 subtypes of AIVs (mainly H5N1, H5N6, H6N1, H7N7, H7N9, H9N2, and H10N8) have been identified to cause human infections. While there are relatively frequent spillover infections (cases of single infections) with avian influenza virus in humans or other mammals, only a small proportion of cases have caused epidemics or pandemics in mammalian hosts. AIV infections in humans can result in a wide spectrum of illness, ranging from conjunctivitis and upper respiratory tract disease to pneumonia and multiorgan failure. Low pathogenic avian influenza A (H7N1, H7N2, H7N3, H9N2, or H10N7) virus infections have caused lower respiratory tract illness that is mild (conjunctivitis or uncomplicated influenza-like illness) to moderate in severity. Of great concern are the highly pathogenic avian influenza (HPAI) viruses. A (H7) viruses have resulted in conjunctivitis or uncomplicated influenza illness, with only a single fatal case with H7N7 virus infection during an outbreak in the Netherlands. In contrast, HPAI H5N1 and H7N9 virus infections have resulted in case fatality rates of approximately 53% and 34% respectively. The most ubiquitous hemagglutinin (HA) subtype of influenza A viruses is the H3, with a wide host range shown that includes humans, horses, dogs, cats, seals, poultry, pigs, and wild aquatic birds. An in vitro experiment also demonstrated its potential to establish successful infections in pigs. However, infection of humans with an H3N8 influenza A virus has not been previously reported. Here we report the human case of infection with H3N8 and describe the epidemiologic, clinical, and genetic features of the case.
The patient was a 4-year-old boy living with his father, grandparents, brother and sister, who are otherwise healthy, in Shangcai County, Zhumadian City of Henan province, China (Supplemental Fig. 1). On April 5, 2022, he developed a high fever of 39.3 °C (Fig. 1A), with lethargy and anorexia appearing on April 6, and cough was present on April 8. Although treated with antipyretic drugs and other supportive therapy, his condition did not improve and respiratory symptoms worsened. On April 10, day 6 of illness, he was admitted to a local hospital (Hospital A) for treatment. On admission, he had fever, shortness of breath, and severe respiratory signs and symptoms appeared. The patient was transferred to the intensive care unit in hospital B (Zhumadian Central Hospital, Zhumadian city of Henan). On admission, physical extermination revealed a body temperature of 39.5 °C, moist rales, and cyanosis of lips, accompanied by bilateral axillary lymphadenopathy. His blood pressure was 135/91 mmHg, tachycardic (pulse rate, 150/min), and transcutaneous oxygen saturation level was 80.0 % with mask oxygen inhalation. Blood gas analysis revealed arterial oxygen partial pressure of 45 mmHg. Blood tests showed leukopenia (2.63 109/L), hyperglycemia (blood sugar level 20.3 mmol/L), hyponatremia (129 mmol/L) and hypocalcemia (1.9 mmol/L) (Fig. 1B and Supplemental Fig. 2). There was decreased concentrations of total protein (50.2 g/L), albumin (30.9 g/L) and elevated concentrations of aspartate aminotransferase (AST, 198 U/L), and adenosine deaminase (ADA, 31.0 U/L) (Fig. 1B and Supplemental Fig. 2). Coagulation tests showed a significant prolongation of prothrombin time (PT, 23.9 s), activated partial thromboplastin time (APTT, >170 s), thrombin time (TT, >160 s), and elevated D-dimer (D-D, 5.11 ug/ml) and fibrin degradation products (FDP, 17.36 ug/ml), indicating a coagulation dysfunction (Fig. 1B). NT-proBNP (38.4 pg/ml) and troponin I (<0.012 ng/ml) were normal. High level of lactate dehydrogenase (LDH, 1935 U/L), creatine kinase (CK, 804 U/L) and creatine kinase BB (CK-BB, 55.0 U/L) were observed (Supplemental Fig. 2). A chest computed tomographic (CT) scan showed multiple patchy high-density shadows in both lungs, especially in the lower lobe of the right lung and the left lung (Fig. 2A–C). Antigen test for nine respiratory pathogens were all negative. The bronchoalveolar lavage fluid (BALF) was positive for influenza A by real-time RT-PCR, while negative for the other 13 commonly seen respiratory pathogens. The same sample was simultaneously subject to high throughput sequencing. Due to high fever of 41 °C, and exacerbation of respiratory symptoms, the patient was further transferred to a tertiary hospital in Zhengzhou City (Hospital C). Laboratory investigations revealed leukopenia, hyperglycemia, hyponatremia, liver dysfunction, renal damage, and coagulation dysfunction, which contributed to multiple organ failure and sepsis (Fig. 1). An inflammatory response characterized by elevated interleukin 6 (IL-6, 13.91 pg/mL), interferon-γ (IFN-γ, 45.55 pg/mL), IL-12P70 (5.63 pg/mL) was observed on day 8 after disease (Supplemental Fig. 2), indicating the development of systemic inflammatory response syndrome. The C-reactive protein (CRP) levels shows modest increases on 14-day hospitalization (11.84 mg/L), and peaked on 19-day hospitalization (16.4 mg/L) (Fig. 1B). The respiratory symptoms exacerbated with laboratory abnormalities including platelet, albumin, Na+ remained abnormally low, while γ-glutamyl transferase (GGT), lactate dehydrogenase (LDH), APTT, FDP, D-D, CK and blood urea nitrogen (BUN) elevated above normal (Fig. 1B, Supplemental Fig. 2). Pseudomonas aeruginosa was determined by metagenomic next-generation sequencing in BALF on day 8 of disease. The culture efforts however, yielded no positive result. Extensive treatment, including antiviral therapy (Oseltamivir 30 mg twice a day and interferon α1b 30 ug twice a day), antibiotic therapy (linezolid, meropenem, and compound sulfamethoxazole). After administration of mechanical ventilation via endotracheal intubation on day 6 of illness, the blood oxygen saturation values dropped to 55% and blood gas analysis revealed arterial oxygen partial pressure of 36 mmHg. The patient further received veno-arterial extracorporeal membrane oxygenation (VA-ECMO) on the same day (Supplemental Table 3). Chest radiographs showed patchy high-density shadows in both lungs on day 7 of illness (Fig. 2D). After treatment, the pulmonary shadows slightly diminished (Fig. 2E, F). The consolidation shadow in the left lung gradually diminished on day 16 (Fig. 2G–I) and day 31 of illness (Fig. 2J–L). His condition gradually improved, with ECMO withdrawn on May 3 and mechanical ventilation ceased on May 23. On June 7, the patient recovered and was discharged after 90-day hospitalization with no sequelae reported.
By high-throughput sequencing on the BALF collected on April 10, we obtained the whole genome sequence of an influenza A/H3N8 virus (A/Henan/ZMD-22-2/2022(H3N8); GenBank accession number ON342803-ON342810). Phylogenetic analysis revealed ZMD-22-2 as a reassortant H3N8 influenza virus which is distinct from previously reported H3N8 viruses. The most closely related HA gene was in the clade of H3N2 and H3N8 viruses detected in ducks from Guangdong Province (A/duck/Guangdong/F352/2018 (A/H3N2) determined in 2018 with a nucleotide homology of 96.09% (Fig. 3A). The highest similarity of NA segment was to H3N8 viruses in various species of birds in North America in 2014 and Japan in 2016 (A/northern pintail/Alaska/870/2014 (A/H3N8), with nucleotide homologies of 97.07% (Fig. 3B). The nucleotide homologies of internal genes were closely related to H9N2 viruses that had been identified throughout China and isolated from humans, ducks or wild birds in recent years (Supplemental Fig. 3). This indicates that the virus was a reassortant genotype which had undergone complicated mutation and reassortment events (Fig. 4). The amino acid sequence at cleavage site in HA protein is PEKQTR/GL, indicating that the virus was a low pathogenic avian influenza (LPAI) virus. Genetic features were evaluated based on a well-accepted correlation between receptor binding characteristics and host specificity of influenza viruses, i.e., viruses isolated from wild aquatic birds bind strongly to α-2,3 sialyl glycan (SA 2,3) receptor, in contrast, human-adapted influenza viruses recognize and bind SA 2,6 receptors, which predominate in the human respiratory tract. For the H3N8 virus, the residue 226 and 228 of HA gene were glutamine (Q) and glycine (G), respectively (Supplemental Table 4). The key mutation Q226L and G228S in HA protein which rendered a strong binding to SA 2,6 receptors and increased transmission ability by air, are absent, thus indicating limited ability to bind to cells in the human respiratory tract. The mutation of E627K was identified in PB2 gene. This mutation had been associated with increased virulence in mice and was reported to be associated with improved replication of avian influenza virus in mammals. Besides, the N30D and T215A mutation in M gene and P42S mutation in NS gene were also observed, which mutations were associated with increased virulence in animal models.
Epidemiological investigation revealed the family had been raising 6 chickens in the back yard, which however, had been killed before the investigation was performed. The patient had intensive contact with a domesticated dog and a cat kept in the household before his illness onset, by feeding and playing with the dog and the cat. Five close-contact family members (grandfather, grandmother, father, eight-year-old brother, and six-year-old sister) who lived together with the patient, and the aunt who had close contact with the patient while he was ill were interviewed. None of them had bred livestock, or reported recent visit to poultry market, the grandfather and brother reported infrequent contact to the dog and cat, although less frequently than the patient. No other significant exposures, i.e., visiting poultry market or exposure to febrile patients were reported. There was a pond 27-m distant from the household of the patient, which wild ducks inhabited, with frequent congregation with the domesticated poultry in the backyard (Supplemental Fig. 1). The molecular test on the 63 animal and environmental samples yielded 10 positive results for the H3N8, all from the patient’s household, including one nasopharyngeal swab of the dog; one anal swab of the cat; surface swabs of the boarding kennels of cat (1), dog (1), and chicken (1); drinking water (1) and food (1) of the dog; swab of the dining table (1), waste bin (1) and cabinet (1). No positive result was obtained in other 53 animal or environmental samples collected in the household (13) or the village (40). No obvious illness was observed for the animals, although the dog was less active than usual and labored breathing as recalled by the family. Biochemistry test revealed elevated level of creatine kinase (CK, 323 U/L), lactate dehydrogenase (LDH, 1.29 g/mL) of the dog, Decreased LDH (0.96 g/mL) in the cat (Supplemental Table 5). Nucleotide sequence of full-length HA from the dog and cat were identical to sequence from the patient. Detection for the H3N8, influenza virus (IFV) A, as well as 13 common respiratory pathogens were all negative for throat swabs collected from the 6 close contacts. The follow-up of the close contacts revealed influenza like illness developed until recently. Serological test on serum of the patient collected on April 10 revealed neutralizing antibody (Nab) titer of 1:121 against ZMD-22-2 virus by using a pseudotyped virus-based assay. Positive Nab was also determined from the grandparents of the patient (1:339 and 1:179, respectively) (Supplemental Table 6).
H3N8 virus is a common subtype of virus detected in wild and domestic ducks, and it has attracted a high attention because of its cross species transmission from avian to mammals. This represented the identification of human infection with H3N8, since its first identification in Florida in 1963. Although the current H3N8 subtype viruses could infect mammalian hosts, the whole genome sequence showed that it is still avian-like virus. The reassortant among different influenza viruses is considered the main mechanism for the emergence of novel virus, such as H5N1, H7N9, and H10N8 which can adapt to mammals and gain the ability to infect humans. In a likewise manner, a multiple reassortant from different origins was displayed for this H3N8 virus. Genetic evolution of the HA revealed a very close relationship with H3 in ducks that circulated in Guangdong, and the NA gene was derived from birds in both North America and Japan lineages. Still, a high similarity of internal gene with the widely circulating H9N2 in China was demonstrated. The pattern of the Chinese sub-lineage mixing with North America and Japanese influenza viruses remained obscure. It’s speculated that a frequent exchange of influenza virus might have occurred due to wild bird migration, or international poultry trade. The field investigation revealed a natural environment that was suitable for the congregating and contact among migratory birds with the local backyard poultry, which can promote the transmission of AIV. Geographically, the village was is located in the East Asian-Australian migratory birds’ flyway and it is one of potential stopovers for migratory birds (Supplemental Fig. 1), rendering a logical postulation of this mixture between wild birds and poultry. It’s notable that H3N8 virus was also detected from the domesticated cat and dog that were in close contact with the patient. Both dogs and cats are known to be susceptible to human influenza and avian influenza strains. Dogs are particularly susceptible to influenza A viruses, including H3N2, H3N8, H5N1, and H6N1.In Asia, respiratory disease caused by influenza virus H3N2 was documented in dogs, and fatal infection with the highly pathogenic avian influenza virus (HPAIV) H5N1 has also been reported. A number of single cases of H5N1 HPAI infections in cats have also been reported in different parts of the world, mainly associated with recent avian outbreaks. Here we observed the H3N8 infected dog developed mild clinical signs, moreover, laboratory abnormality of elevated LDH was observed, possibly indicating a systematic infection. The infected dog excreted virus not only via the respiratory tract but also possibly via the digestive tract as evidenced by positive detection of H3N8 specific RNA in the drinking water. Therefore, both the respiratory and gastrointestinal routes of infection may cause horizontal transmission among dog, cat and the human being. However, it is not possible to infer the direction of transmission, since both dogs and cats are naturally susceptible to influenza virus strains from other hosts, including birds and mammals. Under current situation, both cat and dog are semidomesticated and may highly likely come in contact with wild birds, ducks in the nearby pond, on the other hand, frequently exposed to human and poultry. Transmission of H3N8 virus into dog and cat further to human, or the otherwise manner can both occur. The potential ability of cat and dog to be a “mixing vessel” of diverse origin influenza strains into reassortant might be indicated. Unfortunately, the lack of poultry specimens from the household of the patient, and the unsuccessful sampling of the duck in the neighboring pond means we cannot firmly establish the original zoonotic source of infection nor determine the genetic sequence of the original virus. There was no evidence of infection in other family members and it is unlikely an outbreaks or epidemics could be induced, due to the biological and ecological barriers of the novel genotype. In line with the epidemiological findings, the genetic characterization revealed that ZMD-22-2 was low pathogenic. On the other hand, the presence of mutations, mainly E627K in PB2 gene and P42S in NS gene, indicated the capacity of this influenza A virus subtype to cross the species barrier, acquire some mammalian adaptations and cause human severe disease. The patient has developed severe respiratory distress syndrome, yet with no other known respiratory coinfection identified. Although we determined the presence of Pseudomonas aeruginosa by high-throughput sequencing, no bacterial culture was obtained, indicating its minor contribution to the overall disease. On the other hand, antibiotic therapy was started empirically on early hospitalization, aimed toward bacterial secondary infection that commonly occurs in the clinical course of severe pneumonia. For patients who present without risk factors or signs of bacterial infection, antibacterial use might be unnecessary. The study was subject to a major limitation that no virus isolation was performed due to the biosafety concern, thus the antibody test was performed on a pseudovirus system, and the cross reaction with other H3 influenza cannot be excluded. The biological features of the H3N8 need to be further investigated by experimental approaches. Moreover, based on only one case, our understanding of the clinical aspects of H3N8 infection is rather limited. A full understanding of the clinical spectrum might be proposed based on a large-scale population surveillance. As of preparing the manuscript, an equine influenza (EIV) outbreak caused by H3N8 virus was reported in America. Although not evolved from AIV, it might be transmitted from horses to dogs or birds. It is therefore important to strengthen the surveillance of H3N8 infections among various animal species, based on which a close monitoring of the viral evolution and timely identification of new human cases could be achieved.
In April 2022, as part of hospital surveillance of febrile patients in Zhumadian Central Hospital, Henan province, China, a febrile pediatric patient with recurrent fever of unknown cause was screened. Following the identification of infection with an avian influenza virus, an epidemiological investigation was performed on the patient and his close-contact family members and relatives using a standard questionnaire, which included demographic information, pre-existing underlying diseases and the exposure history before the onset of illnesses. To infer the possible infection source of the patient, we performed field investigation in the residence of the patient and the village on April 13. Nasopharyngeal swabs, anal swabs, faecal and blood samples were collected from companion animals, livestock and poultry. The collection of specimens for companion animals, livestock and poultry were conducted by a veterinarian. The environmental samples that included the surface swabs, drinking water, sewage, and water in a pool close to the family were also collected. Throat swabs and blood samples were collected from close contacts of the patient, who were also asked to report for fever (≥38 °C) and influenza-like symptoms for seven days since the interview. All infectious materials were handled in a BSL-2 facility under approved protocols according to Beijing Institute of Microbiology and Epidemiology guidelines. According to the regulations and guidelines of the NHFPC of China, data collection on this patient was part of the routine surveillance and outbreak investigation, and was therefore exempt from the oversight by institutional review board. The patient’s parents gave informed consent. The family members of the patient and all the participating subjects signed consent forms approving the investigation, sample collection and its publication. The procedures were in accordance with the Helsinki declaration of 1975, as revised in 1983.
The RNA from BALF of the patient were subject to high-throughput sequencing. Briefly, the Viral RNA was extracted using AllPrep DNA/RNA mini kit (Qiagen, Germany) and sequenced using Illumina Nextseq 550 platform. Sequenced data was assembled with the reference sequences in database using CLC Genomic Workbench v21. The sequence alignment and annotation were performed using CLC Genomic Workbench v21.
All the collected samples were tested for 14 respiratory pathogens, including IFV A, IFV B, seasonal influenza H3N2 virus, seasonal influenza H1N1 virus, 2009 pandemic influenza A (pH1N1) virus, H7N9 virus, H5N1 virus, respiratory syncytial virus (RSV), human rhinovirus (HRV), human parainfluenza virus (HPIV), human adenovirus (HAdV), human coronavirus (HCoV), human bocavirus (HBoV), and human metapneumovirus (HMPV), by real-time PCR/Reverse Transcriptase-PCR (RT-PCR) (Supplementary Table 1). Briefly, the nucleic acid was extracted using QIAamp Viral RNA Mini Kit (Qiagen, Germany). For all IFV positive samples, a pair of universal full-length primers (MBTuni-12 and MBTuni-13) were used to reverse transcribe and amplify all eight segments of the virus genome. Specific test for the H3N8 was performed by real-time RT-PCR (Supplementary Table 2). Antigen test for nine respiratory pathogens (legionella pneumophila, mycoplasma pneumoniae, Coxiella burnetii, chlamydia pneumoniae, HAdV, RSV, IFV-A, IFV- B, and HPIV) was also performed by using commercial indirect immunofluorescent kit (Vircell SL, Spain) (Supplementary Table 1).
The level of 7 cytokines, IL-4, IL-6, IL-10, IL-17, IL-12p70, tumor necrosis factor-α (TNF-α), and interferon-gamma (IFN-γ), were measured by multiple microsphere flow immunofluorescence methods using the commercial kit (Qingdao Raisecare Biotechnology Co., Ltd, Shandong, China).
The reference nucleotide sequences were downloaded for phylogenetic analysis. Phylogenetic trees were reconstructed based on maximum likelihood method with nucleotide sequences using IQ-TREE (v1.6.12) with GTR + G as the best-fit substitution model and 1000 bootstrap replicates. MAFFT method was used for sequence alignment.
The H3N8 pseudovirus was generated, based on which the pseudotyped virus neutralization assay was measured by the reduction in Luc gene expression. Briefly, serially diluted samples were incubated with pseudotyped virus in duplicate for 1 h at 37 °C together with the virus control and cell control wells in sextuplicate. Thereafter, 2 × 104 freshly trypsinized Huh7 cells (obtained from Japanese Collection of Research Bioresources) were added to each well of the 96-well plate. After 48 h of incubation at 37 °C with 5% CO2, the RLU was measured in accordance with the instruction manual provided by PerkinElmer (Waltham, MA). The ED50 (median effective dilution) was calculated using the Reed-Muench method.
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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PMC9649024 | 36407558 | Rakesh Pandey,Dayanandan Mani,Karuna Shanker,Dnyaneshwar Umrao Bawankule,Debabrata Chanda,Raj Kishori Lal,Anirban Pal,Puja Khare,Narendra Kumar,Sudeep Tandon,Dharmendra Saikia,Anil Kumar Gupta,Ramesh Kumar Srivastava,Sanjay Kumar,Ram Suresh,Saudan Singh,Alok Kalra,Anil Maurya,Dewasya Pratap Singh,Taruna Pandey,Shalini Trivedi,Shachi Suchi Smita,Akanksha Pant,Laxmi Rathor,Jyotsna Asthana,Mashu Trivedi,Prabodh Kumar Trivedi | Towards the development of phytoextract based healthy ageing cognitive booster formulation, explored through Caenorhabditis elegans model | 10-11-2022 | Caenorhabditis elegans,Mus musculus,Cymbopogon khasianus,Ocimum tenuiflorum,Camellia sinensis,Phyto-extract,Longevity,Cognition,HACBF (healthy ageing cognitive booster formulation) | The positive effect of herbal supplements on aging and age-related disorders has led to the evolution of natural curatives for remedial neurodegenerative diseases in humans. The advancement in aging is exceedingly linked to oxidative stress. Enhanced oxidative stress interrupts health of humans in various ways, necessitating to find stress alleviating herbal resources. Currently, minimal scientifically validated health and cognitive booster resources are available. Therefore, we explored the impact of plant extracts in different combinations on oxidative stress, life span and cognition using the multicellular transgenic humanized C. elegans, and further validated the same in Mus musculus, besides testing their safety and toxicity. In our investigations, the final product—the HACBF (healthy ageing cognitive booster formulation) thus developed was found to reduce major aging biomarkers like lipofuscin, protein carbonyl, lipid levels and enhanced activity of antioxidant enzymes. Further confirmation was done using transgenic worms and RT-PCR. The cognitive boosting activities analyzed in C. elegans and M. musculus model system were found to be at par with donepezil and L-dopa, the two drugs which are commonly used to treat Parkinson’s and Alzheimer’s diseases. In the transgenic C. elegans model system, the HACBF exhibited reduced aggregation of misfolded disease proteins α-synuclein and increased the health of nicotinic acetylcholine receptor, levels of Acetylcholine and Dopamine contents respectively, the major neurotransmitters responsible for memory, language, learning behavior and movement. Molecular studies clearly indicate that HACBF upregulated major genes responsible for healthy aging and cognitive booster activities in C. elegans and as well as in M. musculus. As such, the present herbal product thus developed may be quite useful for healthy aging and cognitive boosting activities, and more so during this covid-19 pandemic. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s13237-022-00407-1. | Towards the development of phytoextract based healthy ageing cognitive booster formulation, explored through Caenorhabditis elegans model
The positive effect of herbal supplements on aging and age-related disorders has led to the evolution of natural curatives for remedial neurodegenerative diseases in humans. The advancement in aging is exceedingly linked to oxidative stress. Enhanced oxidative stress interrupts health of humans in various ways, necessitating to find stress alleviating herbal resources. Currently, minimal scientifically validated health and cognitive booster resources are available. Therefore, we explored the impact of plant extracts in different combinations on oxidative stress, life span and cognition using the multicellular transgenic humanized C. elegans, and further validated the same in Mus musculus, besides testing their safety and toxicity. In our investigations, the final product—the HACBF (healthy ageing cognitive booster formulation) thus developed was found to reduce major aging biomarkers like lipofuscin, protein carbonyl, lipid levels and enhanced activity of antioxidant enzymes. Further confirmation was done using transgenic worms and RT-PCR. The cognitive boosting activities analyzed in C. elegans and M. musculus model system were found to be at par with donepezil and L-dopa, the two drugs which are commonly used to treat Parkinson’s and Alzheimer’s diseases. In the transgenic C. elegans model system, the HACBF exhibited reduced aggregation of misfolded disease proteins α-synuclein and increased the health of nicotinic acetylcholine receptor, levels of Acetylcholine and Dopamine contents respectively, the major neurotransmitters responsible for memory, language, learning behavior and movement. Molecular studies clearly indicate that HACBF upregulated major genes responsible for healthy aging and cognitive booster activities in C. elegans and as well as in M. musculus. As such, the present herbal product thus developed may be quite useful for healthy aging and cognitive boosting activities, and more so during this covid-19 pandemic.
The online version contains supplementary material available at 10.1007/s13237-022-00407-1.
With an exponential increase in elderly population world over, the incidences of age-related neurodegenerative diseases especially AD and PD are also growing very fast. These diseases cause progressive devastating cognitive, behavioral, memory and motor dysfunction. In recent years, there has been an incredible interest in research on medicinal plants to find an effective cure for neurodegenerative diseases. Maximum ROS is generated due to human body’s subjection to various kinds of pollutants and toxic materials. The latter contributes a lot to the mitochondrial dysfunctions and is often associated with misfolded proteins such as α-synuclein and amyloid-β [33, 34, 47, 50, 52, 53]. Since aging is the major contributing factor for the occurrence of various kinds of human diseases, it presents a societal burden on the younger generation and financial burden on health care system. In the USA, it has been estimated that reducing the commencement of age-related disorders by 2 years would save $ 7.1 trillion over the next fifty years [17]. During aging, the changes occurring in structure and function of body organs significantly reduces the metabolic rate, motor activity and cellular damage. For achieving healthy lifespan, the focal point is to discuss the biochemical and cellular changes happening during aging [10, 19, 32]. However, it is not feasible to observe age dependent changes in higher organisms and hence the multicellular organism C. elegans has proved to be one of the valuable organisms for developing healthy aging and cognitive booster resources for elderly as well as younger population [43, 45]. In view of the aforementioned factors about aging and cognition, it was planned to investigate the prospect of plant extracts of selected plant species for their antioxidant, stress tolerance, antiaging and cognitive booster activities, especially against Parkinson and Alzheimer’s by observing various indicative biomarkers in Caenorhabditis elegans.
In the present investigation the major approach has been to study the prospect of various medicinal plant extracts (prepared as per AYUSH guidelines of the Government of India) on healthy aging and cognition using multicellular model C. elegans. Further some experiments related to safety, toxicity, cognition and inflammation have also been performed involving Mus musculus. Beginning with initial screening of combination of twelve prospective plants reported in traditional literature for health promoting properties, we subsequently pinned down to test and examine the combinations of 3 plants viz. leaves of cv Suvarna of Cymbopogon khasianus (Hack.) Stapf ex Bor, herb of cv CIM-Agna of Ocimum tenuiflorum L. and leaves of Camellia sinensis (L.) Kuntze, in different ratios.
For metal analysis, after overnight pre-digestion, acidified samples were digested in a microwave digester (Anton Paar Multiwave Pro oven) at 120 °C for 41 min. The samples were filtered. The metal in digested samples was determined by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES, Perkin Elmer Optima 5300 V).
Various C. elegans strains were grown on nematode growth medium (NGM) and spotted with E. coli OP50 as a food source [9]. E. coli OP50 is an uracil auxotroph strain, used to limit the growth of bacteria on NGM. Nutrient food for bacteria i.e., minimal essential medium (MEM) was prepared by using standard protocol [49]. All the strains of C. elegans utilized in the current study were made available by Caenorhabditis Genetic Center (CGC), University of Minnesota (www.cgc.cbs.umn.edu). Sigma and Merck companies provided the chemicals used in the present study. The worm strains utilized in the present study were wild type Bristol N2, transgenic strain NL5901 (Punc-54::α-synuclein::YFP + unc-119), CL2006 (unc:54, Aβ1-42), CL4176 {dvIs27 [myo-3/ Aβ 1–42 minigene + rol-6(su1006)]}, CF1553 (sod-3::gfp), CL2166 (gst-4::gfp).
Alkaline hypochlorite treatment method has been primarily used to produce age synchronized population of the worms for the different experiments [16] conducted in this study.
Toxicity determination assays are imperative as only non-toxic extracts can be used for the various experiments, including aging and cognition. Acute toxicity assay was carried out using 1–5 mg/ml concentration in C. elegans. The worms were synchronized using the standard protocol [31]. The synchronized L1 worms were put into fresh NGM plates and the worms were grown upto L4 stage at 20 °C. Different concentrations (1–5 mg/ml) of HABCF were prepared in double distilled water and 20 worms were transferred to 24 well plates containing relevant concentration of HABCF in each of the test well. The assay was performed in triplicate. Touch provoked movement method was used to observe the survival of the worms [24]. The toxicity was analyzed using the formula (live worms/ total number of worms) × 100. SPSS software has been used to calculate the statistical significance using one-way ANOVA, followed by Scheffe and Dunnett tests.
Acute and sub-acute oral toxicity of HABCF was carried out in Swiss albino mice for defining the safety limit in the rodent model for its further development. The experiment was carried out in accordance with the approved protocol and ethical guidelines of the institute.
Acute oral toxicity was carried out following the methodologies published earlier (11). HABCF was suspended in double distilled water (DDW) and was given orally at 5, 50, 300, 1000 and 2000 mg/kg body weight as a single oral dose. Control animals received only vehicle.
In the sub-acute oral toxicity of HABCF, fractional doses of maximum tolerated doses from acute oral toxicity (2000 mg/kg) was considered and the animals were treated with the HABCF at 0.2, 2, 20 and 200 mg/kg once orally for 28 days following the methodologies previously published by us [11].
The animals were checked for mortality and morbidity at hourly interval on the day of administration of HABCF and a daily general examination of the animals was carried out for changes in skin, mucous membrane, eyes, occurrence of secretion and excretion and also responses like lachrymation, piloerection respiratory patterns etc. Further changes in gait, posture and response to handling were examined [5]. Body weights were recorded and blood samples were collected from all the animals on 7th day of the experiment in acute oral toxicity and were examined for serum biochemistry and hematology. Further, in the sub-acute oral toxicity experiment, animals were also examined for the observational changes as well as hematological and biochemical changes as stated in acute oral toxicity and blood samples were collected on 28th day of the experiment and analyzed for hematological and biochemical changes. The experimental animals were sacrificed at the end of experiment (7th day in acute toxicity and 28th day in sub acute toxicity) and vital organs were collected, and necropsied for any gross pathological changes. Weights of vital organs like liver, heart, kidney etc. were recorded [11]. Liver and Kidney samples were collected from animals of sub-acute oral toxicity and fixed in 10% buffered formalin and processed for histomorphological analysis [46].
In order to start an acute inflammation, 100 μl of saline containing 1% w/v carrageenan was injected into the sub-plantar injection of the left hind footpad of Charles Foster rats (4 rats/group). The same amount of vehicle was administered to the rats in the vehicle-treated group at the same time. A water Plethysmometer was used to calculate the paw volumes. Each left footpad's volume was measured before to the carrageenan injection, and three hours later, the swelling of the footpads was measured. The HABCF (50,100, and 200 mg/kg) was given orally to the rats for three days in a row prior to the injection of carrageenan, and Diclofenac (15 mg/kg), a standard anti-inflammatory medicine, was given 30 min before the injection of carrageenan to examine the effect of the HACBF administered orally. After 3 h of carrageenan injection, the sub-plantar region of the left hind paw showed the highest inflammation. The percentage inhibition of edema was calculated as follows:where Vo = Volume (ml) of paw in vehicle treated rats (Carrageenan + Vehicle), Vt = Volume (ml) of paw in treatment group rats (Carrageenan + Treatment).
To make the stock solution test plant extracts and HABCF were suspended in sterilized DW (distilled water). The stock solution was diluted to prepare for required concentrations for antioxidant assays. The reagents used in the assay were prepared freshly before experiment. To study the various antioxidants the stable free radical, 1, 1-Diphenyl-2-picrylhydrazyl (DPPH) (purple color, λmax 515–517 nm) was used [55]. According to previous studies alteration in the absorbance of DPPH after adding test compound is used as an index of the antioxidant capacity of the compound. The antioxidant activity of the HABCF was quantified according to the previously known method by DPPH assay [1]. Briefly, 0.5 mM of DPPH (freshly prepared) and 0.1 M Tris buffer pH (7.4) were added to 100 μl of the sample (1.5 ml eppendorf tube) at various concentrations (1–10 mg/ml) of HABCF. The reaction combination was incubated in dark for 45 min at 37 °C temperature in an incubator followed by spectrophotometric quantification at 517 nm against control. The scavenging activity was computed using the following equation [57]where Ac = absorbance of control; At = absorbance of the sample.
To evaluate the effect of resource extract for oxidative stress assay in worms, juglone (5-Hydroxy-1, 4-napthoquinone, Sigma-Aldrich), an intracellular ROS generator was applied. The age synchronized N2 wild type worms were raised from L4 to adult stage in presence/ absence of different doses of HABCF. The treated and untreated day 4 adult C. elegans was exposed to 24-well plates containing liquid NGM. Further, juglone (250 μM) was used in a total volume of 300 μL per well for inducing oxidative stress. The worms were placed in a incubator at 20 °C and scored for their viability after 24 h of continuous exposure [51].
The established fluorescent H2DCF-DA (Molecular Probes, U.S.A) (2′,7′-dichlorodihydrofluorescein–diacetate) probe was utilized for determination of in vivo ROS level in worms. Following 72 h of different concentrations of formulation treated and control, day 4 wild-type worms were washed thrice using M9 buffer to wash off the extra E. coli OP50 and any residual extract. Then the washed worms were suspended in 1.5 ml micro-centrifuge tubes filled with 300 μl of 0.1% PBST buffer (PBS with Tween 20). Approximately, 50 C. elegans were shifted into Corning® 96 Well Black Flat Bottom Polystyrene NBS™ Micro plate, and 1.5 μl of 10 mM H2DCF-DA dye was dropped to each well before taking observation. Spectra Max M2 multimode micro plate reader, (Molecular Devices) was used to quantify the fluorescence at 485 nm excitation and 530 nm emission. The change in fluorescence was recorded for 120 min at 20 min intervals at 37 °C [48]. The assays were performed in triplicate. GraphPad Prism software was used to calculate the statistical significance using independent t-test.
Exposure of same age L4 worms was done to varied concentrations of HABCF at 20 °C on NGM plates spotted with E. coli. 50 μM 5-Fluoro- 2′deoxyuridine (FUdR) and was mixed to the NGM plates for inhibiting growth of the progeny. For avoiding contamination, the worms were moved to new OP50 seeded NGM plates every 3–4 days. The survival was assessed each day until the death of the last worm. The experiments were replicated thrice and the results showed as the mean life span per trial.
The wild type N2 worms were raised from L4 larvae as in the lifespan assays and on 5th day of adulthood, fluorescent imaging was performed for lipofuscin and other GFP specific strains treated with an effective dose (4 mg/ml) of HABCF. The intestinal autofluorescence of lipofuscin and GFP fluorescence was measured in randomly selected worms (n = 30) from each set of experiments. Worms were mounted onto microscope slides coated with 3% agarose pads and 2% sodium azide was added to anesthetize for visualization of fluorescence. Images were captured with a fluorescence microscope (Leica, DMI3000); levels were quantified by determining the average pixel intensity in each worm using Image-J software (NIH).
For sample preparation age synchronized embryos were added to treatment plates and incubated at 22 °C for 48 h. After the 48-h incubation period, the worms were washed thrice using M9 buffer and sonicated in cell lysis buffer for 3 min at 30 percent amplitude with pulse on/off for 2 s. The sonicated worms were centrifuged for 7000 rpm for 7 min at 4 °C to remove cell debris. Supernatant was collected and stored at temperature -20 °C and protein estimation was done using Bradford method [8].
The effect of HABCF treatment on lipid levels of worms was measured using Nile red (a fluorescent dye used to stain intracellular lipid droplets) staining [6]. A 5 mg/ml stock solution of Nile red (9-diethylamino-5-benzo[α] phenoxazinone) was prepared in acetone, further diluted with E. coli OP50 in a ratio of 1:250 and spotted onto NGM plates along with or without treatment. Thereafter, age synchronized L1 worms were transferred to treatment plates and incubated at 20 °C. After 72 h, the worms were washed off the plates using M9 buffer. Worms were anaesthetized using 100 mM sodium azide, mounted onto slides and were observed using rhodamine filter. The fluorescence intensity was calculated semi-quantitatively using Image J.
The age synchronized day 2 adult transgenic worms carrying inducible green fluorescence protein (GFP) specific strains were treated with an effective dose of HABCF (4 mg/ml). Reporter transgene viz sod-3 (CF1553), gst-4 (CL2166), were treated with HABCF and control in these two strains. The GFP fluorescence was captured using randomly selected 20–30 adult worms mounted on 3% agarose pads and anesthetized by 2% sodium azide [7]. GFP expression was quantified at the site of its expression which varied from strain to strain. Images were captured using a fluorescence microscope (Leica, DMI3000) in GFP filter (with excitation 365 nm and emission 420 nm) at the 20 × objective [38]. Quantification of data was performed by using Image J software. The experiment was done thrice.
Aldicarb sensitivity assay is an indirect assay to check the relative effect on neurotransmission. Aldicarb is a carbamate insecticide which acts as an acetylcholinesterase inhibitor. Aldicarb assay was performed as per previously described protocol [26].
Levamisole is an anti-helminthic compound which works as a nicotinic acetylcholine receptor agonist that causes continued stimulation of the parasitic worm muscles, leading to paralysis. Levamisole assay was done as per described method [36] with slight modifications.
The dopamine levels in worms were studied by an indirect repulsion assay through 1-nonanol. Worms raised from the L1 stage along with HABCF (4 mg/ml) treated/control worms were washed thrice at day 5 by using M9 buffer. The washed nematodes were exposed to 1-nonanol by placing a drop of 1-nonanol by using poking lashes near head region. The alteration in repulsion time was observed in both wild type and diseased worms and data was plotted against repulsion time ± SEM. The experiment was performed in three biological replicates.
To investigate the health of motor neurons, body bend assay was performed. Treated with HABCF (4 mg/ml) and control hermaphrodite day 5 worms were used to study the neural behavior. Approximately 10 worms were transferred in a drop of M9 buffer on a glass slide and the body movement was observed (S-shaped curved movement represent 1 bend) for 20 s. The data of HABCF/Control was plotted against total bends per 20 s. The experiment was performed in three biological replicates.
For the examination of locomotion and movement alteration in worms, head thrash assay was performed. HABCF (4 mg/ml) treated and control worms were transferred (n = 10) on a glass side with 20 µl M9, observed and the to and fro motion in the head region of worms was recorded one by one for 20 s. The data was analyzed and compared for the movement of treated/control worms by using GraphPad Prism software. The experiment was repeated thrice independently.
This experiment was conducted to study the impact of HABCF on the aggregation of α-synuclein protein under a fluorescence microscope. For the quantification of α-syn aggregation levels, transgenic strain NL5901 (Punc-54::α-synuclein:: yellow fluorescence units (YFP); unc-119) was used by previously described method [21, 54]. Briefly, the synchronized eggs were transferred on different concentrations of HABCF (1, 2, 3, 4, 5 mg/ml) and control plates and incubated at 20 °C till the worms reached to adult day 5 stage. Randomly picked healthy day 5 adult hermaphrodite worms (n = 30) were placed on glass slides having 20 µl M9 along with 4 µl of 100 mM NaN3 and mounted under the cover slip. For the observation of α-syn aggregation, images were captured under fluorescence microscope with excitation at 488 nm and emission at 530 nm at 20 × . The fluorescence intensity was calculated semi-quantitatively using Image J and represented in terms of normalized values of Corrected Total Cell Fluorescence (CTCF) [CTCF = Integrated Density − (Area of selected cell × Mean fluorescence of background readings)].
Total RNA was extracted from adult worms using RNAzol reagent (Molecular Research Centre) according to the manufacturer’s protocol.
cDNA synthesis was carried out from 1 µg of total C. elegans RNA in a 96 well thermal cycler using High-Capacity cDNA synthesis Kit (Thermo Scientific) according to manufacturer’s protocol. cDNA samples were quantified using nanodrop spectrophotometer and stored at -80 °C.
qRT-PCR studies were done using SYBR Green (Puregene, Genetics Asia, catalogue no. Pgk022) technology. Briefly, 10 µl total reaction mixture was prepared by mixing cDNA template (125 ng), RNAase free water, forward and reverse primer and SYBR green dye. gpd-1 was used as internal control. Applied Biosystems 79,000 HT was used to perform the RT-PCR studies using the program of pre incubation cycle of 50 °C for 2 min and 95 °C for 10 min followed by 35 amplification cycles for 15 s and 58 °C for 30 s and final extension at 72 °C for 20 s. Relative expressions were calculated by 2−∆∆CT method. Primers were procured from Integrated DNA technologies.
Statistical analysis and graphical representation of data were carried out using Graph Pad Prism Version 5. Analysis of variance and independent t-test was used to calculate statistical significance wherever applicable. For survival assays long rank analysis were performed using Kaplan–Meier survival analysis. The level of statistical significance was ascribed at p < 0.05.
The analysis of extract was done for examining the occurrence of active molecules and purity of plant extracts. Blank and certified reference material were included in each batch of samples for quality control. Diverse class of secondary metabolites viz. caffeic acid, rutin, and rosmarinic acid in Ocimum tenuiflorum cv Agna; citral, gallic acid, quercetin, and syringic acids in C khasianus cv Suvarna; and catechins epigallocatechin gallate, epigallocatechin, epicatechingallate, epicatechin, catechin) and caffeine in C. sinensis are reported in the ingredient herbs of the present decoction formulations. The content of key phytochemicals viz. caffeic acid (7.20 mg/100 g), rosmarinic acid (30.22 mg/100 g), quercetin (0.25 mg/100 g), epicatechin (21.33 mg/100 g), catechin(1.70 mg/100 g) were quantified in the decoction formulations using external standard HPLC method (Fig. 1).
The concentrations of heavy metals (mg/kg dry weight) were found as follows: Al 0.5, Ca 0.2, Cd 0.027, Cu4.125, Fe 0.7, Mg 0.775, Mn1.0, Zn 0.15, which are below the permissible limit. The concentrations of Cr, Co, Pb and Ni were not detected in HABCF. The concentrations of all other metals in the HABCF samples were within accepted limits set for herbal extract according to international regulatory bodies (Fig. 2).
During the experimentation no genotoxicity has been detected in the HABCF up to 10 mg/ml concentration (full data is Supplementary Table 1).
The HACBF at 1, 2, 3, 4 mg/ml was found non-toxic whereas at 5 mg/ml showed significant toxicity (Fig. 3). Therefore 1, 2, 3, 4 mg/ml were selected as experimental concentrations for further studies. C.elegans were exposed to 0.05% DMSO that served as a vehicle control. The experiment was independently replicated thrice. No observable toxicity was recorded throughout the experimental period in all the groups of animals up to the tested dose levels of 2000 mg/kg of HACBF including control in acute oral toxicity and up to 200 mg/kg in sub acute oral toxicity. Haematology and serum biochemistry showed non-significant changes in all the haematological and biochemical parameters like haemoglobin level, total RBC, WBC count, differential leucocyte counts, ALP, SGPT, SGOT, total cholesterol, triglycerides, creatinine, bilirubin, albumin and serum protein levels (Supplementary Table 2 and Fig. 4a) except significant increase in serum albumin level in animals treated with HACBF at 1000 and 2000 mg/kg. Similarly, animals on gross pathological study showed no significant changes in any of the organ weight pertaining to their absolute and relative weight (Fig. 4b, c) in acute oral toxicity. Similarly, the lyophilized HACBF in sub-acute oral toxicity at 0.2, 2, 20 and 200 mg/kg once daily for 28 days did not produce any observational changes, morbidity or mortality during the experimental period. Haematological, biochemical and gross pathological studies also showed nonsignificant changes among all the doses studied (Supplementary Table 3 and Fig. 4d–f). Liver and kidney tissues from the animals of all groups in the sub-acute oral toxicity experiment were saved for histopathological studies. The sections of liver and kidney from all the groups including control showed no observable changes and are presented in Supplementary Fig. 1. The present acute and sub-acute oral toxicity study in Swiss albino mice suggests that HACBF is well tolerated by the experimental mice up to 2000 mg/kg in acute oral toxicity and up to 200 mg/kg body weight in sub-acute oral toxicity when dosed daily for 28 days.
Oral treatment of HACBF (200 mg/kg) has shown significant inhibition of carrageenan-induced paw edema inflammation in rats when compared to vehicle-treated rats (Fig. 5). The persistent rise in inflammatory marker concentration in the blood is a characteristic of aging, which has become a major public health problem with a large socioeconomic component globally. Chronic low-grade inflammation is regarded to be a major contributor to many age-related diseases.
Aggregation of free radicals is associated with many health problems. Reactive oxygen species (ROS) play a key part in various biological manifestations [10, 14]. Antioxidants are the defense machinery of a cellular system to protect them by scavenging the ROS. Plant-derived active molecules are rich antioxidants that are able to scavenge the free radicals[32, 45]. The antioxidant potential of HACBF was examined by colorimetric method using DPPH as a colouring reagent. Different concentrations of HACBF ranging from 1 to 10 mg/ml were selected for the assay. The results exhibited a decrease in absorbance of DPPH in as a dose dependent manner with the increasing concentration of HACBF (Fig. 6). This result suggested that HACBF possesses potent free radical scavenging effect on DPPH. Therefore, the results revealed that HACBF possess a significant antioxidant potential.
ROS is generally produced by various pollutants especially heavy metal ions, radiation, UV etc. During studies it was found that ROS play a major role in the intracellular signaling process [28, 35, 53]. The increased oxidative stress level with raise in intracellular ROS result in cellular damage which has a significant part in the pathology of several elderly diseases [58]. The additional retention of ROS inside the cells can be related to the toxicity in wild-type worms during thermal threat. Therefore, to evaluate the correlation between HACBF mediated enhanced survival and intracellular ROS level on the wild-type C. elegans at the 2nd day of lifespan, the intracellular ROS level was quantified employing DCF-DA method using live worms. The pre-treatment with different doses of HACBF reduced in vivo ROS level (Fig. 7a), indicating HACBF might postpone ROS production during the aging process and subsequently a decline in intracellular ROS could lead to extension in lifespan. Maximum reduction of ROS was observed at 4 mg/ml concentration.Similarly, maximum survivality was observed in juglone assay at different doses of HACBF (Fig. 7d). Present studies indicate that HACBF brought tolerance in C. elegans against oxidative and this might have resulted from its ROS scavenging activity.
According to previous reports, activation of stress response in cells and tissues is mandatory for survival of multicellular life forms. In this group of stress associated responses, stress resistance is related to various types of interferences that enhance life expectancy [29]. In the present experimentation, it was observed that HACBF treatment with different doses enhances life span in C. elegans and maximum enhancement was observed at 4 mg/ml followed by other doses. The longevity enhancement capability of HACBF in wild type N2 was investigated. The wild type N2 population was given treatment of various doses (0, 1, 2, and 4 mg/ml) of HACBF at the early embryonic stage on NGM plates. Further, we noticed a marked surge in mean lifespan of HACBF treated worms at 1 mg/ml (16.30 ± 0.42, p ≤ 0.01), 2 mg/ml (16.72 ± 0.51, p ≤ 0.001), and 4 mg/ml (19.06 ± 0.57, p ≤ 0.001), as compared with control (14.95 ± 0.36) (Fig. 7c–e). The utmost addition in average lifespan of 27% was displayed by 4 mg/ml concentration (p < 0.001, Fig. 7c, e). For the first time the present study reveals the competence of HACBF to modify the normal lifespan of wild-type C. elegans (Bristol N2). In the present experiment it was noticed that reductions in protein carbonyl, lipofuscin and lipid levels are directly related to the longevity of worms.
Aging is a progressive multifactorial biological process and mitochondria play a central role in it. The mitochondrial dysfunction in aggregation with altered mitochondrial dynamics drives aging. The low levels of ROS trigger a stress response and biogenesis pathways that increase the mitochondrial dynamics which is protective and prevent aging [23]. However, the increased free radicals can break down the cellular components like DNA, RNA, protein or lipids, which may also contribute to aging. The wild-type worms were treated with HACBF (4 mg/ml) for 2 days and then mitochondrial health was quantified using a MitoTracker. The viability of mitochondria was found significantly increased in HACBF treated worms (4 mg/ml), compared to control (Fig. 8a–c), The accumulation of protein carbonyl and lipofuscin contents are the result of oxidative and cellular damage and it increases with age in C. elegans [2]. The results showed significant reduction in protein carbonyl content in the HACBF treated (4 mg/ml) wild-type worms, compared to control (Fig. 7f). These findings suggest that dietary supplementation of HACBF notably attenuate the oxidative stress in worms by the augmentation of mitochondrial viability, and decreasing the levels of protein carbonyl content. The amount of endogenous lipofuscin is an important biomarker of aging in multicellular organisms. This auto fluorescent protein accumulates with the passage of time in cells and tissues with a declined turnover. This auto fluorescent age pigment fluoresces yellow to red wavelength when excited with UV or blue light [18]. It is generated during aging process and its contents are reduced through usage of phytomolecules [13]. Therefore, the effect of the most effective dose of HACBF (4 mg/ml) was evaluated by observing pre-treated HACBF day 4 worms to examine the lipofuscin levels in the intestines. The HACBF supplementation was able to reduce intestinal lipofuscin accumulation in worms by 34% (p < 0.01**) in comparison to untreated control worms where a high level of lipofuscin build-up was observed (Fig. 9d–f). These results suggest HACBF extends lifespan by reducing oxidative stress level and stress-mediated macromolecular damage. The decline in age pigment lipofuscin is consistent with DR like effect mediated by HACBF treatment. The decline in intestinal lipofuscin autofluorescent suggests a reduction in oxidative stress and delay in aging in C. elegans. The result indicates that aging progression is directly proportional to lipofuscin accumulation. HACBF delayed aging process may be due to its inherent antioxidative property.
Fat accumulation is a biomarker of aging and is associated with higher ROS levels [27, 37, 40, 41]. The fat storage is also found to alter with the energy expenditure and diet [30]. Here, we used Nile red staining methods to probable the effect of HACBF on the fat accumulation in wild type-N2 worms. The progression in age is associated with higher accumulation of triglycerides which is correlated with age related diseases like obesity, cardiovascular diseases and cancer [4, 20, 30]. The C. elegans fats are majorly stored as triglycerides and it is found to deplete in case of DR [3]. The HACBF treated worms were found to have lower fat levels significantly by 54% (Fig. 9a–c). This result indicates that HACBF can be a useful remedy against increased lipid peroxidation, which is the major pathological hallmark behind age-associated neurodegenerative disorders.
The cellular signaling pathways are associated with lifespan and cellular stress levels [13, 39, 42, 56]. The cellular antioxidant defence system counteracts this process by detoxifying ROS [15]. Superoxide dismutase (SOD-3), is vital for oxidative stress resistance in worms [12, 22] and binding domain of DAF-16 is located in a transcriptional promoter region of SOD-3, and expression of SOD-3 depends on DAF-16 activity [25]. Therefore, the effect of HACBF exposure on expression of antioxidant enzyme encoding stress response gene SOD-3 and GST-4 was evaluated in transgenic strains CF1553 (SOD-3∷GFP) and CL2166 (GST-4::GFP). In the present experiment 4 mg/ml HACBF displayed higher SOD-3∷GFP and GST-4::GFP expression as compared to control (Fig. 10a–f). The results indicated that the HACBF at 4 mg/ml may considerably enhance SOD-3∷GFP expression by 67% in transgenic strain CF1553 of C. elegans (Fig. 10a–c). In addition, the effect of HACBF on GST-4 expression was studied because it is a major phase II detoxification enzyme, which is regulated by SKN-1 in response to oxidative stress. Plant extract (4 mg/ml) boosted the expression of GST-4::GFP significantly by 96% over the control (Fig. 10d–f). Antioxidant genes viz. sod-3, gst-4 genes were also upregulated in treated worms as compared to untreated control wild-type N2 worms (Fig. 13a). Such genes are directly controlled by DAF-16, which behave as stress-sensitive reporters to predict longevity in C. elegans [44]. It is suggested that up-regulation of SOD-3 and GST-4 and decline in oxidative stress level could be responsible for HACBF mediated life span extension and stress tolerance in worms. These results are consistent with previous studies where the decline in oxidative stress level and elevated level of antioxidant gene promoted mean lifespan in an organism [32, 25].
To study the effect of HACBF on synaptic ACh availability, aldicarb assay was performed with different doses i.e., 1 mg/ml, 2 mg/ml, 3 mg/ml, 4 mg/ml and 5 mg/ml. In the present study it was observed that increase in percentage of worms paralyzed was found to be dose dependent. Treatment with 1 mg/ml HACBF was 54.33 ± 4.41(p ≤ 0.001) followed by 2 mg/ml HACBF 67.33 ± 2.84 (p ≤ 0.001), 3 mg/ml HACBF 87.81 ± 2.08 (p ≤ 0.001), 4 mg/ml HACBF 89.45 ± 1.85 (p ≤ 0.001), and 5 mg/ml HACBF 50.33 ± 1.4 (p ≤ 0.001) as compared to that of control 47.33 ± 1.33. The results suggested that HACBF increased synaptic Ach content (Fig. 11a).
In addition to synaptic Ach level enhancement, the involvement of nicotinic acetylcholine receptor (nAChR) in HACBF mediated elevation of cholinergic transmission, using levamisole assay was also ascertained. nAChR responsiveness is proved by the percentage of paralyzed worms at a time point. In comparison to the control (45.15 ± 0.57), increased percentage of paralyzed worms was noticed upon treatment with 4 mg/ml HACBF (73.33 ± 3.28, p ≤ 0.01) showing the useful effects of HACBF on nAChR (Fig. 11b).
qPCR experiment was carried out to examine the impact of different genes on ACh synthesis and nAChR activities. The genes selected for transcript examination were related to ACh synthesis; cha-1 (choline acetyl transferase), degradation; ace-1 and ace-2 (genes coding for AChE), transport; unc-17 (acetylcholine transporter) and cho-1 (high affinity choline transporter), and receptor; unc-29 (non-alpha subunit of nAChR) and unc-38 (alpha subunit of nAChR), and unc-50 (regulator of nicotinic acetylcholine receptor). In comparison to the control, at 4 mg/ml HACBF[CO7], a significant upregulated expression of cho-1 (2.82 ± 0.15, p ≤ 0.001), unc-17 (1.98 ± 0.23, p ≤ 0.001), and unc-29 (1.81 ± 0.13, p ≤ 0.001) and downregulated expression of ace-1 (0.60 ± 0.20, p ≤ 0.05), and ace-2 (0.63 ± 0.10, p ≤ 0.05) was found (Fig. 11c). The rest of the genes were devoid of any significant alteration (11c). Moreover, the study was further validated by up regulated expression of neuromodulator gene related to acetylcholine cascade in male and female mice respectively unc-29 (3.04-fold and 2.62-fold), (Fig. 11d).
In the present experiment, a marked reduction at day 5 in α-syn levels of HACBF treated transgenic C. elegans at 1 mg/ml HACBF (18.57 ± 1.27, p ≤ 0.001), 2 mg/ml HACBF (15.47 ± 0.84, p ≤ 0.001), 3 mg/ml HACBF (12.72 ± 0.85, p ≤ 0.001), 4 mg/ml HACBF (11.43 ± 0.21, p ≤ 0.001), and 5 mg/ml HACBF (16.84 ± 1.4, p ≤ 0.001), was seen as compared with control (25.62 ± 0.73) (Fig. 12a, b). The maximum reduction of α-syn level (55%) was observed at 4 mg/ml HACBF treatment.
During experimentation, HACBF treatment overexpress the mRNA level of insulin signaling pathway genes daf-2 (11.16-fold) and daf-16 (11.64-fold) (Fig. 13c). Here, a significant upregulation of skn-1 (6.97-fold), gst-4 (1.73-fold) and gst-7 (2.02-fold) genes which are directly related to aging process was observed. Additionally, an increase in mRNA transcript levels of pha-4 (2.55-fold), and its downstream target genes sod-3 (1.91-fold), and sod-4 (1.73-fold) was seen (Fig. 13a, c). Further the change in the mRNA expression level of genes bec-1 (14.36-fold) and lgg-1 (11.33-fold) confirmed the role of HACBF in autophagy-mediated by DR (Fig. 13c). Besides, we observed a significant change in the expressions of stress responsive genes ctl-2 (2.15-fold) and jnk-1(3.35-fold) which shows that HACBF also requires antioxidant defense system for the longevity promotion. Moreover, the study was further validated by upregulated expression of antioxidant and longevity promoting genes in male and female mice respectively ctl-1 (5.57-fold and 1.84-fold), sod-2 (12.66-fold and 4.16-fold), sod-3 (5.65fold and 2.33fold), and sod-4 (3.60-fold and 1.65-fold), (Fig. 13b).
During the present experimentation the usefulness of HACBF on dopamine was investigated in wild type N2 C. elegans and reported higher repulsion time (1.33 ± 0.04) in wild type control worms as compared to treated worms (0.77 ± 0.03, p = 0.02) (Fig. 12c), which directly indicate the marked increase in dopamine.
Various activities in worms, especially head thrash and body bends show the motor neuron health and dopamine level in the worm. Usefulness of HACBF was noticed by observing the head thrash and body bend in C. elegans. In the result it was documented that HACBF treated N2 worms showed greater number (Fig. 12e) of head thrash (40.17 ± 0.87, p = 0.02) compared to untreated control worms (35.83 ± 1.55). Subsequently, wild type treated worms (14.67 ± 1.45, p < 0.001) showed significant improvement in body bends (8.33 ± 0.88) (Fig. 12d).
It is well established that α-syn aggregation play a major role in onset of PD [33, 43] and many genes are also responsible for α-syn suppression in C. elegans. That is why the qPCR studies of PD was performed to identify the mechanism behind the HACBF modulation of α-syn, lipid, dopamine levels and mitochondrial functioning through ubc-12, lagr-1, ymel-1, pink-1, and pdr-1. It was found that HACBF[CO7] showed a significant useful effect in fold change of mRNA expression of ubc-12(1.96 ± 0.06, p < 0.05), pdr-1(2.08 ± 0.02, p < 0.01), lagr-1(2.98 ± 0.34, p < 0.001), pink-1(1.68 ± 0.04, p < 0.01), and ymel-1(12.93 ± 0.80, p < 0.001) in HACBF treated worms (Fig. 12f). Moreover, the study was further validated by upregulated expression of neuromodulatory genes in male and female mice respectively ubc-12 (2.52-fold and 4.67-fold) and pink-1(1.61fold and 2.75fold), (Fig. 12g).
The present study is the first of its kind that demonstrates the effect of HACBF on ageing and cognition in C. elegans model system. The healthy ageing activities of medicinal plant extracts can be attributed for stress resistance, lifespan extension and cognitive boosting activities. Our results suggest that HACBF altogether alleviate oxidative stress, promote antiageing activities and boost cognition in C. elegans. The traditional Indian system of ayurvedic medicine utilizes a mixture of herbs for treating various ailments. Thus, such research work opens new avenues for the development of anti-aging and cognitive booster herbal products as an herbal therapy for delaying aging and age-related disorders. The formulations thus developed could be conveniently implemented in the form of decoctions/tea.
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 19 kb)Supplementary file2 (DOCX 226 kb) |
PMC9649266 | Nicola Sgherza,Daniela Di Gennaro,Paola Curci,Rita Rizzi,Daniela Roccotelli,Maria Croce,Martina Avantaggiato,Loredana Ruga,Vanda Strafella,Angelantonio Vitucci,Antonio Palma,Antonella V. Russo Rossi,Teresa Troiano,Angela M. V. Larocca,Maria Chironna,Silvio Tafuri,Francesco Albano,Pellegrino Musto | SARS-CoV-2 Infection Incidence and Outcome Before and After Full Vaccination in Patients With Monoclonal Gammopathy of Undetermined Significance | 09-11-2022 | SARS-CoV-2 Infection Incidence and Outcome Before and After Full Vaccination in Patients With Monoclonal Gammopathy of Undetermined Significance
Epidemiological studies have previously reported that patients with monoclonal gammopathy of undetermined significance (MGUS) may have an increased risk of developing viral infections. Regarding specific antiviral immunological response, no significant differences between MGUS and normal controls have been detected about herpesviruses HSV1, cytomegalovirus (CMV), and Epstein-Barr virus, while the median titer of antivaricella-zoster virus IgG was found significantly lower in MGUS patients. It has been also speculated that the intrinsic immune dysregulation exhibited by MGUS subjects may contribute to determine a suboptimal serological response to vaccines, including those anti-SARS-CoV-2. In this setting, the capacity to produce neutralizing antibodies after anti-SARS-CoV-2 vaccine (2 doses of BNT162b2 or 1 dose of AZD1222) in patients with different types of plasma-cell dyscrasia was reported to be not significantly different between MGUS subjects and healthy controls (HCs). These data were further confirmed by another study, carried out on fully vaccinated patients (2 doses of BNT162b2 or mRNA-1273 or 1 dose of Ad26.COV2.S) with asymptomatic precursor stages of multiple myeloma. Contrarily to smoldering myeloma, an attenuated antibody response in MGUS patients was not observed. The role of at least two doses and that of a “booster” administration of anti-SARS-CoV-2 vaccines has been further underlined in multiple myeloma but only marginally in MGUS. We and others recently showed that patients with MGUS had the same risk of SARS-CoV-2 infection and a similar clinical outcome compared to age- and sex-matched HCs during the first wave of the COVID-19 pandemic, before the availability of anti-SARS-CoV-2 vaccines. Here, we report that fully vaccinated individuals with MGUS maintain an analogous incidence of SARS-CoV-2 infection, but also show a significant improvement in clinical outcomes of COVID-19 compared to not vaccinated patients, formally proving, for the first time, the efficacy of anti-SARS-CoV-2 vaccines in this population of patients. We obtained retrospective information from 86 individuals found to be SARS-CoV-2-positive until March 2022, among 1060 vaccinated MGUS patients analyzed after at least a second dose of anti-SARS-CoV-2 vaccine received between March and December, 2021, with a median follow-up of 300 days (range 30–454). Patients with monoclonal gammopathies of clinical significance, previous SARS-CoV-2 infection, only one dose of vaccine received or SARS-CoV-2 infection after one dose, as well as not vaccinated (no-vax) subjects, were excluded from the final analysis. SARS-CoV-2 infection was confirmed by rapid antigen test or RT-PCR on nasopharyngeal swabs. Clinical data were collected from a review of medical records and regarded, in particular, age, comorbidities (cardiovascular, pulmonary or renal diseases, diabetes, and nonhematological cancers), the presence of symptoms (in detail: fever or chills, cough, shortness of breath or difficulty breathing, fatigue, muscle or body aches, headache, loss of taste or smell, sore throat, congestion or runny nose, nausea or vomiting, diarrhea), hospitalization, hospitalization in intensive care unit (ICU), and outcome (alive/dead). Additional information were extracted from “Infections Regional Information System (IRIS)-Puglia,” a regional platform where authorized medical health workers can view the results of the nasopharyngeal swabs for SARS-CoV-2 performed, along with other clinical information. Statistical analyses were carried out using GraphPad Prism version 8.3.0 (GraphPad Software Inc., San Diego, CA). The study was conducted within the context of the ClinicalTrials.gov Identifier NCT04352556. Clinical characteristics of not vaccinated MGUS have been previously reported in detail and are summarized and compared with those of vaccinated patients in Table 1. Of the 86 vaccinated SARS-CoV-2-positive patients, 53 (61.6%) were men, while the mean (SD) age was 65.9 (±13.4) years. One, 2, and ≥3 of evaluated comorbidities were reported in 32 (37.2%), 10 (11.6%), and 14 (16.3%) of patients, respectively The most frequent MGUS-isotype was IgG (74.4%), followed by IgM (16.3%), IgA (7%), and biclonal (2.3%). Immunoparesis was present in 10 patients (11.6%), absent in 68 (79.1%), not available in 8 (9.3%). Most of patients (96.5%) were at low or low-intermediate risk, according to the Mayo Clinic prognostic model. Overall, the incidence of SARS-CoV-2 infection was not significantly different between not vaccinated and vaccinated MGUS patients (Table 1). A case of reinfection was found. The patient was a 65-year-old woman, with IgGk, low-intermediate risk MGUS, positive at first on January 2021 (not yet vaccinated), and then in January 2022, 25 days after the second dose of BNT162b2 vaccine. In both circumstances, infection was asymptomatic. Rates of symptoms, hospitalization, hospitalization in ICU, and deaths were instead significantly lower in vaccinated than in not vaccinated MGUS patients (Table 1). In particular, only 2 hospitalizations, one of which in ICU, and one death were reported among vaccinated MGUS subjects. The patient hospitalized in ICU and discharged alive, was a 75 years-old man with IgA lambda-MGUS and hypertension. The dead patient was an 89 years-old man with IgM kappa-MGUS, asbestosis, and implantable cardioverter defibrillator; both these patients had received 3 doses of BNT162b2 vaccine. Notably, SARS-CoV-2 incidence and related symptoms were highly more frequent among patients after 2 doses than in those treated with 3 doses, while the mean number of days between the last dose of vaccine and infection was inferior (Table 2). Variants of concern were available in 25 vaccinated patients: n = 2 Alfa (8%), n = 4 Delta (16%), and n = 19 Omicron BA.1 (76%). Despite the high vaccination coverage rate, most of the cases of positivity (n = 81; 94.2%) to SARS-CoV-2 were found between December, 2021, and January, 2022, mimicking what was observed in the general population. About safety, from the start of vaccination campaign (December 27, 2020), no relevant or unexpected vaccine-related side effects were recorded in our court of 1086 MGUS vaccinated subjects until March 31, 2022. Data regarding evaluation of humoral response after a complete course of anti-SARS-CoV-2 vaccine are yet limited in MGUS. In particular, we are not aware of previously published results specifically addressing the determination of anti-spike IgG antibodies with three doses of vaccine. Such an evaluation (after a median time of 100 days, range 45–180) was available in 20 of our MGUS, COVID-19-naive subjects. All these patients were judged as “responders,” as they achieved a titer greater than 50 AU (Arbitrary Units)/mL, which is considered the cutoff limit for response by the test manufacturer (Abbott). In particular, median value was 9050 AU/mL (range 1,482–54,390), a level that was quite similar to that observed in vaccinated age- and sex-matched HCs (data not shown). To the best of our knowledge, this is the first report of “clinical” efficacy on COVID-19 of anti-SARS-CoV-2 vaccines in MGUS patients. Obviously, our study suffers from some limitations, such as the lack of information regarding protection provided by vaccines in the long term due to the short duration of follow-up, as well the scarcity of data regarding amount and durability of serological immune response. Notwithstanding, our observations highlight some relevant points. First, the incidence of SARS-CoV-2 infection was not significantly reduced in vaccinated MGUS patients, particularly in those who had received only 2 doses, probably because of a different pandemic scenario, characterized by higher diffusion capacity of the more recently recognized viral variants and fewer restriction measures applied during the last months. Three doses also significantly prolonged the time elapsed from vaccination to infection. Furthermore, the clinical outcome of COVID-19 appeared to be significantly improved by vaccines, particularly after 3 doses, thus supporting fully, extended vaccination programs also in patients with MGUS. Finally, we report here preliminary data about the apparently “normal” humoral response after 3 doses of anti-SARS-CoV-2 vaccines in MGUS. More patients and adequate follow-up will be necessary to evaluate the clinical significance of this finding, also in the light of the emerging issue of “hybrid” immunization (vaccines plus SARS-CoV-2 infection), recently reported in multiple myeloma.
PM and NS conceived and led the project. NS conducted database building, extraction and coding. NS and PM queried and analyzed the data. PM and NS wrote the main manuscript text and created all tables. All authors made a substantial intellectual contribution to the study, interpreted the data, discussed the results and reviewed, edited, and approved the final version of the manuscript.
The authors have no conflicts of interest to disclose. |
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PMC9649297 | 36130700 | Xinming Liu,Siwen Zhang,Yunran Guo,Xiaokun Gang,Guixia Wang | Treatment of Primary Pigmented Nodular Adrenocortical Disease | 10-11-2022 | primary pigmented nodular adrenocortical disease (PPNAD),adrenocorticotropin hormone (ACTH)-independent Cushing’s syndrome (CS),hypercortisolemia,Carney complex (CNC),adrenalectomy | Primary pigmented nodular adrenocortical disease (PPNAD) is a rare cause of adrenocorticotropin hormone (ACTH)-independent Cushing’s syndrome (CS), which mainly occurs in children and young adults. Treatment options with proven clinical efficacy for PPNAD include adrenalectomy (bilateral or unilateral adrenalectomy) and drug treatment to control hypercortisolemia. Previously, the main treatment of PPNAD is bilateral adrenal resection and long-term hormone replacement after surgery. In recent years, cases reports suggest that unilateral or subtotal adrenal resection can also lead to long-term remission in some patients without the need for long-term hormone replacement therapy. Medications for hypercortisolemia, such as Ketoconazole, Metyrapone and Mitotane et.al, have been reported as a preoperative transition for in some patients with severe hypercortisolism. In addition, tryptophan hydroxylase inhibitor, COX2 inhibitor Celecoxib, somatostatin and other drugs targeting the possible pathogenic mechanisms of the disease are under study, which are expected to be applied to the clinical treatment of PPNAD in the future. In this review, we summarize the recent progress on treatment of PPNAD, in which options of surgical methods, research results of drugs acting on possible pathogenic mechanisms, and the management during gestation are described in order to provide new ideas for clinical treatment. | Treatment of Primary Pigmented Nodular Adrenocortical Disease
Primary pigmented nodular adrenocortical disease (PPNAD) is a rare cause of adrenocorticotropin hormone (ACTH)-independent Cushing’s syndrome (CS), which mainly occurs in children and young adults. Treatment options with proven clinical efficacy for PPNAD include adrenalectomy (bilateral or unilateral adrenalectomy) and drug treatment to control hypercortisolemia. Previously, the main treatment of PPNAD is bilateral adrenal resection and long-term hormone replacement after surgery. In recent years, cases reports suggest that unilateral or subtotal adrenal resection can also lead to long-term remission in some patients without the need for long-term hormone replacement therapy. Medications for hypercortisolemia, such as Ketoconazole, Metyrapone and Mitotane et.al, have been reported as a preoperative transition for in some patients with severe hypercortisolism. In addition, tryptophan hydroxylase inhibitor, COX2 inhibitor Celecoxib, somatostatin and other drugs targeting the possible pathogenic mechanisms of the disease are under study, which are expected to be applied to the clinical treatment of PPNAD in the future. In this review, we summarize the recent progress on treatment of PPNAD, in which options of surgical methods, research results of drugs acting on possible pathogenic mechanisms, and the management during gestation are described in order to provide new ideas for clinical treatment.
Primary pigmented nodular adrenocortical disease (PPNAD) is a rare cause of adrenocorticotropin hormone (ACTH)-independent Cushing’s syndrome (CS) and is characterized by small, black and brown pigmented micronodules in adrenal cortex. PPNAD mainly occurs in children and young adults. The incidence of PPNAD is unknown at present 1 . PPNAD accounts for only about 1.1–1.8% of all causes of CS 2 3 . It may be isolated or associated with Carney complex (CNC). About 10% of patients with PPNAD without familial history or other manifestations of CNC, are generally termed as isolated PPNAD (i-PPNAD) 4 . CNC is a rare autosomal dominantly inherited multiple neoplasia syndrome characterized by spotty skin pigmentation, multiple endocrine neoplasia, and myxomas 5 . It is most frequently caused by mutations in the type 1a regulatory subunit gene of protein kinase A (PKA) ( PRKAR1A ), and approximatively 30% of cases occur sporadically. Clinical manifestations of CNC include lentigines, blue nevus, myxomas in the heart, skin, and breast, pituitary tumors, adrenocortical tumors, and thyroid neoplasms. PPNAD and the consequent ACTH-independent Cushing’s syndrome is one of the most common endocrine manifestations of CNC, accounting for about 25–60% of CNC patients 6 7 . Hypercortisolism in PPNAD can be overt, subclinical, cyclic or atypical. And adrenal imaging is also often non-specific, which makes the diagnosis and treatment of the disease difficult 8 9 10 11 . In the last two decades, much progress has been made in describing the various forms, clinical manifestations, and pathogenesis of PPNAD. However, there is still controversy regarding the treatment of the disease. Treatment options with proven clinical efficacy for PPNAD included adrenalectomy (bilateral or unilateral adrenalectomy) and drug treatment to control hypercortisolemia. The preferred treatment is surgery. Bilateral adrenalectomy is the universal recommended option, which will cause permanent adrenal insufficiency and require a life-long hormone replacement therapy 12 . In recent years, some authors considered that unilateral adrenalectomy or subtotal adrenalectomy can also let selected patients to obtain long-term remission without hormone replacement therapy, so that patients have a better quality of life 13 14 15 . As PPNAD often leads to mild, occult or atypical Cushing’s syndrome, drug therapy may be a good candidate. In medical treatment, Ketoconazole, Metyrapone, Mitotane and other drugs can be used to treat hypercortisolemia for patients who have severe hypercortisolemia before surgery and refuse to undergo further surgery or who have surgical contraindications 16 . In addition, a number of promising drugs are being studied and may be used in the clinic in the future. Along with the basic research of PPNAD, scholars found that there were several alternative drugs associated with the possible mechanism targets of the disease: tryptophan hydroxylase inhibitor, 5-hydroxytryptamine receptor antagonist, COX2 inhibitor celecoxib and somatostatin, etc. 17 18 19 . These drugs are promising, but most of them only are proven in animal studies, and further research is needed to illustrate whether they can be used in the clinic. PPNAD is now better recognized, an increasing number of endocrinologists and urologists were aware of its numerous manifestations and the need for effective management. Furthermore, the management of some special groups, such as children and pregnant women, need multidisciplinary cooperation. Appropriate treatment strategy and follow-up management are important to prognosis of disease. In this review, we will summarize the update of diagnosis and treatment of PPNAD, including treatment experience on patients during gestation, and results of studies on new type drugs acting on new molecular targets.
PPNAD patients are generally younger. The age of PPNAD patients associated with CNC appeared to be bimodally distribution, with a few appearing in the first 2–3 years and most in the twentieth and thirtieth decades 20 . Among a total of 212 patients with PPNAD, the prevalence was significantly higher in women than in men (71 vs. 29%). The median age of PPNAD diagnosis was 34 years, and women were diagnosed at a younger age than men (30 years vs. 46 years). Such gender difference became apparent after puberty 21 . CS usually starts insidiously, and the main features are hypertension, weight gain, and growth restriction in children, although it is not seen in all patients. Typical features also include osteoporosis, proximal muscle weakness, wide purple striae, full moon face, central obesity, irregular menstruation in women, hirsutism, acne, decreased cognitive function, and so on 22 23 . According to previous literature, hypercortisolism in PPNAD can be overt, subclinical, cyclic or atypical 8 9 10 11 . Due to the variable Cushing’s syndrome phenotypes of PPNAD, its symptoms and signs may not be obvious. Patients always visit doctors repeatedly until the diagnosis of ACTH-independent CS can be clearly diagnosed 12 . Whether these different CS phenotypes are related to the genotype of PPNAD remains to be further explored. Osteoporosis and osteoporotic fractures are more common in patients with PPNAD compared with other causes of Cushing’s syndrome. In a 10-year retrospective study of 1652 Chinese Cushing’s syndrome patients, osteoporotic fractures were observed more frequently in PPAND than in adrenocortical adenoma (ADA) and primary bilateral macronodular adrenal hyperplasia (PBMAH) patients (26.7 vs. 9.0 vs. 4.9%) 3 . In another Chinese PPNAD cases (25 patients), PPNAD patients are more likely to develop osteoporosis than ADA patients (78.3 vs. 48.0%), while there are no differences between PPNAD and PBMAH patients 9 . In addition, some patients are accompanied by hyperandrogenemia, with hirsutism and irregular menstruation as the main symptoms 8 24 25 . In a case series of 6 patients from India, one patient had CS associated with symptoms of hyperandrogenism (hirsutism and irregular menses) 8 . Since the majority of PPNAD patients are associated with CNC, patients usually have clinical manifestations associated with CNC, such as spotty skin pigmentation, heart myxoma, skin myxoma. And many patients have a family history of CNC or PPNAD 7 26 . Although CS is rare in children, it can lead to significant morbidity and even mortality. CS should be diagnosed early by clinician 27 . PPNAD mainly occurs in late childhood or youth, and is very rare in infancy, the reported youngest patients are only 15 months 8 . Bilateral adrenal resection and replacement therapy were the most common treatment strategies 28 29 , Ketoconazole and other drugs can be used for transition before surgery 8 . They always showed catch-up growth and improvement in the BMI after adrenalectomy 8 28 30 . PPNAD may be a signal of CNC, so patients with dominant CS at a younger age may requires genetic testing and long-time follow-up. PPNAD is very rare in infancy, whereas CS associated with McCune – Albright syndrome (MAS) is usually in infancy. MAS is a sporadic heterogeneous disorder caused by an activating mutation in GNAS , which encodes the alpha subunit of the Gs G-coupled protein receptor 31 . It is characterized by congenital polyostotic fibrous bone dysplasia, café-au-lait skin plaques, precocious puberty and other endocrine disorders. About 7.1% of patients can present with CS, which is usually severe, and is also associated with other endocrine dysfunction, such as hyperthyroidism and precocious puberty 32 . Adrenal causes of CS, such as PBMAH and adrenal cortical adenoma, may be closely associated with anomalous adrenal hormone receptors, especially G-coupled protein receptors, such as the gastric inhibitory polypeptide (GIP) receptor, the luteinizing hormone/human chorionic gonadotropin (LH/HCG) receptor, vasopressin (AVP) receptor, and the beta-adrenergic receptor 33 . These aberrant adrenal hormone receptors are functionally associated with steroidogenesis. Although less reported in PPNAD, aberrant 5-HT receptors have recently been found to be expressed in PPNAD 17 . The detection of abnormal adrenergic hormone receptors provides a new target for treatment.
The diagnosis of PPNAD is challenging, especially for patients without other manifestations of CNC or family history due to: 1) clinical manifestations may not be obvious, atypical, and progress slowly; 2) laboratory tests can be normal during the non-secretory period of periodic CS; 3) those without CNC-related clinical manifestations (skin pigmentation, myxoma, etc.) and family history which effect judgement; and 4) it is difficult to accurately find small nodules on CT. First, the diagnosis of CS should be established. After excluding exogenous glucocorticoid exposure, initial screening tests include urine free cortisol (UFC), late night salivary cortisol, 1 mg overnight dexamethasone suppression test (DST) and 2 mg/d for 48 hours DST 34 . The dexamethasone-CRH test or the midnight serum cortisol test can be used as subsequent evaluation to establish the cause of CS in patients with concordantly positive results from two different initial tests 34 . Adrenal imaging should be evaluated in patients with suspicious adrenal cause CS. In PPNAD, computed tomography (CT) imaging examinations may show normal size adrenal glands and several small bead-like nodules 22 35 . But ‘normal’ imaging is often reported too 10 . CT with slice thickness of 3 mm or less may be helpful for diagnosis 36 . Single-photon emission computed tomography (SPECT-CT) with the use of iodomethyl-norcholesterol (I-131) showed bilateral glands uptake was increased 25 . It supported the diagnosis of PPNAD. In addition, adrenal cortical imaging with (6–131I) iodomethyl-19 cholesterol-lowering (NP-59) also can be an optional imaging examination. Vezzosi et al. compared CT scans and adrenal cortical imaging with (6–131I) iodomethyl-19 cholesterol-lowering (NP-59) in 17 patients with diagnosed PPNAD. NP-59 imaging showed bilateral adrenal uptake in all patients, and asymmetric uptake was observed in 59% of patients 37 . Besides, bone scintigraphy will help identify bone lesions associated with MAS 38 . Laboratory examination often show that patients with overt CS have ACTH-independent cortisol secretion hypercortisolism unrelated to ACTH and lack of cortisol diurnal rhythm. However, laboratory tests may be normal during the non-secretory period of periodic CS. It is worth noting that patients with PPNAD often appear an abnormal increase in urinary free cortisol during the Liddle test 39 . This increase in urinary glucocorticoid excretion following dexamethasone administration is one of the diagnostic criteria for CNC. But recently, a prospective study showed that only 39 percent of patients with confirmed or probable PPNAD had an abnormally elevated UFC 40 . In 2018, for the first time Chen et al. proposed to distinguish PPNAD from bilateral macronodular adrenal hyperplasia and adrenal cortical adenoma (ADA) by using the ratio of UFC to pre-HDDST 24 hours after high-dose dexamethasone inhibition test (HDDST). When 24-hour UFC (post-H-DEX)/UFC (pre-H-DEX) was>1.08, the sensitivity and specificity were 84.0 and 75.6%, respectively 9 . In addition, adrenal tissue ACTH immunohistochemistry will help identify the presence of ACTH paracrine secretion 41 . ACTH is a regulator of androgen secretion from the adrenal cortex. Due to the inhibitory effect of excessive cortisol secretion on ACTH, the dehydroepiandrosterone (DHEAS) of adrenal CS patients were often lower than those of healthy individuals 17 . According to previous reports of patients with PPNAD, laboratory results showed frequent decreases in serum DHEAS levels 18 , and some patients showed overproduction of testosterone and normal DHEAS 6 . Overproduction of androgens by PPNAD-associated adenomas leading to virilization and infertility has also been reported in the literature 19 . ACTH is a regulator of androgen secretion from the adrenal cortex. Due to the inhibitory effect of excessive cortisol secretion on ACTH, the dehydroepiandrosterone (DHEAS) of adrenal CS patients were often lower than those of healthy individuals 42 . According to previous reports of patients with PPNAD, laboratory results showed frequent decreases in serum DHEAS levels 25 , and some patients showed overproduction of testosterone and normal DHEAS 24 . Overproduction of androgens by PPNAD-associated adenomas leading to virilization and infertility has also been reported in the previous literature 43 . Both CNC and PPNAD can be associated with inactivating mutations of the PRKAR1A gene. In 353 patients with PPNAD or CNC, 73% carried 80 different PRKAR1A mutations 21 . Such inactivating PRKAR1A germline mutations are also common in i-PPNAD 24 44 . In addition, mutations in PDE11A 45 and PDE8B 46 are described for PPNAD. Somatic defects in PRKAR1A gene 47 , and β-catenin gene ( CTNNB1 ) 48 have been also observed in PPNAD patients, which has potential clinical and genetic significance. The study of genetic mutations in adrenal tissue will help to identify such somatic mutations. And genetic testing may be of great help in diagnosing PPNAD.
Bilateral adrenalectomy is the universal recommended surgical type for PPNAD treatment because of its feature for involvement of bilateral adrenal glands 8 12 49 50 51 . Clinical Practice Guidelines for the treatment of CS from Endocrine Society suggested that laparoscopic bilateral adrenalectomy is the definite treatment for PPNAD 52 . Laparoscopic surgery can be performed as transabdominal or retroperitoneal approach, both of which have significant advantages over open surgery, including a clear field of vision, small incisions, less bleeding, and shorter hospital stays. The retroperitoneal approach, which can avoid abdominal stimulation has shorter operative time, fewer complications and less postoperative pain. But it has a smaller workspace and may increase intraocular pressure due to the need for prone position 53 . The advantages of bilateral adrenalectomy are that it rapidly cures CS, has low risk of recurrence, and is suitable for patients of all ages 8 28 54 . Bilateral adrenalectomy was performed in most patients presenting with overt CS. According to the current literatures, most of patients have biochemical remission after bilateral adrenalectomy, and the corresponding symptoms can disappear. The children always catch-up with growth, and the weight of patients can return to normal, and the Cushing appearance can disappear. Most of the patients were followed up for a short period of time. It is conceivable that bilateral adrenalectomy will cause permanent adrenal insufficiency and require a life-long careful glucocorticoid and mineralocorticoid replacement therapy. Such patients are prone to acute adrenal crisis as a complication especially under stress or infection, and may have a lower quality of life, so they have to be good compliance 8 12 15 35 . Unilateral total adrenalectomy associated with contralateral partial adrenal (adrenal-sparing surgery) has been observed to achieve good treatment results in PBMAH 55 . This approach avoids the need for lifelong steroid replacement in most cases and has a low rate of adrenal insufficiency and recurrence 56 . It may be a potential surgery approach for PPNAD. In 31 patients with adrenal hyperplasia (MAH) or PPNAD who underwent bilateral adrenalectomy, 30 (97%) patients are cured biochemically 12 . These patients had complete resolution for Cushing’s syndrome symptoms at 9 to 12 months postoperatively. Six patients in their cases presented with hypertension, and all were cured or improved after surgery. Five of them were able to discontinue all antihypertensive drugs, and the remaining one required only one antihypertensive drug. In the case series of Memon et al. on a mean age of 8.2 years, 5 patients are cured after bilateral adrenalectomy, and 1 of them die about 2 years after surgery probably caused by an adrenal crisis. All patients showed improvement in height post-surgery 8 . Two adolescent brothers with complaints of weight gain and growth retardation had typical Cushing’s syndrome manifestations: hypertension, moon-shaped face, facial plethora, centripetal obesity, and red-purple skin striae. One month after bilateral adrenalectomy, they lost 5–6 kg of weight their skin textures fades and there was a reduction in the facial plethora. And their blood pressure, lipids and heart ejection fraction were improved 28 . A 20-year-old woman presenting with overt CS had bilateral avascular necrosis of the femoral heads. After bilateral adrenalectomy, her presentation of cortisol excess improved, and her femoral head necrosis resolved without orthopedic intervention with 1-year follow-up 57 .
In recent years, more studies have shown that unilateral adrenal resection can achieve remission of long-term symptoms and biochemical for some patients without adrenal insufficiency 51 . Some case reports 58 59 and cases series proved the feasibility and effectiveness of unilateral adrenalectomy 10 13 60 . Most patients acquired biochemical remission and disappearance of clinical symptoms after unilateral adrenalectomy. But some patients reappear with symptoms of hypercortisolism after surgery, usually less severe than at the time of presentation. And most patients with recurrence undergo contralateral resection, while some are treated with medication. A 16-year-old female patient with complaints of weight gain and irregular menstruation had a preoperative iodocholesterol scintigraphy showing unilateral uptake of the left adrenal gland. She underwent left adrenalectomy. After 10 months of follow-up, she had significant weight loss, disappearance of the features of CS and biochemical remission 51 . Xu et al. reviewed 13 PPNAD patients who underwent unilateral adrenalectomy, of whom only 1 patient recurred with a requirement of contralateral adrenal resection, while the rest achieved remission (Median follow-up time: 47 months) 13 . Kyrilli et al. summarized the patients with unilateral adrenal resection in the literature. There were 24 cases with unilateral adrenalectomy, followed by contralateral resection in 5 cases, with the duration of contralateral resection ranging from 2 months to 25 years 14 . Cohen et al. reported a case of successful pregnancy after unilateral adrenal resection without adrenal hypofunction during pregnancy, indicating that unilateral adrenal resection may be considered as an option for women with fertility needs 59 . There were two 15-year-old patients with complaints of weight gain and growth retardation, with typical manifestations of hypercortisolism on examination (central obesity, hirsutism, purplish streaks, facial acne). They underwent unilateral adrenalectomy. 5–6 months postoperatively, they had significant clinical and biochemical improvement, weight loss, accelerated growth, and regression of skin streaks and acne. One required HC replacement for a short period of time and one not required HC replacement. Then they reappeared with CS symptoms and laboratory tests suggesting loss of cortisol circadian rhythm, so contralateral adrenalectomy was performed at 8 and 10 months postoperatively 61 . Bilateral adrenocortical hyperplasia (BAH) mainly includes Primary Bilateral Macronodular adrenocortical hyperplasia (PBMAH) and PPNAD 62 . Studies have shown that unilateral adrenalectomy improves clinical symptoms and biochemical status successfully for PBMAH for adult patients, particularly for patients with asymmetric hyperplasia and mildly phenotypes 62 63 64 . Similar to PBMAH, PPNAD is often manifested as mild to moderate CS. That suggested that unilateral resection for PPNAD may also be a feasible approach. Some patients have bilateral adrenal nodular hyperplasia with the presence of macronodules. Research studies of Vezzosi et al. about Adrenal [6β- 131 I]-iodomethyl-19-norcholesterol] (NP-59) scintigraphy revealed that asymmetrical adrenal uptake associated with macronodules in 10 of 17 patients (59%) 37 . In those patients with unilateral greater nodular hyperplasia, selective resection of the side with greater nodules may be a good option. There are twins female patients with PPNAD from Belgium who under unilateral adrenalectomy have ongoing clinical and biochemical remissions without any adrenal insufficiency after 3 years and 18 months follow-up after surgery, respectively 14 . Unilateral adrenalectomy was chosen because the symptoms were mild and asymmetric bilateral adrenal uptake or size was shown on imaging ( 131 I iodomethyl-norcholesterol scintigraphy coupled with single-photon emission computed tomography (SPECT)/CT Adrenal CT scan) 14 . It seems unilateral adrenalectomy can relieve the symptoms of mild to moderate CS without causing adrenocortical insufficiency. When NP-59 scintigraphy or (SPECT)/CT scan revealed asymmetrical adrenal uptake 37 , selective resection of the side with prevalent uptake may be a good option 14 . But scintigraphy is not available in many countries. Adrenal volume measurement, adrenal vein sampling are also modalities that can substitute scintigraphy. Clinicians should also consider the possibility that unilateral resection may not be a complete cure and may require a second operation. There is also a case showed that adrenal crisis occurred during a viral infection 3 weeks after unilateral adrenalectomy 50 . But we thought the probability for adrenal crisis of unilateral adrenalectomy is much lower than bilateral resection. Most adrenalectomies are performed by laparoscopy with less trauma. For patients who have the possibility of remission after resection of one side, they and their doctors may prefer to remove one side first and then remove the other side after recurrence. Compared with permanent adrenal insufficiency caused by bilateral adrenal resection, it may be better to selectively remove one side of the adrenal gland for patients with atypical or mild symptoms. In addition, genotypic - phenotypic correlation indicated that some gene mutation sites were associated with milder phenotypes, which may be a basis for determining surgical approaches, but further studies are needed 21 . Therefore, we suggested that clinicians should select unilateral or bilateral adrenal resection based on a comprehensive evaluation of the degree of increased cortisol, symptoms, preoperative imaging evaluation, and the patient’s willingness, to choose the surgical procedure with the best benefit. Due to the rarity of PPNAD, clinical studies about the treatment were limited, the indication and long-term efficacy of unilateral adrenal resection remained to be studied. If further studies could confirm the indications for unilateral adrenal resection, it may greatly improve the quality of life of patients without long-term hormone replacement on the basis of disease control.
There are several kinds of drugs to treat hypercortisolism as conservative managements. These include steroidogenic inhibitors (Metyrapone, Mitotane, Etomidate, LCI699) and glucocorticoid receptor antagonists (Mifepristone) 65 . They can be used as preoperative transition for patients with severe hypercortisolemia or maintenance therapy for patients who refuse surgery 65 . Medication is also needed for patients who are not cured completely after surgery 44 . If patients have severe hypercortisolemia preoperatively, they should receive adrenolytic medications for weeks preoperatively 12 . In the literature, Ketoconazole can as a bridge therapy before surgery 8 25 . It is worth noting that Ketoconazole has potential side effects such as liver enzyme elevation, gastrointestinal discomfort, androgen reduction and pruritus 66 . Therefore, it is not generally preferred for men, and it should be used under liver enzyme monitoring 67 . Ketoconazole treatment has been used successfully in several cases of CS pregnancy and did not cause birth defects to the fetus 68 69 . Fluconazole has been considered as an alternative to Ketoconazole 70 . Navarro et al. reported case series in which a patient showed a significant decrease in urinary cortisol after 3 months of Fluconazole treatment. But he discontinued due to the side effects of pruritus and facial rash; Another patient remained on low-dose Metyrapone as maintenance therapy after unilateral adrenalectomy 44 . As for a patient who refused surgery, long-term and low-dose mitotane therapy (from 0.5 g/d to 4 g/d) was an effective method to correct hypercortisolism. The typical symptoms of Cushing’s syndrome and hyperandrogenism gradually subsided at two months from the beginning of the treatment. The size of the adrenal glands appeared to be reduced after 7 months adrenolytic treatment. The patient was still in remission after 122 months follow-up 16 . Besides, Osilodrostat as an oral 11β-hydroxylase (CYP11B1) inhibitor, catalyzes the final step of cortisol synthesis. And it is administered less frequently than conventional drugs, with fewer adverse effects and fewer interactions with other drugs 71 72 . Phase III clinical trial results have confirmed its safety and efficacy in adult CS patients, and it may be a new option for the treatment of PPNAD.
The pathogenic mechanisms of PPNAD are still not clear up to now, a possible mechanism is that genetic events lead to structural activation of cAMP/PKA (cyclic adenosine monophosphate/protein kinase A) signal pathway 73 74 . The PKA consists of two molecules of regulatory (R) subunits bound to two molecules of catalytic (C) subunits. PRKAR1A as a tumor-suppressor gene located at 17q22–24 encodes the type 1a regulatory subunit of cAMP-dependent PKA 73 75 . Inactivation mutation of PRKAR1A cause premature stop codons through the nonsense-mediated mRNA decay (NMD). This process would lead to a truncated protein product which contributes to increased signal by PKA [76 . In addition, mutations in the phosphodiesterase PDE11A 45 and PDE8 and the PKA catalytic subunit PRKACA gene 77 are also detected in patients with PPNAD, and all of these gene events lead to structural activation of the cAMP/PKA signaling pathway, resulting in high glucocorticoid secretion independent of ACTH. There are a number of drug candidates currently under study that target possible mechanisms of the disease, such as Rapamycin, Celecoxib, tryptophan hydroxylase inhibitors ( Fig. 1 ). These drugs have been proven in animal or in vitro tests, but there are no clinical studies have been reported.
The mammalian target of Rapamycin sensitive complex 1 (mTORC1) pathway was activated by PKA signaling in adrenal glands of specific PRKAR1A knockout mouse and human PPNAD tissues, leading to increased cell survival. PKA/mTOR activation was correlated with BAD (a member of the BCL-2 apoptotic family) hyperphosphorylation which may lead to apoptosis resistance and tumor formation. Rapamycin is an effective and selective mTOR inhibitor that acts on the PKA/mTOR pathway. Treatment with Rapamycin specifically sensitized ADKO adrenal cortical cells to dexamethasone-induced apoptosis in animal experiments, indicating its potential efficacy in the treatment of adrenal hyperplasia 78 79 .
In specific PRKAR1A knockout mice, Sahut et al. observed that the atypical hyperplasia of fetal-like cortex at the corticomedullary junction. And such hyperplasia extended to the periphery over time. Fetal-like adrenal cortical cells (FLACs) caused by the lack of R1a cannot be clear out and then morbid hyperplasia of adrenal cortical 80 . Celecoxib is a prostaglandin peroxidase synthase 2 (PTGS2) [also known as cyclooxygenase 2 (COX2)] inhibitor that is currently used to inhibit the growth of certain tumors. It is mainly related to the inhibition of prostaglandinE2 (PGE2), and the reduction of stem cell-like cells induced by this molecule 19 . Celecoxib can effectively reduce the proliferation of adrenal cortical cells induced by FLACs and thereby reduce glucocorticoid production, which has been verified in mice and human adrenal cells in vitro 81 .
Somatostatin (SST) analogues (SSA) like Octreotide can reduce intracellular cAMP production and thus may reduce cortisol secretion in PPNAD and other ACTH-independent CS patients. A short-term preliminary clinical study on the application of Octreotide in patients with PPNAD showed that the expression levels of somatostatin receptors (SSTRs) in PPNAD tissues were significantly higher than those in normal adrenal glands. But short-acting SSA Octreotide had no significant effect on cortisol secretion in patients with PPNAD. However, because of the sample size of this study was small, only Octreotide was used and further SSA-related studies with larger sample sizes may be possible in the future to further explore its effect on PPNAD 18 .
Bramet et al. found that adrenal tissues removed from PPNAD patients overexpress the key enzyme tryptophan hydroxylase type 2 (TPH2) and the 5-hydroxytryptamine receptors (5-HT4R, 5-HT6R and 5-HT7R) because of cAMP/PKA pathway activation, leading to an illicit serotonergic stimulatory loop associated with hypercortisolemia 17 . Later, Le Mestreet al. confirmed this by studying adrenal tissues exposed to high plasma ACTH levels for a long time and found that TPH and/or 5-HT4/6/7 receptors were overexpressed in tissues of different disease types. In addition, they found that 5-HT4R antagonists can reduce the stimulatory effect of 5-HT in vitro. It is suggested that tryptophan hydroxylase inhibitors may also be a promising drug for the treatment of PPNAD 82 . Drug therapy is commonly used as a transition treatment preoperatively for patients with severe hypercortisolemia currently 65 . The drugs targeting the specific mechanism of PPNAD are very promising. But they only have been proven in animal or in vitro tests. We expect these drugs to be proven clinically and look forward to the clear indications for drug therapy. Although there are still many problems to be solved in the treatment of PPNAD, we proposed a brief treatment strategy for PPNAD by summarizing the literature ( Fig. 2 ). We look forward to more studies that will tell us more about specific treatments.
Using “PPAND” and “pregnancy” as key words, we searched English literature published before May 2021 in PubMed database and found 3 cases with PPNAD during pregnancy 83 84 85 . There are 2 patients received Metyrapone to control hypercortisolemia during pregnancy 83 84 . All the 3 patients were successfully delivered by cesarean section, and the causes of delivery included preeclampsia and recurrent vaginal bleeding. All the infants survived after birth, and two infants developed respiratory distress syndrome and transient hyponatremia ( Table 1 ). Hypercortisolism in PPNAD may be related to the high estradiol level during pregnancy. Catichaet.al. observed that estradiol can stimulate cortisol secretion in a dose-dependent manner with the absence of ACTH in vitro of PPNAD cells 86 . Most drugs in the hypercortisolism treatment are contraindicated during pregnancy 87 88 . Metyrapone is an 11β-hydroxylase inhibitor that has been used safely during pregnancy 65 89 . It can reduce serum cortisol levels, but adrenocorticotropic hormone (ACTH) is stimulated with the decreased cortisol levels, which increases the production of mineralocorticoid, possibly leading to hypertension. This point should be vigilant in clinical practice 89 . Due to the limited evidence reported in the literature, it should be used cautiously. Due to high blood pressure, diabetes, weight gain and skin purple lines may also associate with pregnant, the diagnosis of CS during pregnancy is challenging. In addition, the physiological changes during pregnancy can also include the increase of serum cortisol and urine free cortisol 89 . However, the loss of circadian rhythm of cortisol secretion and the significant increase of 24-hour urine free cortisol level are still helpful for the diagnosis of CS. If severe preeclampsia is diagnosed and blood pressure is difficult to control, pregnancy or delivery may need to be terminated promptly. In patients with a history of bilateral adrenal resection, hormone replacement must be monitored, and a stress dose of glucocorticoids administered at delivery. Unfortunately, there is no data on long-term follow-up of mothers and fetuses after completion of delivery of PPNAD. We have no idea about long-term remission. In conclusion, the pregnancy decision of PPNAD patients requires multidisciplinary cooperation. Due to the genetic characteristics of PPNAD and CNC, the fetus should be screened for genetic diseases after birth and a detailed follow-up strategy should be developed to detect and manage the possible related diseases at an early stage.
PPNAD is a rare disease with atypical symptoms and difficult clinical diagnosis. And the exact mechanism remains unclear. Surgery is the treatment of PPNAD, while the appropriate surgical methods should be selected according to individual circumstances. For patients who cannot receive surgical treatment or only have mild hypercortisolemia, drug treatment can be candidate. The drugs that may have therapeutic potential for PPNAD, such as tryptophan hydroxylase inhibitor, 5-HT receptor antagonist, COX2 inhibitor celecoxib and somatostatin, still need further clinical studies to confirm. Furthermore, we should pay attention to the management of PPNAD in children and pregnancy, strengthen interdisciplinary teamwork, and long-term follow-up should be conducted no matter what treatment plan is selected. |
PMC9649303 | Aozheng Chen,Min Xu,Jing Chen,Tingting Chen,Qin Wang,Runjie Zhang,Jin Qiu | Plasma-Based Metabolomics Profiling of High-Risk Human Papillomavirus and their Emerging Roles in the Progression of Cervical Cancer | 03-11-2022 | High-risk human papillomavirus (HR-HPV) is the main etiological factor for cervical cancer. Accumulating evidence has suggested the active role of metabolites in the initiation and progression of cancers. This study explored the plasma metabolic profiles of HPV-16 positive (HPV16 (+)), HPV-18 positive (HPV18 (+)), and HPV negative (CTL) individuals using a nontargeted metabolomics approach. C8 ceramide-1-Phosphate (d18 : 1/8 : 0) was found to inhibit cervical cancer cell proliferation and migration in vitro, evidenced by CCK8 experiments, a cell migration test, RT-qPCR, and western blotting. The underlying mechanism demonstrated that C8 inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. These findings may contribute to the clinical treatment of HR-HPV-induced cervical cancer by intervening in its initiation and progression. | Plasma-Based Metabolomics Profiling of High-Risk Human Papillomavirus and their Emerging Roles in the Progression of Cervical Cancer
High-risk human papillomavirus (HR-HPV) is the main etiological factor for cervical cancer. Accumulating evidence has suggested the active role of metabolites in the initiation and progression of cancers. This study explored the plasma metabolic profiles of HPV-16 positive (HPV16 (+)), HPV-18 positive (HPV18 (+)), and HPV negative (CTL) individuals using a nontargeted metabolomics approach. C8 ceramide-1-Phosphate (d18 : 1/8 : 0) was found to inhibit cervical cancer cell proliferation and migration in vitro, evidenced by CCK8 experiments, a cell migration test, RT-qPCR, and western blotting. The underlying mechanism demonstrated that C8 inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. These findings may contribute to the clinical treatment of HR-HPV-induced cervical cancer by intervening in its initiation and progression.
Cervical cancer is the fourth most common malignant tumor in women, and it results in over 300000 deaths worldwide each year [1, 2]. Cervical cancer is a multifactorial, complex multistep process related to the activation of protooncogenes or the inactivation of tumor suppressor genes. Almost 99.7% of cervical cancers in women are attributable to human papillomavirus infections [3]. Persistent infection with high-risk human papillomavirus (HR-HPV) is considered as the most vital epidemiologic risk factor for cervical cancer, and HPV types 16 and 18 contribute to over 70% of cervical cancer [4]. Moreover, HR-HPV leads to the dysfunction of basal epithelial cells and the occurrence of cervical cancer [5]. Tumor cells reprogram their metabolism to support cell growth, proliferation, and differentiation, thus, driving cancer progression [6]. Previous studies indicated that metabolites are the final products of various biological processes and maybe the biomarkers reflecting upstream events, such as environmental influences and genetic mutations [7]. Therefore, metabolomic variations are considered ideal biomarkers for the screening and diagnosing cancers [8, 9]. Metabolites are defined as biologically active metabolites by metabolomics [10]. Emerging studies have highlighted the functional role of metabolites in physiology and disease. A previous study indicated that mTOR kinase6 acts as an active entity in cell nutrients and energy [11], and an additional study revealed that α-ketoglutarate activates macrophages and regulates immunity [12]. Abnormal accumulation of fumarate and succinate, termed oncometabolite, causes potential transformation to malignancy [13]. Furthermore, metabolites such as lipids, amino acids, and bile acids regulate insulin sensitivity [14]. Moreover, lysophosphatidic acid can mediate ovarian cancer cell migration and metastasis by activating the AMPK pathway [15]. Likewise, HPV infections drive metabolic modifications, so altered metabolites may be potential markers for predicting the risk of cervical cancer [16, 17]. Numerous significant metabolites were elevated in plasma from both cervical dysplasia and cervical cancer in previous reports [16, 18]. However, the changes in plasma bioactive metabolites caused by HPV16/18 infection and their underlying mechanisms in cervical cancer initiation and progression remain largely unknown. This article aimed to explore the difference in the global metabolite expression profiles between HR-HPV and the healthy control group with liquid chromatography-mass spectrometry (LC-MS). In addition to analyzing the global profiling between HR-HPV and the healthy control group, we aimed to validate further how these identified metabolites intervene in the oncogenic capacity of HR-HPV in vitro.
For this study, only individuals with either HPV16 or HPV18 positive and healthy controls were included. Clinical samples were randomly collected from female patients seen from July to September 2020 at the Gynecological Department, Tongren Hospital. This study was approved by the Ethics Committee of Tongren Hospital, Shanghai Jiao Tong University. (No. 2018-049-01) and recruited 20 patients with an HPV16-positive or HPV18-positive diagnosis and 10 healthy controls following a Thinprep Cytology test (TCT). Of the 10 female patients who tested HPV16 positive, 7 were diagnosed with chronic cervicitis, and the 3 had low-grade squamous intraepithelial lesions (LSILs). Of the 10 female patients who tested HPV18 positive, 5 had chronic cervicitis, 1 had LSIL, 2 had a high-grade squamous intraepithelial lesion (HSIL). The 10 healthy controls did not present any precursor lesion. All participants aged between 20 and 45 years old were not pregnant or experiencing menstruation (Table 1). All individuals were thoroughly informed and granted written informed consent prior to blood sampling.
Trained gynecologists collected all the blood samples in the anticoagulant tubes following the standard hospital protocol and then centrifuged at 3000 rpm for 15 min at 4°C to collect plasma. Afterward, the supernatant was transferred into 1.5 mL tubes and stored at -80°C as plasma samples for liquid chromatography-mass spectrometry (LC-MS) detection before use. Each frozen plasma sample was thawed at room temperature. Approximately 100 μL of the supernatant sample was added to 10 μL of 2-chloro-L-phenylalanine (0.3 mg/mL) and Lyso PC17:0 (0.01 mg/mL diluted in methanol) as an internal standard and then vortexed for 10 s. Precold methanol and acetonitrile (1/2, v/v) were mixed, added to the sample, and then vortexed for 1 min. Samples were ultrasonically extracted in an ice water bath for 10 min and held at -20°C for 30 min. After centrifuging at 13000 rpm at 4°C for 10 min, 300 μL of the supernatant was transferred into an LC-MS vial and evaporated to dryness. Next, 400 μL of methanol and water (1/4, v/v) were applied to each sample, vortexed for 30 s, held at 4°C for 2 min, and then stored at -20°C. After 30 min, samples were centrifuged at 13000 rpm at 4°C for 10 min. The supernatants were aspirated using syringes, filtered through 0.22 μm microfilters, transferred to LC vials.
We performed nontargeted metabolomics with LC-MS in the plasma samples to identify altered metabolites in the HPV16 positive, HPV18 positive, and healthy control groups. Metabolic profiles in both electrospray ionization (ESI)-positive and ESI-negative ion modes were generated using an ACQUITY UPLC I-Class system (Waters Corporation, Milford, USA) coupled with an AB SCIEX Triple TOF 5600 System (AB SCIEX, Framingham, MA, USA). The binary gradient elution systems consisted of water containing 0.1% formic acid (v/v, A) and acetonitrile containing 0.1% formic acid (v/v, B). Separation was achieved using the following conditions: 20% B for 2 min; 60% B for 4 min; 100% B for 11 min; 100% B for 13 min; 13.5 5% B for 13.5 min, and a final 5% B for 14.5 min. All the samples were analyzed at 4°C. The injection volume was 2 μL. Data acquisition was performed in full scan mode (m/z ranges from 70 to 1000) combined with information-dependent acquisition (IDA) mode at collision energies of 10 eV (+) and -10 eV (-). The mass spectrometry parameters were as follows: ion source temperature of 550°C (+) to 550°C (-) throughout the acquisition; ion spray voltage from 5500 V (+) to 4500 V (-); curtain gas at 35 PSI; decluttering potential was set between 100 V (+) and -100 V (-); and interface heater temperature from 550°C (+) to 600°C (-). For the IDA analysis, the range of m/z was set to 25-1000, and the collision energy was 30 eV.
Metabolites were identified and analyzed using public databases (http://www.hmdb.ca/;http://www.lipidmaps.org/) and self-built databases. The distinct tendency among groups was analyzed using principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models. Statistical significance was observed at variable importance in projection (VIP) value > 1 and P value < 0.05. In addition, qualitative and metabolic pathway analyses of differential metabolites were investigated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) online database. Furthermore, the human metabolome database (HMDB) IDs and KEGG IDs of the metabolites were entered into the ingenuity pathway analysis (IPA) server (IPA, Ingenuity® Systems, http://www.ingenuity.com) to analyze networks between metabolites.
C8 ceramide-1-phosphate (d18 : 1/8 : 0) (#860532P) was purchased from Sigma-Aldrich (St. Louis, MO, USA), and C18 ceramide-1-phosphate (d18 : 1/18 : 0) was synthesized by chemical method. The 50 mM stock solutions of C8 and C18 were prepared in dimethyl sulfoxide (DMSO). These solutions were then diluted to 0, 5, 10, 20, and 30 μM before adding to the cultures, with the DMSO concentration maintained below 0.1%. Next, the same amount of DMSO was added to the controls.
Two human cervical cancer cell lines HeLa (ATCC CCL-2) and GH354 (ATCC CRL-13003) were purchased from ATCC (Manassas, VA) and cultured in DMEM (Dulbecco's modified Eagle's medium) containing 10% fetal bovine serum (Gibco, New Zealand) and 500 μL penicillin-streptomycin (Gibco, USA) following the manufacturer's instructions. All cells were cultured in a 37°C incubator with 5% CO2 under aseptic conditions. The growth of the cells was observed daily and the medium was changed according to the growth of cells. The cells were digested with 0.05% trypsin (ScienCell, USA) for inoculation when fused to 80%.
CCK8-assay was employed to evaluate the effect of the metabolites on the proliferation of cervical cancer cells. Cultured HeLa and GH354 cells suspended in a 100 μL culture medium with 10% FBS were inoculated in a 96-well plate (3000 cells/well) with 0, 5, 10, 20, and 30 μM C8 and C18. The cells with different concentrations of C8 and C18 were incubated for 24 hrs, 48 hrs, and 72 hrs. Before measuring the absorbance at 450 nm wavelength using a microplate reader, 10 μL CCK8 (TargetMOL, China) solution was added to each well and incubated for 1 hour.
Different groups of cultured cells were plated in a 6-well plate with 1000 μL of serum-free medium per well along with C8. After C8 intervention, the center of the 6-well plate bottom was cross-marked with a black marker pen. Then, one scratch was generated per well. Starting from the scratch area marking the midpoint, the microscope ruler selected the scratch area within 1.00 mm as the photographic area at an equal distance. The photographs were taken at 0 and 72 hours under an inverted microscope. The scratch area was calculated using Image J image analysis software, and the healing rate = (0 h scratch area − current scratch area)/0 h scratch area × 100%.
The cells with or without C8 intervention were used to isolate total RNA using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer's instructions and reverse transcribed using HiScript®II QRT SuperMix (Vazyme, Nanjing, China) for reverse transcription-quantitative PCR (RT-qPCR) to obtain cDNA chains. The target genes were amplified in three replicates using the SYBR Green PCR Master Mix (Vazyme, Nanjing, China). The primers specific for E-cadherin, N-cadherin, Vimentin, MMP9, and GAPDH were as follows: E-cadherin primers (E-cadherin F: ATTTTTCCCTCGACACCCGAT; R: TCCCAGGCGTAGACCAAGA), N-cadherin primers (N-cadherin F: AGCCAACCTTAACTGAGGAGT; R: GGCAAGTTGATTGGAGGGATG), Vimentin primers (Vimentin F: TGCCGTTGAAGCTGCTAACTA; R: CCAGAGGGAGTGAATCCAGATTA), MMP9 primers (MMP9 F: AGACCTGGGCAGATTCCAAAC; R: CGGCAAGTCTTCCGAGTAGT), and GAPDH primers (GAPDHF: ACAACTTTGGTATCGTGGAAGG; R: GCCATCACGCCACAGTTTC). The RT-qPCR procedures were as follows: 95°C for 5 seconds, followed by 40 cycles at 95°C for 10 s, and 60°C for 30 s. Quantified mRNA was normalized to GAPDH as a control. The relative expression of mRNA was determined by the 2-ΔΔCT method.
A total of 5-10 mg of cells with or without C8 intervention extract were used for western blot analysis. Protein was extracted using protease inhibitors (Thermo Fisher Scientific, Waltham, MA, USA) and centrifuged to remove the cell pellet. The samples were heat-denatured at 95°C for 5 minutes with 5× SDS-PAGE loading buffer and fractionated on 10% SDS-PAGE gels (Bio-Rad, Hercules, CA, USA). GAPDH (HRP-60004, Proteintech, Manchester, UK) was used as a standard control protein. Following electrophoresis, proteins were transferred to a nitrocellulose membrane and blocked with 5% skim milk powder. The membranes were incubated at 4°C overnight with anti-E-cadherin (1 : 4000, 60335-1-lg, Proteintech, Manchester, UK), anti-N-cadherin (1 : 5000, 66219-1-lg, Proteintech, Manchester, UK), anti-MMP9 (1 : 500, 10375-2-AP, Proteintech, Manchester, UK), anti-Vimentin primary antibodies (1 : 2500, 60330-1-lg, Proteintech, Manchester, UK), anti-JNK1 (1 : 1000, 66210-1-lg, Proteintech, Manchester, UK), anti-Phospho-JNK1 (1 : 2000, 80024-1-RR, Proteintech, Manchester, UK), anti-ERK1/2 (1 : 1000, 16443-1-AP, Proteintech, Manchester, UK), anti-Phospho-ERK1/2 (1 : 2000, 28733-1-AP, Proteintech, Manchester, UK), anti-Bax (1 : 5000, 50599-2-lg, Proteintech, Manchester, UK), and anti-Bcl2 (1 : 1000, BF9103, Affinity Biosciences, Changzhou, China). Goat anti-rabbit IgG (1 : 5000, SA00001-2, Proteintech, Manchester, UK) and Goat anti-mouse IgG (1 : 5000, SA00001-2, Proteintech, Manchester, UK) horseradish peroxidase conjugated secondary antibodies were incubated with the membranes at 25°C for 1 h. Densitometric analysis was performed to quantitate western blot results using computerized image software (ImageJ).
The univariate analysis of variance (ANOVA) quantified the differences between the HR-HPV infected and CTL groups with GraphPad Prism 6.0. The result was presented as the mean ± standard error (SE). P values were determined by one-way analysis of variance (ANOVA) with Tukey's post hoc correction for multiple group comparisons. All data analyses were processed using GraphPad Prism, version 6.0 (GraphPad Software, San Diego, CA). A two-sided P value of <0.05 was considered statistically significant. Significance was indicated as follows: ∗P < 0.05, ∗∗P < 0.01, ∗∗∗P < 0.001, and ∗∗∗∗P < 0.0001.
To examine the differences in metabolites in the CTL and HR-HPV infection groups, we conducted a multivariate assessment and OPLS-DA analysis. After the PCA model was established, a separation tendency was observed between the CTL group and the HPV16 (+) (Figure 1(a)) or HPV18 (+) group (Figure 1(b)). OPLS-DA models were obtained with principal predictive components and principal orthogonal components. OPLS-DA removed unassociated data from the dataset and verified the metabolic profile dissolution between groups (Figures 1(c) and 1(d)). We next performed a permutation examination of the OPLS-DA model. The R2 and Q2 intercept values were (0.0, 0.893) and (0.0, -0.285) between the CTL and HPV16 (+) infection groups, respectively (Figure 1(e)), and (0.0, 0.862), (0.0, -0.321) between the CTL and HPV18 (+) infection groups, respectively (Figure 1(f)).
Overall, 7696 differentially expressed metabolites were detected by LC-MS, and the details of all the metabolites are provided in Supplementary Table 1. Among these differential metabolites, a total of 88 significantly differential metabolites with VIP > 1 and P < 0.05 were identified in the HPV16 (+) group, 31 of them were significantly upregulated, and 57 metabolites were significantly downregulated (Figure 2(a)). On the other hand, 101 significant differentially expressed metabolites were identified in the HPV18 (+) group, 26 of them were significantly upregulated and 75 metabolites were significantly downregulated (Figure 2(b)). Further pathway analysis (P < 0.05) revealed that most enriched metabolic pathways were in the high-risk groups (Figures 2(c) and 2(d)), and this indicated that HPV16 and HPV18 might have similar metabolic functions in the initiation and progression of cervical cancer. To evaluate similarities and differences in the datasets, we compared the number of shared and unique significant differentially expressed metabolites among the three groups by a Venn diagram. The Venn diagram illustrated 24 significant differential metabolites shared by HPV16 and HPV18 groups (Figure 2(e)), and all these 24 significant differential metabolites (P < 0.05) were presented in Table 2. They were roughly categorized into twelve distinct classes: amino acids and peptides, monosaccharides, glycerophosphocholines, steroid conjugates, fatty acid moieties, organooxygen compounds, ceramide-1-phosphate, carboxylic acids and derivatives, diarylheptanoids, coumarins and derivatives, benzene derivatives, and diarylpropanoids (Figure 2(f)). Among these classes, ceramide-1-phosphate draw our attention, as there has been an increasing body of evidence indicating ceramide-1-phosphate regulates cell proliferation, apoptosis, migration, and other life processes as well as invasion, metastasis, and clinical outcome of pancreatic cancer, lung cancer, and breast cancer [19–23]. Hence, we selected C18 Ceramide-1-Phosphate (d18 : 1/18 : 0) and C8 Ceramide-1-Phosphate (d18:1/8 : 0) to verify their biological functions. These two ceramide-1-phosphates expressed differentially in both HPV16 and HPV18 groups and were detected by LC-MS (Table 3).
We conducted the assays to examine the proliferation and migration potency of C8 and C18 in HeLa and GH354 cervical cancer cell lines, C8 presented significant biological function at the concentration of 30 μM. The proliferation rate of HeLa and GH354 cells treated with C8 of 30 μM decreased, while C18 had no effect, based on CCK8 assay results (Figures 3(a)–3(d)). Comparing the migration index and healing rate of C8 (concentration of 30 μM) treated cell lines and the control group at 0 and 72 hours revealed that the migration index and healing rate significantly diminished in the C8 group (Figures 3(e)–3(h)). To verify the observed effects of C8, we analyzed the expression levels of several molecules closely related to malignant behavior, especially epithelial-to-mesenchymal transition (EMT), by western blotting and RT-qPCR experiments. The RT-qPCR analysis revealed the changes in E-cadherin, N-cadherin, Vimentin, and MMP9 expression after C8 (concentration of 30 μM) intervention in HeLa and GH354 cells. Specifically, E-cadherin was upregulated after C8 intervention in both HeLa and GH354 cells, while N-cadherin and Vimentin were downregulated after C8 intervention in HeLa cells (Figures 3(i) and 3(j). Western blot and densitometric analysis verified the changes in EMT again at the protein level. Figure 4 presented significantly upregulated E-cadherin and downregulated N-cadherin after C8 intervention in HeLa and GH354 cells.
Differential metabolites were imported into IPA software for further biological pathway prediction to reveal potential targets and mechanisms. The results indicated that these differential metabolites had a close association with PI3K/AKT signaling, mTOR signaling, PTEN signaling, and specific lipids were associated with MAPK signaling, TGF-β signaling, and PLA2G2A regulation (Figure 5). Thus, whether C8 intervention modified the expression of molecules in this pathway was then explored by western blot. After C8 intervention, JNK1 remained at the same expression level, but P-JNK1 decreased. In addition, the expression level of Bax was upregulated, while Bcl2 was downregulated after C8 intervention. These results have suggested that C8 may exert biological functions by inhibiting the MAPK signaling pathway (Figures 4).
HR-HPV is a potent human carcinogen, and persistent HR-HPV infection is a necessary risk factor for the cervix and cervical cancer [24]. It induces epithelial cell malignant transformation and suppresses the immune response by encoding oncoproteins [25]. For instance, the E6 and E7 proteins encoded by HR-HPV could promote cervical cancer [26, 27], the E6 protein encourages the growth of cervical cancer cells by targeting the P53 protein [28], and the E7 protein immortalizes human epithelial cells by targeting the pRb protein [29]. Rapid proliferation is a driving force for the massive energy required by malignant cells to adapt to metabolic modifications [30]. Driven by oncoproteins, metabolites can be used to characterize the molecular mechanisms of HR-HPV comprehensively [30]. In addition, the abnormal expression or activation of metabolic pathway-related enzymes is tightly associated with the occurrence of cancers [31]. Previous studies revealed that E6/E7 could also regulate the glycolytic pathway via elevated expression of hexokinase-II and act as a promotor in HPV-associated cervical lesions in serum [32]. With the rapid development of metabonomics technology, the function of metabolites has been identified [33]. Metabolites, as indicators are valuable for identifying biomarkers of cervical cancer. Studies have reported that in the study of metabonomics about cervical cancer, samples were blood (serum or plasma), vaginal secretions, tissues, and urine [34]. A study reported that combination of lysophosphatidylcholine (17 : 0), n-oleoyl threonine, bilirubin, tetracosahexaenoic acid (lysoPC(17 : 0)), and 12-hydroxydodecanoic acidare are satisfactory candidate biomarkers for cervical cancer diagnosis from cervical cancer plasma samples [35]. In a lipidomics study profiling of plasma, Nam et al. showed that compared to healthy controls and patients with CIN1, phosphatidylcholine, phosphatidylethanolamine, diglyceride, and free FAs are major lipid classes with significant differences in patients with CIN2/3 and cervical cancer [17]. The researchers retrieved plasma samples for nontargeted metabolome analysis and screened AMP, aspartate, glutamate, hypoxanthine, lactate, proline, and pyroglutamate to construct a linear prediction model combined with positive HPV status were correlated with substantial risk of cervical cancer [18]. The metabolic modulations by HPV infection is inextricably linked to cervical cancer progression [36, 37]. However, the metabolic profile in response to HPV16/18 infection has not yet been elucidated. To further explore the potential biological functions of these metabolites with HR-HPV infection and its carcinogenic effects, nontargeted metabolomics analysis was executed to identify differential metabolic signatures between the HPV16 (+), HPV18 (+), and CTL groups. Among the differential metabolites shared by the HR-HPV infection and the control groups, the ceramide-1-phosphate extensively discussed in the cancer treatment aroused our attention. The ceramide-1-phosphate belongs to the ceramide phosphate (CerPs), they are members of the sphingolipids class and a component of eukaryotic cell membranes. They act as a bioactive lipid in apoptosis, inflammation, cell cycle arrest, and the heat shock response [14]. CerPs are transformed from ceramides by a specific ceramide kinase (CerK), and they can be dephosphorylated by phosphatidate phosphatase back to the ceramide. These CerPs are able to regulate cell proliferation, apoptosis, and migration [19, 20]. In addition, an increasing number of papers elucidated the clinical potential of the CerPs in cancer treatment. Ceramide synthase 2-C-ceramide axis was reported to limit the metastatic potential of ovarian cancer cells [38]. Ceramide-1-phosphate promoted stem cell mobility and enhanced cell migration and invasion of pancreatic cancer [21, 39]. Also, the CerPs in breast cancer patients were correlated with invasion and metastasis [23]. Moreover, the biological functions of Ceramide-1-phosphate vary depending on the side chain length and isoforms. Research shows that short-chain C2-ceramide-1-phosphate-or C8-ceramide-1-phosphate induces Ca2+ mobilization in CAPE cells, thyroid FRTL-5, or Jurkat T cells, but not in fibroblasts or neutrophils [40]. Long-chain C16-ceramide-1-phosphate fails to alter Ca2+ concentration in A549 cells [41]. Ceramide-1-phosphate with an acyl-chain of 6, 16, and 18 : 1 carbons efficiently activated cPLA2 in vitro, whereas C2-ceramide-1-phosphate failed to do so [42]. High-risk HPV E6 and E7 proteins can enter the host cell nucleus and are the key factors to maintain the malignant phenotype of HPV-positive cancer cells. HPV E6 and E7 proteins promote the migration, invasion, and epithelial-mesenchymal transition (EMT) abilities of cervical cancer cells through a series of action modes [43]. Therefore, we detected proliferation, migration, and EMT-related markers E-cadherin and N-cadherin by QPCR and WB. Our study indicated that CerPs were also implicated in cervical cancer progression, and C8 ceramide-1-phosphate (d18:1/8 : 0) was found to inhibit cell proliferation, migration, and malignant behaviour of cervical cancer in vitro. Metabolites serve as controllers of biological processes and phenotypes [10], and tumorigenesis may change the overall metabolism of the human body. Metabolomics can help capture altered biological processes, such as amino acids and lipid metabolites [44]. The bioactive metabolomics drives phenotype modulation by participating in life activity and exerting biological activities via competitive inhibition, posttranslational modifications, and signal transduction [45]. Oncometabolite accumulation is a causal process in malignant conversion that contributes to propagating cancer. To directly examine the possible mechanism underlying the observed phenotypic changes, we analyzed the expression levels of molecules in the signaling pathway. IPA analysis in this study revealed that differential metabolites in HR-HPV infection and CTL plasma were enriched in PI3K/AKT signaling, mTOR signaling, PTEN signaling, MAPK signaling, TGF-β signaling, and PLA2G2A regulation. Following HPV infection, the host cells of HPV infection are immortalized and transformed which will alter expression of multiple genes and activate several signalling pathways, especially the PI3K/Akt/mTOR signalling pathway [46]. Moreover, In HPV-positive cells, phosphorylation of p38 and MAPK AP kinase2 (MK2) proteins was induced along with relocalization to the cytoplasm confirming the p38/MK2 pathway as a key regulator of the HPV life cycle [47]. In addition, it has also been reported that HPV16 E6 and HPV18 E6 oncoproteins activate MAPK signalling pathway to promote cell proliferation by upregulating p-PI3K [48]. Our further western blot results indicated that the C8 intervention inhibited proliferation and migration in cervical cancer cells via the MAPK/JNK1 signaling pathway. This ceramide-1-phosphate identified by our study is closely associated with tumorigenesis and metabolic phenotype changes in the progression of CC. Notably, the nontarget metabolomics in our study also indicated a significant increase in LysoPC and PC, members of the fatty acid moieties class, in the HPV16 (+) and HPV18 (+) groups. This result suggested LysoPC and PC may also correlate with HR-HPV-induced oncogenesis, consistent with a previous study concluding PC and LysoPC were potential biomarkers for cervical cancer [49]. LysoPC originates from the cleavage of PC, the main component of oxidatively damaged low-density lipoprotein, was reported to aggregate inflammation and associated with the invasion, metastasis, and prognosis of tumors [24]. In addition, emerging evidence has indicated that enzymes participating in key lipid metabolism are potential therapeutic targets because they either inhibit lipid synthesis or stimulate their degradation [10]. For example, lactate dehydrogenase A was proven to be a key enzyme for lactic acid synthesis. It could also promote apoptosis leading to a decrease in the cell cycle when inhibited [50]. Furthermore, phosphoglycerate dehydrogenase, the enzyme of serine biosynthesis, has potential therapeutic value in lung adenocarcinoma [13]. The exciting consistency in the findings between our study and previous reports is worthy of more thorough research. In conclusion, HR-HPV infection causes changes in metabolite profiles in humans, and these metabolites may contribute to the process by which HPV16/18 infection induces cervical cancer. This paper is the first to identify C8 as an important lipid metabolite that modulates cervical cancer cell function. In addition, we also identified that other lipids, such as LysoPC and PC, may also involve cervical cancer progression, requiring further investigation. These results refresh our understanding of how bioactive metabolites modify the oncogenic potential of HR-HPV, and provide new insights into the mechanism of the oncogenic process of HPV16/18 infection. |
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PMC9649307 | Hui Wang,Yu Guo,Peipei Zhang,Zhijun Lin,Di Yang,Jiaohong Chen,Zhanzhan Li,Chi Zhang,Haoyu Yang,Binghui Yan,Zhimin Han,Chuntao Tian | Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer | 03-11-2022 | Background Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator. Methods We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR. Results Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased. Conclusion In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients. | Development of an Independent Prognostic Signature Based on Three Hypoxia-Related Genes for Breast Cancer
Hypoxia was considered to be a prognostic indicator in a variety of solid tumors. This study aims at identifying the hypoxia-related genes (HRGs) in breast cancer (BC) and the feasibility of HRGs as a prognostic indicator.
We downloaded the mRNA expression data of BC patients from TCGA and GEO databases. The LASSO Cox regression analysis was applied to screen the hub HRGs to establish a prognostic Risk Score. The independence of Risk Score was assessed by multivariate Cox regression analysis. And the immune checkpoint analysis was also performed. In addition, we also detected the expression level of hub HRGs in MCF-10A cells, MCF-7 cells, and SK-BR-3 cells by RT-qPCR.
Three HRGs were identified as hub genes with prognostic value in BC, including CA9, PGK1, and SDC1. The Risk Score constructed by these three genes could efficiently distinguish the prognosis of different BC patients and has been shown to be an independent prognostic indicator. In the high-risk group, patients had lower overall survival and poorer prognosis. In addition, the expression levels of five immune checkpoints (PD1, CTLA4, TIGIT, LAG3, and TIM3) in the high-risk group were significantly higher than those in the low-risk group. Moreover, the expression levels of PGK1 and SDC1 in BC cells were significantly increased.
In this study, we established an efficiently model based on three optimal HRGs (CA9, PGK1, and SDC1) could clearly distinguish the prognosis of different BC patients.
Breast cancer (BC) is one of most common malignancy in women, resulting in a severe decline in women's quality of life [1]. Among the malignant diseases, BC accounts for 23% of all cancer deaths, seriously threatening women's health [2]. Modern treatment for BC is multimodal, including surgery, radiation, and drug therapy; moreover, it has also been demonstrated that patients with early BC, locally advanced disease, and locoregional relapse could be cured [3]. Despite of advances in diagnosis and treatment of BC, approximately 12% of BC patients eventually developed tumor metastatic, and the 5-year survival rate was only 26% [4]. Therefore, identification of effective prognostic biomarkers contributes to developing personalized therapy and extending the scope of treatment for BC. Tissue hypoxia was one of the pathological characteristics of malignant solid tumors, leading to tumor progression and refractory treatment [5]. Moreover, hypoxia could directly (through inhibiting T cell proliferation and producing effector cytokines) or indirectly (by metabolic competition, upregulating coinhibitory receptors, or recruiting/transforming immunosuppressed cell populations) induce immunosuppression [6, 7]. In human cancers, tumor hypoxia was considered to be an indicator of poor prognosis, which could reduce the efficacy of surgical resection, radiotherapy, and chemotherapy [8, 9]. Recently, hypoxia-related genes (HRGs) have been considered as valuable biomarkers for the prognosis or curative effect in tumors. For instance, Yang et al. established a HRG signature with strong prognostic value for patients with prostate cancer [10]. Dao et al. identified and validated a reliable hypoxia-related survival score in IDH-mutant glioma stem cells based on five HRGs (LYVE1, FAM162A, WNT6, OTP, and PLOD), which was significantly related to the survival of patients with glioma [11]. Cai et al. constructed and validated a prognostic model for hepatocellular carcinoma (HCC) composed of three hypoxia genes (ENO1, UGP2, and TPI1), which was shown to be effective for the prognosis of HCC patients [12]. Although previous study indicated that downregulated hypoxia transcriptome in vitro was closely related to the depressed prognosis in BC [13], the prognostic values of HRGs in BC was still unclear and attract us to further study on it. In the present study, we established a Risk Score for BC patients' prognosis based on three optimal HRGs (CA9, PGK1, and SDC1). Moreover, this predictive model could predict the prognosis of BC patients and should provide novel clues for prognostic stratification.
The clinical information and mRNA profile data of 1092 BC patients were obtained from TCGA database (https://tcga-data.nci.nih.gov/tcga/). We eliminated 10 inappropriate samples, and the remaining 1082 samples were randomly divided into two groups: training set (N = 541) and testing set (N = 541). The clinical information of BC patients in the two groups was provided in Table 1. In addition, we also obtained two mRNA expression profiles (GSE42568 and GSE48391) and corresponding clinical information from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), which were combined as the verification set to determine the accuracy of the predictive model. These two GEO datasets included 186 BC patients totally, and all the data were detected by using the Afymetrix Human Genome U133 Plus 2.0 Array.
In this study, a total of 26 HRGs were taken into consideration. These genes were derived from previous studies, and most of them have been proven to play a key role in the prognosis of a variety of cancers, including esophageal cancer, laryngeal cancer, and HCC [14–16]. The information of the 26 HRGs was provided in Table S1.
Based on the mRNA expression values of the 26 HRGs, the samples were clustered by the “ConsensusClusterPlus” package of the R software [17].
Based on the expression values of 26 HRG, BC samples were analyzed by univariate Cox regression, and the genes were screened which significantly associated with the prognosis of BC (P < 0.05). The candidate genes associated with prognosis were further screen via LASSO Cox regression analysis, and finally hub genes were obtained [18]. The Risk Score was constructed based on hub HRGs as follows: In this formula, Coefi (risk coefficient of each HRG) was calculated by LASSO Cox regression analysis, and Xi represented the expression level of each HRG. The optimal cutoff value of the Risk Score was determined by the survival package and survminer package of R software using the bilateral log-rank test. Then, all BC samples were divided into the following two groups based on the optimal cutoff value: the high-risk group and low-risk group.
The Kaplan-Meier method was used to evaluate the overall survival (OS) probability of all groups by the survival and survminer packages of R software, and the subsequent significance was determined via log-rank test. The survival ROC package of R software was used to plot the time-dependent receiver operator characteristic (ROC) curve [19].
The CIBERSORT was a widely used method to assess the composition of immune cells in tumor microenvironment [20]. In our study, the CIBERSORT algorithm was used to evaluate the infiltration level of 22 immune cells in each BC sample.
A nomogram model was constructed by the RMS package of R language to predict the survival probability of BC patients for one-, three-, and five-year based four independent prognostic factors (Risk Score, age, radiation therapy, and TNM Stage). The calibration curve of nomogram was plotted to determine the relationship between the actual probability and predicted probability.
The human mammary epithelial cells (MCF-10A) and BC cell lines (MCF-7 and SK-BR-3) were provided by ATCC (American Type Culture Collection, Manassas, VA, USA). MCF-10A cells were cultured in MEpiCM Medium (ScienCell) supplemented with 10% FBS (Gibco), 1% MEpiCGS (ScienCell), and 1% penicillin/streptomycin (PS, ScienCell) at 37°C in 5% CO2. MCF-7 cells were cultured in DMEM (Gibco) supplemented with 10% FBS (Gibco), 0.01 mg/mL human recombinant insulin (HRI, Solarbio), and 1% PS (HyClone) at 37°C in 5% CO2. SK-BR-3 cells were cultured in RPMI 1640 (Gibco) supplemented with 10% FBS (Gibco) and 1% PS (HyClone) at 37°C (no CO2).
Total RNAs of cells were extracted by TRNzol Universal (TIANGEN BIOTECH (BEIJIN) CO., LTD. China). Nanodrop 2000 (Thermo, USA) was used to detect the quantification and concentration of total RNAs. Next, the total RNAs were reversely transcribed into cDNAs with RevertAid First Strand cDNA Synthesis Kit (Thermo, USA) and then used to perform RT-qPCR with TB Green® Premix Ex Taq™ II (Takara, Japan). RT-qPCR thermocycling protocol was as follows: initial denaturation at 95°C for 30 s, denaturation at 95°C for 10 s, annealing at 60°C for 30 s, and amplification for 40 cycles. GAPDH was used as the housekeeping gene. The primer sequences were shown in Table 2. The 2−△△CT method was applied to calculate the expression level of genes and normalized to GAPDH.
We used R software v3.5.2. for statistical analysis. The Mann–Whitney tests were used to analyze the infiltration differences of immune cells among all groups, with P < 0.05 was considered statistically significant.
To better display the process building the hypoxia-related prognostic signature of BC, the flow chart of this work was shown in Figure 1. Firstly, K-mean clustering analysis was performed on BC samples according to the expression levels of 26 HRGs, and all BC patients were divided into 3 clusters (k = 3) (Figure 2(a)). The results of consensus clustering (Figure 2(b)) and the heat map of expression values (Figure 2(c)) exerted a better clustering effect, suggesting that the three clusters could be efficiently separated. Meanwhile, principle component analysis (PCA) suggested that there were significant differences among the three clusters (Figure 2(d)). Moreover, the Kaplan-Meier curves demonstrated that there were significant differences in OS among three clusters (Figure 2(e)). These results indicated that all the BC patients with different prognosis could be efficiently separated through the expression levels of these 26 HRGs, suggesting the potential predictive values of HRGs in BC prognosis.
We next used Univariate Cox regression analysis to calculate the hazard ratio (HR) of 26 HRGs, and the results showed that CA9, PGK1, and SDC1 (HR > 1.0, P < 0.05), were significantly associated with the OS of BC patients (Figure 3(a)), indicating that these three genes were risk genes, and their high expression was associated with poor prognosis. Further, LASSO Cox regression analysis also showed that these three genes were significantly associated with the prognosis of BC patients (Figures 3(b) and 3(c)). Next, these three genes were used to construct a Risk Score model for prognosis of BC patients. First, we calculated the expression levels of these three genes in the TCGA dataset, the GSE42568 cohort, and the GSE48391 cohort; then, we standardized and normalized the expression values. Normalization was Coefi weighting of gene expression values using LASSO Cox regression analysis. Subsequently, the formula of Risk Score was obtained as follows: Risk Score = 0.0962∗ express value of CA9 + 0.1993, ∗ express value of PGK1 + 0.2067, and ∗ express value of SDC1. We calculated the Risk Score of each sample and then divided all samples from TCGA database and GEO database into two groups based on the optimal cut-off point (0.1137): low-risk group and high-risk group. The Risk Score distribution of all samples was shown in Figures 4(a)–4(c). As shown in Figures 4(d)–4(f), the expression values of the three genes were significantly different between two groups. Moreover, the results of survival analysis demonstrated that BC samples from high-risk group had a lower OS than that from low-risk group (Figures 4(g)–4(i)). In addition, the results of time-dependent ROC curves indicated that the area under curve (AUC) values of 1-, 3-, 5-year survival of samples from the training set were 0.785, 0.689, and 0.67, respectively; the AUC values of 1-, 3-, 5-year survival of samples from the testing set were 0.595, 0.671, and 0.631, respectively; the AUC values of 1-, 3-, 5-year survival of samples from verification set were 0.628, 0.603, and 0.637, respectively (Figures 4(j)–4(l)), suggesting that the Risk Score could efficiently predict the prognosis of BC patients. In general, the Risk Score constructed by three hypoxia genes could distinguish the prognosis of different BC patients. We also validated the expression of these three genes in BC and paracancer samples, and the results showed that the expression levels of the genes were higher in tumor samples (Figures S1(a–c)). Among which, the upregulated levels of PGK1 and SDC1 were more significant. Hence, we selected PGK1 and SDC1 with the most significant differences for RT-qPCR verification.
Hypoxia in solid tumor tissue may be involved in the formation of immunosuppressive microenvironment, resulting in the difficulty of immunotherapy [6, 7]. We next employed CIBERSORT and LM22 eigenmatrix to assess the immune microenvironment composition of the two subgroups defined by Risk Score. The results of immune cell infiltration in all BC samples from TCGA database were shown in Figure 5(a); we also found that there was a weaker correlation in the proportion of infiltration of 22 immune cells (Figure 5(b)), suggesting that the infiltration of different immune cells was more heterogeneous in BC patients. Moreover, there were significant differences in the proportions of ten infiltrating immune cell types, including three types of macrophages (M0, M1, and M2), Monocytes, B cells naive, Dendritic cells activated, Mast cells resting, T cells CD4 memory resting, T cells CD8, and T cells follicular helper between two subgroups (Figure 5(c)). In the high-risk group, the samples had lower proportions of infiltrating B cells naive, Monocytes, Mast cells resting, Macrophages M2, T cells CD4 memory resting, T cells CD8, and higher proportions of infiltrating Dendritic cells activated, Macrophages M0, Macrophages M1, T cells follicular helper, which might account for the prognostic difference in BC patients from these two subgroups. Recently, the immune checkpoints have emerged as potential biomarkers for cancer immunotherapy [21]. Here, we found that Risk Score was closely correlated with the expression levels of five immune checkpoints, TIGIT, TIM3, PD1, LAG3, and CTLA4 (Figure 5(d)). In addition, compared with the low-risk group, these five immune checkpoints were significantly highly expressed in high-risk group (P < 0.05) (Figure 5(e)). It has been known that the immune checkpoints contributed to maintaining an immunosuppressive microenvironment for tumor cells to escape immune surveillance [22]. These results suggested that the poor prognosis of BC patients with high Risk Score might be associated with the immunosuppressive microenvironments.
To determine whether Risk Score was an independent prognostic factor, we included Risk Score, gender, age, TNM Stage, and radiation therapy in a multivariate Cox regression analysis. We found that the Risk Score was significantly associated with the OS of BC patients, and the samples with high Risk Score had a higher risk of death (Figure 6(a), HR = 2.708, 95% CI: 1.5061-4.869, P < 0.001) compared with those with low Risk Score. Notably, the OS of the samples in the high-risk group was significantly lower than that in the low-risk group (Figures 6(b)–6(f)); survival analysis for male BC patients was not performed since there were only 12 male patients, indicating that the prediction of BC prognosis by Risk Score was not affected by these factors, and Risk Score could be used as an independent prognostic signature to predict the prognosis of BC patients.
Finally, the nomogram model was established based on the four independent prognostic factors including Risk Score, radiation therapy, age, and TNM Stage (Figure 7(a)). The results showed that the corrected curves for 1- (Figure 7(b)), 3- (Figure 7(c)), and 5-year (Figure 7(d)) were closer to the ideal curves (a straight line with a slope of 1 passing through the origin of the coordinate axis), indicating that the prediction was in powerful agreement with the actual results. Meanwhile, the AUC values of nomogram for 1-, 3-, and 5-year were 0.728, 0.651, and 0.673, respectively (Figure 7(e)). These results suggested that the nomogram model could reliably predict the long-term survival probability of BC patients.
Previous studies have shown that the high level of intracellular PGK1 was related to tumorigenesis, progression, and chemoradiotherapy resistance [23]. And the high level of PGK1 was indicative of undesirable overall survival for various cancers [24]. In addition, the high level of SDC1 was also considered to be related to more aggressive tumors and a worse prognosis of prostate cancer [25]. In this study, we found that compared with MCF-10A cells, the expression level of PGK1 was significantly increased both in MCF-7 cells and SK-BR-3 cells (Figure 8), and the expression level of SDC1 was significantly increased in SK-BR-3 cells, while only slightly increased in MCF-7 cells (Figure 8). Such data were consistent with the bioinformatics analysis, indicating that the Risk Score constructed based on HRGs was reliable to evaluate the prognosis of BC patients.
BC has become the most common leading cause of tumor-related mortality among women in the world [26]. Increasing prognostic signatures have been evidenced to show great significance in various tumors [27]. Hence, the identification of efficient prognostic signatures will contribute to the diagnosis and treatment of BC. Recently, HRGs in BC have attracted more attention due to their crucial function closely associated with the development or diagnosis of BC, and even might be the potential therapeutic targets. Duechler et al. revealed that the heterogeneous immune microenvironment in BC was significantly affected by HRGs, and suggested that targeting HRGs might not only sensitize breast tumor for radiation and chemotherapies but also interfere with cancer immunosuppression [28]. Guerrab et al. found that the quantification of HRG expression might be considered as a potential approach for the prediction of clinical outcome in BC [29]. These reports all confirmed the important roles of HRGs in BC. However, the research about the prognostic values of HRGs in BC is lacking. In the present study, we were the first to explore the prognostic values of HRGs in BC and identified three HRGs, including CA9, PGK1, and SDC1, which were closely associated with the prognosis of BC patients, suggesting their potential prognostic values. The expression of cell-surface carbonic anhydrases IX (CA9) was significantly upregulated in hypoxia for all BC cell lines including MCF7, ZR-75.1, and MDA-mb231 cells and has been demonstrated to be novel therapeutic targets for BC [30, 31]. The mitochondrial translocation of phosphoglycerate kinase 1 (PGK1) was induced by hypoxia [32], and Fu et al. determined that PGK1 was a potential survival biomarker and invasion promoter through modulating the HIF-1α-mediated process of epithelial-mesenchymal transition (EMT) in BC [33]. Although the role of SDC1 in BC remains unclear, its crucial function in other human cancer has been studied in detail. Syndecan-1 (SDC1), also known as CD138, can induce an immature and stem cell-like transcriptional program in myeloma cells [34]. In addition, SDC1 has been the gold-standard surface marker to detect multiple myeloma (MM) cells for decades [35]. These studies suggested that the three HRGs play essential functions in various human cancers including BC. Here, a predictive model for prognosis in BC was established based on the three HRGs. Moreover, three datasets composed of training set, testing set, and verification set were all applied to determine the accuracy of this model, which revealed that the prognostic model could efficiently predict the prognosis of BC patients. On the other hand, considering the essential role of hypoxia in various tumors, the pathogenic or therapeutic target potential of CA9, PGK1, and SDC1 in BC should be investigated in our future work. In the last decades, BC is not generally viewed as a highly immunogenic cancer, but recent studies have described a rich tumor immune microenvironment in BC [36]. Soysal et al. revealed that various components of BC microenvironment, such as suppressive immune cells and altered extracellular matrix, function together to prevent effective antitumor immunity and promote the progression and metastasis of BC [37]. In this study, we analyzed the immune infiltration differences of 22 immune cells in BC samples from high-risk group and low-risk group and found that there were significant differences in the proportions of ten types of infiltrating immune cells in BC patients from high- and low-risk groups. Our analysis was in agreement with previous studies that a rich tumor immunoreaction occurred during the progression of BC, which might account for the prognostic difference in BC patients. Accordingly, our hypoxia-related signature might be helpful to choose appropriate immunotherapy for BC patients, which deserved further exploration in near future. Although our results suggested that SDC1 might be related to the prognosis in BC, its specific function or mechanism in BC progression should be explored; meanwhile, more samples are needed to be collected to verify the accuracy of our prognostic model.
In summary, our study established a predictive model based on three HRGs (CA9, PGK1, and SDC1) and demonstrated that this model could reliably predict the prognosis of patients with BC. Our prognostic signature provides an additional alternative for BC prognosis prediction, which will indirectly benefit for better clinical decision and treatment strategies of BC patients. |
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PMC9649313 | Ruilong Lu,Kexin Xu,Yanqin Qin,Xuejie Shao,Miaomiao Yan,Yixi Liao,Bo Wang,Jie Zhao,Jiansheng Li,Yange Tian | Network Pharmacology and Experimental Validation to Reveal Effects and Mechanisms of Icariin Combined with Nobiletin against Chronic Obstructive Pulmonary Diseases | 03-11-2022 | Background Chronic obstructive pulmonary disease (COPD) is a long-term respiratory disorder marked by restricted airflow and persistent respiratory symptoms. According to previous studies, icariin combined with nobiletin (I&N) significantly ameliorates COPD, but the therapeutic mechanisms remain unclear. Purpose The aim of the study is to investigate the therapeutic mechanisms of I&N against COPD using network pharmacology and experimental validation. Methods The targets of I&N and related genes of COPD were screened and their intersection was selected. Next, the protein-protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Further, a COPD rat model was established to validate the effect and mechanisms of I&N. Results 445 potential targets I&N were obtained from SwissTargetPrediction, STITCH 5.0, and PharmMapper databases. 1831 related genes of COPD were obtained from GeneCards, DrugBank, and DisGeNet databases. 189 related genes were screened via matching COPD targets with I&N. 16 highest score targets among 189 targets were obtained according to PPI networks. GO and KEGG pathway enrichment analyses of 16 highest score targets suggested that these key genes of I&N were mostly enriched in the tumor necrosis factor (TNF) pathway, mitogen-activated protein kinase (MAPK) pathway, and phosphatidyl inositol 3-kinase (PI3K)-protein kinase B (AKT) pathway. Therefore, the treatments of I&N for COPD were connected with inflammation-related pathways. In in vivo experiments, the studies indicated that I&N improved the lung function and alleviated the damage of pulmonary histopathology. Moreover, I&N reduced levels of interleukin (IL)-6, IL-1β, and TNF-α in lung tissues of COPD rats and inhibited the activation of the MAPK pathway and PI3K-Akt pathway. Conclusions Icariin combined with nobiletin has therapeutic effects on COPD by inhibiting inflammation. The potential mechanisms of I&N may relate to the MAPK pathway and PI3K-Akt pathway. | Network Pharmacology and Experimental Validation to Reveal Effects and Mechanisms of Icariin Combined with Nobiletin against Chronic Obstructive Pulmonary Diseases
Chronic obstructive pulmonary disease (COPD) is a long-term respiratory disorder marked by restricted airflow and persistent respiratory symptoms. According to previous studies, icariin combined with nobiletin (I&N) significantly ameliorates COPD, but the therapeutic mechanisms remain unclear.
The aim of the study is to investigate the therapeutic mechanisms of I&N against COPD using network pharmacology and experimental validation.
The targets of I&N and related genes of COPD were screened and their intersection was selected. Next, the protein-protein interaction (PPI) networks, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Further, a COPD rat model was established to validate the effect and mechanisms of I&N.
445 potential targets I&N were obtained from SwissTargetPrediction, STITCH 5.0, and PharmMapper databases. 1831 related genes of COPD were obtained from GeneCards, DrugBank, and DisGeNet databases. 189 related genes were screened via matching COPD targets with I&N. 16 highest score targets among 189 targets were obtained according to PPI networks. GO and KEGG pathway enrichment analyses of 16 highest score targets suggested that these key genes of I&N were mostly enriched in the tumor necrosis factor (TNF) pathway, mitogen-activated protein kinase (MAPK) pathway, and phosphatidyl inositol 3-kinase (PI3K)-protein kinase B (AKT) pathway. Therefore, the treatments of I&N for COPD were connected with inflammation-related pathways. In in vivo experiments, the studies indicated that I&N improved the lung function and alleviated the damage of pulmonary histopathology. Moreover, I&N reduced levels of interleukin (IL)-6, IL-1β, and TNF-α in lung tissues of COPD rats and inhibited the activation of the MAPK pathway and PI3K-Akt pathway.
Icariin combined with nobiletin has therapeutic effects on COPD by inhibiting inflammation. The potential mechanisms of I&N may relate to the MAPK pathway and PI3K-Akt pathway.
Chronic obstructive pulmonary disease is the most common disease of the respiratory system with high morbidity and mortality and endangers public health [1]. Lung and systemic inflammation and lung injury are the main pathophysiology changes in COPD [2]. Nowadays, various treatment strategies are available for COPD, including bronchodilators and anti-inflammatory agents, and bronchodilator therapy is the most common treatment against COPD [2]. However, serious side effects, such as potentially paradoxical bronchospasm, may arise due to adhibition of bronchodilator therapy [3]. Traditional Chinese medicine (TCM) has special superiorities for treating COPD. Bufei Yishen formula (BYF), which is an effective therapeutic strategy for COPD, exerts various positive effects for COPD patients via inhibition of inflammation [4]. Icariin and nobiletin, two active ingredients screened from BYF, have been reported to have anti-inflammatory, antiapoptosis, and antioxidant effects against several inflammatory diseases [5–7]. The effects on improving the lung function and inhibiting the inflammatory response of I&N in COPD rats had been proved in previous studies. However, the mechanisms of I&N for treatment of COPD remain unclear and the traditional experimental approaches are difficult to elucidate the mechanisms and key action targets of I&N for COPD. Network pharmacology is a strategy based on multidirectional pharmacology, system biology, network analysis, and computational biology, which systematically expounds the potential targets and mechanisms of TCM [8]. In this method, the relationship networks of herb, compound, target, pathway, and disease are established, which reveal the molecular basis and forecast the pharmacological mechanisms [9]. In this study, the targets of I&N and related genes of COPD were screened and the ingredients-disease targets network was established. Then, the potential molecular mechanisms were revealed via gene enrichment analysis and molecular docking. Finally, the COPD rat model was established to verify therapeutic effects and potential pathway of I&N against COPD (Figure 1). Therefore, the primary goals of this study were (1) to screen related genes of COPD and the potential targets of I&N; (2) to dissect the underlying mechanisms of I&N for COPD using network pharmacology; and (3) to validate anti-inflammatory effects and the potential pathway of I&N for treatment of COPD.
The canonical SMILES of icariin and nobiletin were acquired by searching the keywords of “icariin” (Compound CID: 5318997) and “nobiletin” (Compound CID: 72344) from PubChem [10]. The molecular targets of icariin and nobiletin were filtered by searching the canonical SMILES of icariin and nobiletin from SwissTargetPrediction (https://www.swisstargetprediction.ch/), STITCH 5.0 (https://stitch.embl.de/) [11], and PharmMapper (https://lilab-ecust.cn/pharmmapper/) [12].
The related genes of COPD were screened via the keywords of “chronic obstructive pulmonary disease” in GeneCards (https://www.genecards.org/) [13], DrugBank (https://go.drugbank.com/) [14], and DisGeNet (https://www.disgenet.org/) [15]. Then, all targets of components and COPD were submitted to UniProtKB (https://www.uniprot.org/) [16] to acquire the standardized gene symbols.
First, we intersected the obtained components targets with the genes associated with COPD and obtained a Venn diagram of the intersected gene symbols. Then, a PPI network was built using STRING [17] and Cytoscape 3.8.2. To screen the key targets, the topological characteristics were analyzed of the PPI network. First, the gene symbols were chosen by the degree score. Next, the betweenness centrality (BC), closeness centrality (CC), degree, and average shortest path length (ASPL) were calculated by Cytoscape to indicate the potential targets.
The GO and KEGG pathways enrichments of the topological potential targets were analyzed in DAVID 6.8 [18]. The p value <0.05 was set as a significant difference for KEGG pathway analysis.
The 3D structures of icariin and nobiletin were acquired from PubChem and were transformed from their original constructions into PDB formats using Open Babel 3.1.1. From RCSB Protein Data Bank, the X-ray crystal structures of key proteins were obtained [19]. Seven protein targets were studied: AKT1 (PDB ID: 2UZR), TNF (PDB ID: 7KP9), VEGFA, (PDB ID: 7LL8), EGFR, (PDB ID: 5Y9T), JUN, (PDB ID: 5T01), MMP9, (PDB ID: 1L6J), and SRC, (PDB ID: 2BDF). The water molecules were deleted and hydrogen atoms were added in optimizer of structures using AutoDock Tool 1.5.6. Then, the receptor proteins were docked with ligand molecules via AutoDock. All of options were default setting for docking run. Finally, the molecular docking results were visualized by PyMoL 2.2.3, which acquire the highest scores.
Sprague-Dawley (SD) rats were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd (220 ± 20 g, No.110011211105823815, Beijing, China). Hongqi Canal® Filter tip cigarette was purchased from Henan Tobacco Industry (Zhengzhou, China). Klebsiella pneumoniae (46117-5a1) was purchased from National Center for Medical Culture Collections (Beijing China). Icariin (Cas, 489-32-7) and nobiletin (Cas, 478-01-3) were purchased from Chengdu Must Bio-Technology (Chengdu, China). Doxofylline was obtained from Heilongjiang Fuhe Pharmaceutical Group Co., LTD. (Heilongjiang, China). The rat ELISA kits of IL-6 (Cat.No.550319) were purchased from BD Biosciences (California, America). The rat ELISA kits of IL-1β (E-EL-R0012c) and TNF-α (E-EL-R2856c) were purchased from Elabscience Biotechnology Co., Ltd (Wuhan, China). The antibodies for rat of PI3K (GTX55747, Gene Text) and P-AKT (GTX128414, Gene Text) were obtained from Gene Tex, Inc (North America). The antibody for rat of P-p38 (4511, CST) was obtained from Cell Signaling Technology (Shanghai, China). The antibody for rat of GAPDH (10494-1-AP, Proteintech) was purchased from Proteintech (Wuhan, China).
A COPD rat model was performed in terms of previous studies [20]. SD rats were randomly classified to 4 groups: control group, COPD model group, I&N group, and doxofylline group. The COPD rat model was created via exposure to cigarette smoke (CSE) and Klebsiella pneumoniae infection. Specifically, the rats were exposed to CSE (3000 ± 500 ppm) for 40 minutes twice daily for 8 weeks and to Klebsiella pneumoniae (6 × 108 CFU/ml, 0.1 ml) for 5 days once for 8 weeks. The procedures of this study were approved by the Experimental Animal Care and Ethics Committees of the First Affiliated Hospital of Henan University of Chinese Medicine, and the ethical review approval number is YFYDW2019031.
From week 9, the I&N group rats were given I&N at 2.12 mg/kg/d (the ratio of icariin to nobiletin was 12.5 : 1). The doxofylline is a newer generation xanthine, which is a kind of effective bronchodilator recommended by Global Initiative for Chronic Obstructive Lung Disease (GOLD) [2]. The doxofylline has beneficial effects with both bronchodilating and anti-inflammatory activities in COPD 1. So, we chose doxofylline as the control drug. The doxofylline group rats were given doxofylline at 36 mg/kg/d. The dosages of these drugs were calculated according the following formula (D: dose; K: body shape index, K = A/W2/3, A: surface area in m2, W: weight in kg): At week 17, 4 group rats were sacrificed after intraperitoneal injection of 2% pentobarbital sodium at 40 mg/kg.
Lung function was detected for all group rats every four weeks from 0 week to 16th week via the tidal volume (TV), peak expiratory flow (PEF), and 50% tidal volume expiratory flow (EF50) by unrestrained pulmonary function testing plethysmographs (Buxco Inc., Wilmington, NC, USA). The lung tissues were soaked in 4% paraformaldehyde solution. Next, the tissues were cut and embedded in paraffin and made slices. Then, the lung tissues slices were stained with hematoxylin and eosin and were observed by a light microscope (Olympus, Tokyo, Japan). The mean linear intercept (MLI) and mean alveolar numbers (MAN) were considered as the degree of alveolar damage. Under microscopy (×200), 6 visual fields were taken in each slice, and the alveolar number and the linear intercept in a fixed area of visual field were measured. MAN (/mm2) = Na/A. Na is the number of pulmonary alveoli in each visual field. A is the area of the visual field. Then, we made a cross (+) under the visual field and counted the number of alveolar septaon the cross. MLI (μm) = L/Ns. Ns is the number of alveolar septa. L is total length of the cross.
The lung tissue was homogenized in PBS solution and centrifuged to collect the supernatant. The secretion of TNF-α, IL-1β, and IL-6 in a lung tissue homogenate was measured using ELISA kits, according to the manufacturer instructions. The dilution ratio of the lung tissue homogenate was determined according to the standard curve. Samples were incubated with antibodies in 96-well plates. The OD value was detected by a microplate reader (Thermo Fisher Scientific 1500, Vantaa, Helsinki, Finland), and the concentration was calculated according to the standard curve.
The mRNA levels of GAPDH (forward: ACAGCAACAGGGTGGTGGAC, reverse: TTTGAGGGTGCAGCGAACTT), TNF-α (forward: CGTCAGCCGATTTGCCATTT, reverse: TCCCTCAGGGGTGTCCTTAG), IL-1β (forward: CCTATGTCTTGCCCGTGGAG, reverse: CACACACTAGCAGGTCGTCA), and IL-6 (forward: TCCGGAGAGGAGACTTCACA, reverse: TTCTGACAGTGCATCATCGCT) in lung tissues were detected by qPCR.
The lung tissues were lysed with RIPA buffer in ice to obtain protein samples. The concentrations of lung tissue protein samples were measured using BCA kits, and the lung tissue protein samples were adjusted to equal concentrations. The lung tissue protein samples with equal concentrations in each group were divided by SDS-PAGE electrophoresis and metastasized to PVDF membranes. 5% skim milk was used to block the PVDF. Next, membranes were incubated with their primary antibodies, including GAPDH (1 : 5000), P-p38 (1 : 1000), P13K (1 : 1000), and P-AKT (1 : 1000), and secondary antibodies (1 : 5000). The membranes were visualized using the Bio-Rad Imaging System (Pierce, USA).
The experimental data were analyzed by SPSS v21.0. A comparison among groups was performed by one-way analysis of variance with an appropriate post-hoc test. If the variances were homogeneous, the LSD method was performed. If the variances were inconsistent, Dunnett's T3 test was performed. The mean ± SD were used as the data present presentation. A p value of <0.05 was set for a statistically significant difference.
From PubChem, the 2D structures of icariin and nobiletin were downloaded (Figure 2(a)). 445 genes were obtained as potential targets of icariin and nobiletin from SwissTargetPrediction database, STITCH database, and PharmMapper database. Then, 1,831 related genes of COPD were obtained from DisGeNET database, GeneCards database (score >15.0), and DrugBank database. Matching COPD targets with icariin and nobiletin targets, 189 genes (Figure 2(b)) were chosen as related genes of I&N against COPD for constructing the component-target (C-T) network (Figure 2(c)). The C-T network was built by Cytoscape software. According to the C-T network, 120 potential targets were common targets of icariin and nobiletin. 59 potential targets were unique targets of icariin and 10 potential targets of nobiletin.
All of 189 potential therapeutic targets were submitted to STRING database, and they were submitted to CytoScape3.8.2 for constructing and analyzing the PPI network (Figure 3). The PPI network consisted of 189 nodes and 2809 edges and the average degree was 23. Then, the targets with degree higher than double average degree were selected and 41 targets were screened for further analysis. Next, the mean value of BC, CC, ASPL, and degree of 41 targets were calculated using the Analyze Network tool of Cytoscape3.8.2. The targets with values of BC, CC, and degree higher than the mean value of BC, CC, and degree (BC > 0.0092, CC > 0.5875, degree > 60), and value of ASPL lower than mean value of ASPL (ASPL < 1.7021), were selected as key targets. Finally, 16 targets were screened out, including TNF, AKT1, VEGFA, EGFR, JUN, SRC, MMP9, CASP3, MYC, IGF1, HSP90AA1, HRAS, ESR1, PTGS2, PPARG, and MAPK1 (Figure 3).
The DAVID 6.8 database was used to perform GO and KEGG analyses on 16 important targets. Positive regulation of transcription from the RNA polymerase II promoter, negative regulation of the apoptotic process, and positive regulation of transcription, DNA-templated, were mostly enriched in BP enrichment analysis; nucleus, cytoplasm, and cytosol were mostly enriched in CC enrichment analysis; protein binding, identical protein binding, and enzyme binding were mostly enriched in MF analysis. (Figure 4(a)). The results of KEGG analysis indicated that the regulatory pathway included TNF, MAPK, and PI3K-Akt pathways (Figure 4(b)). These results suggested that I&N may exert inhibition effects of inflammation in COPD by regulating the TNF, MAPK, and PI3K-Akt pathways. The component-target-pathway network was built by Cytoscape software (Figure 4(c)).
To clarify the potential interaction between two components and the key proteins, molecular docking was performed to reveal the possible binding mode between the 7 highest scoring proteins, including TNF, AKT1, VEGFA, EGFR, JUN, SRC, and MMP9 (Figure 4(d)), and two components. The binding energy was considered as an important factor for constituents screening (Table 1). Icariin was predicted to interact with AKT via 3 residues (ASP-119, GLN-59, and LEU-78), with EGFR via 6 residues (PRO-669, ASN-700, ARG-831, ARG-776, ILE-1018, and TYR-1016), with MMP9 via 6 residues (ARG-370, LEU-35, LYS-184, ASN-38, ASP-185, and THR-96), with JUN via 4 residues (DG-26, DG-27, DA-23, and DA-17), with SRC via 2 residues (GLU-270 and GLU-265), and with TNF via 4 residues (PHE-144, GLY-24, ASP-140, and PRO-139). In addition, nobiletin could bind to AKT by 3 residues (GLN-79, LEU-78, and GLN-59), to EGFR by 5 residues (VAL-769, ARG-776, ALA-767, LEU-777, and ILE-1018), to MMP9 by 4 residues (THR-426, GLY-428, PRO-430, and LEU-431), to SRC by 2 residues (TRP-260 and LYS-316), and to VEGFA by 2 residues (CYS-131, TYR-52). According to Table 1 and Figure 5, icariin and nobiletin have strong binding interactions with TNF, AKT1, VEGFA, EGFR, JUN, SRC, and MMP9.
To verify treatment of I&N of COPD, we established the COPD model through co-treatment with CSE and Klebsiella pneumoniae in rats. As described in Figure 6, compared with the control group, the TV, PEF, and EF50 in lung functions descended significantly in COPD rats (P < 0.05), and I&N and doxofylline increased the TV, PEF, and EF50 in rats (P < 0.05).
Lung tissue histopathology analysis indicates that I&N reduced alveolar damage and airway wall thickness (Figure 7(a)). Quantitative analysis of lung tissue histopathology showed that (Figure 7(b)), compared to the control group, MAN was decreased and MLI was increased in COPD rats (P < 0.05); I&N and doxofylline increased MAN and decreased MLI (P < 0.05); and I&N effectively relieved the thickened airway wall in COPD rats (P < 0.05).
In the COPD rats, the mRNA levels and protein secretion of inflammatory factor in lung tissues were significantly increased, including IL-6, IL-1β, and TNF-α, and these were decreased with treatment of I&N (P < 0.05) (Figures 8(a) and 8(b)). As in Figures 8(c) and 8(d), the expression of PI3K, P-AKT, and P-p38 of lung tissues were significantly increased in the model group, and I&N decreased the expression of PI3K and phosphorylation of P-AKT and P-p38 of lung tissues in COPD rats (P < 0.05). These results suggested that I&N inhibit inflammatory responses in COPD rats via regulating the PI3K-AKT and MAPK pathways.
It has been verified that TCM has positive therapeutic effects on COPD. BYF, a TCM therapeutic strategy for COPD, has demonstrated that it can inhibit secretion of inflammatory cytokine, recover protease-antiprotease imbalance, and reduce collagen deposition [22]. Due to the complicacy of TCM ingredients, it is difficult to explore potential therapeutic mechanisms of BYF. Therefore, five critical active ingredients of BYF were screened out and integrated into effective-component compatibility of Bufei Yishen formula (ECC-BYF), including icariin, nobiletin, astragaloside IV, 20-S-ginsenoside Rh1, and paeonol. It has been verified the treatment effects of ECC-BYF for COPD on improving the pulmonary function and reducing pathological damage and the inflammatory cytokine levels in lung tissues in COPD rats [23]. Icariin and nobiletin are two main active ingredients of ECC-BYF. In a previous study, we found the effects of I&N on improving the lung function, reducing pathological damage, and inhibiting inflammatory response in COPD rats. However, the therapeutic mechanisms of I&N for COPD remain unclear. In this study, we devote to reveal the treatments and mechanisms of I&N against COPD. Therefore, we integrated network pharmacology and experiment verification to systematically evaluate the potential pharmacological mechanisms of I&N for COPD. First, we applied network pharmacology to screen the possible targets of I&N against COPD. 189 targets of I&N in COPD were obtained from 6 databases, and those with BC > 0.0092, CC > 0.5875, degree > 60, and ASPL < 1.7021 were considered as key targets. 16 key targets were screened out from the 189 targets via PPI network analysis, including TNF, AKT, and MAPK1. These key targets were significantly related to inflammation. Furthermore, the 16 key targets were mostly enriched inflammation-related pathway according GO analysis and KEGG analysis, such as TNF, PI3K-AKT, and MAPK signaling pathways. The result suggested I&N may inhibit the inflammatory response in COPD via these proteins and pathways. Then, molecular docking of I&N and these proteins was performed to verify the possibility of interaction, and these proteins, including AKT, TNF, EGFR, and MMP9, had strong binding energy with I&N. Inflammation is a key pathological reaction for the development of COPD [24]. The main inflammatory cells in COPD involve neutrophils, macrophages, and lymphocytes in the lung tissue and airway [25]. The inflammatory mediators and destructive enzymes from inflammatory cells are related to the structural damage of the airway and lung tissue in COPD [26]. For instance, neutrophils in COPD patients and COPD model rats are recruited to the lung and airway and secrete various serine proteases, including myeloperoxidase (MPO), matrix metalloproteinase (MMP), and neutrophil elastase (NE), all of which are related to destruction of the alveolar airway and cause emphysema [27]. PI3K, a kind of lipid kinases, induced the phosphorylation of AKT to regulate cell survival, growth, multiplication, and death in response to extracellular signals. Based on previous studies, the inflammatory efficacy of the PI3K-AKT signaling pathway in COPD. The concentrations of TNF-α and IL-6 in both the bronchoalveolar lavage fluid (BALF) and serum are decreased via restraining the activation of PI3K-AKT signaling in COPD model rats [28]. Macrolide reduces lung and systemic inflammation of COPD patients by regulating the PI3K-AKTN pathway [29]. The family of MAPKs, including p38, ERK, and JNK, is considered as a significant role in the inflammatory process [30]. The MAPK signaling pathway regulates COPD-related characteristics such as chronic inflammation and cytokine expression. The levels of phosphorylation of ERK, p38, and JNK in RAW 264.7 cells stimulated by CSE are much higher, indicating that MAPK signaling was activated in macrophages. Treatment with a MAPK signaling inhibitor also successfully inhibited the TNF-α, IL-1β, and HO-1 overexpression following CSE [31]. Moreover, PI3K-AKT and MAPK signaling pathways are considered as the major pathways, which observably upregulate the MUC5AC expression with the elevated phosphorylation level [32]. MUC5AC, a major secreted mucin which is closely connected with the viscoelasticity of sputum, endangers mucociliary functions and decreases mucus clearance because of secretion excessive, and leads to aggravated lung infection [33]. According to previous studies, the secretion of MUC5AC was downregulated via the inhibition of PI3K-AKT signaling pathway phosphorylation [34]. It has been reported that icariin inhibits CSE-induced inflammation, ROS production, and airway remodeling via mitigating glucocorticoids resistance in CSE-exposed BEAS-2B cells [35]. In addition, nobiletin exhibited protective effects in decreasing the production of TNF-α, IL-6 via restraining activation of NF-κB signaling in the LPS-induced acute lung injury mice model and LPS-stimulated A549 cells [36]. We had validated the anti-inflammatory effect of I&N against COPD in in vivo experiment. The mRNA and protein expression levels of IL-6, IL-1β, and TNF-α in lung tissues of COPD model rats were significantly increased and were decreased by I&N and doxofylline. On the other hand, the decline of lung function and emphysema is a common symptom during the development of COPD [37]. In in vivo experiments, the lung function and alveolar damage were significantly improved by treatment of I&N and doxofylline compared to the model group. Furthermore, the expression levels of PI3K and phosphorylation levels of P-AKT and P-p38 in lung tissues were significantly decreased after the treatment of I&N and doxofylline compared to the model group. These results confirm the inhibition inflammatory response effects of I&N in by decreasing the expression levels of inflammatory cytokines. Moreover, doxofylline can improve the lung function and the expression of inflammatory factors in COPD rats. The therapeutic effects of I&N were consistent with those of doxofylline in improving symptoms and inhibiting inflammation. Moreover, the potential mechanism may be related to suppress the phosphorylation of the PI3K-AKT and MAPK pathway in COPD.
In our research, the therapeutic efficacy and mechanisms of I&N for COPD are verified via the method integrating network pharmacology and experiment validation. 16 key targets of I&N against COPD were screened, including TNF, AKT1, and MAPK1. According KEGG pathway analysis, the activation of the MPAK and PI3K-AKT pathways was a significant mechanism of I&N against COPD. In in vivo experiments, the lung function, pathological damage of lung tissues, and secretion of IL-6, IL-1β, and TNF-α were improved by treatment of I&N in COPD rats. Furthermore, the levels of PI3K, P-AKT, and P-p38 were reduced by I&N. In conclusion, I&N have significant anti-inflammation effects for COPD via the restraining activation of PI3K-AKT and MPAK pathways. However, the complex mechanisms of I&N for treatment of COPD require further exploring. |
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PMC9649317 | Jiyun Cheng,Genxiang Rong,Ziqi Wang,Shencai Liu,Qinfeng Yang,Weilu Liu,Dongkun Zhang,Jianwei Li | ECM-Mimicking Hydrogels Loaded with Bone Mesenchymal Stem Cell-Derived Exosomes for the Treatment of Cartilage Defects | 03-11-2022 | It is well-established that treating articular cartilage injuries is clinically challenging since they lack blood arteries, nerves, and lymphoid tissue. Recent studies have revealed that bone marrow stem cell-derived exosomes (BMSCs-Exos) exert significant chondroprotective effects through paracrine secretions, and hydrogel-based materials can synergize the exosomes through sustained release. Therefore, this research aims to synthesize an ECM (extracellular matrix)-mimicking gelatin methacryloyl (GelMA) hydrogel modified by gelatin combined with BMSCs-derived exosomes to repair cartilage damage. We first isolated and characterized exosomes from BMSCs supernatant and then loaded the exosomes into GelMA hydrogel to investigate cartilage repair effects in in vitro and in vivo experiments. The outcomes showed that the GelMA hydrogel has good biocompatibility with a 3D (three-dimensional) porous structure, exhibiting good carrier characteristics for exosomes. Furthermore, BMSCs-Exos had a significant effect on promoting chondrocyte ECM production and chondrocyte proliferation, and the GelMA hydrogel could enhance this effect through a sustained-release effect. Similarly, in vivo experiments showed that GelMA-Exos promoted cartilage regeneration in rat joint defects and the synthesis of related cartilage matrix proteins. | ECM-Mimicking Hydrogels Loaded with Bone Mesenchymal Stem Cell-Derived Exosomes for the Treatment of Cartilage Defects
It is well-established that treating articular cartilage injuries is clinically challenging since they lack blood arteries, nerves, and lymphoid tissue. Recent studies have revealed that bone marrow stem cell-derived exosomes (BMSCs-Exos) exert significant chondroprotective effects through paracrine secretions, and hydrogel-based materials can synergize the exosomes through sustained release. Therefore, this research aims to synthesize an ECM (extracellular matrix)-mimicking gelatin methacryloyl (GelMA) hydrogel modified by gelatin combined with BMSCs-derived exosomes to repair cartilage damage. We first isolated and characterized exosomes from BMSCs supernatant and then loaded the exosomes into GelMA hydrogel to investigate cartilage repair effects in in vitro and in vivo experiments. The outcomes showed that the GelMA hydrogel has good biocompatibility with a 3D (three-dimensional) porous structure, exhibiting good carrier characteristics for exosomes. Furthermore, BMSCs-Exos had a significant effect on promoting chondrocyte ECM production and chondrocyte proliferation, and the GelMA hydrogel could enhance this effect through a sustained-release effect. Similarly, in vivo experiments showed that GelMA-Exos promoted cartilage regeneration in rat joint defects and the synthesis of related cartilage matrix proteins.
Since articular cartilage is an avascular tissue with limited intrinsic repair capacity, treating cartilage defects caused by trauma or illness remains challenging during clinical practice [1, 2]. To date, various treatments, including microfractures and arthroplasty, have failed to fully regenerate hyaline cartilage and restore its original mechanical properties due to the failure to generate sufficient tissue to restore damaged cartilage [3]. Damage to the articular cartilage can induce joint swelling and discomfort and precipitate the course of osteoarthritis, eventually leading to permanent full-thickness cartilage degradation and limb movement restriction [4]. Accordingly, a safe and effective treatment strategy for cartilage defects is warranted. Mesenchymal stem cells (MSCs) are well-recognized to possess powerful immunomodulatory effects and tissue repair capabilities and have been widely used in cartilage-related diseases [5–7]. Indeed, significant inroads have been made in recent years, with reports that MSC transplantation can effectively promote the repair of cartilage damage by regulating the differentiation of MSCs into chondrocytes [8]. However, the efficacy of MSCs is limited by their poor cell survival at the injury site, the possibility of immunological rejection, and the uncertainty of differentiation direction after transplantation [9]. Overwhelming evidence substantiates that the therapeutic benefits of MSCs can be attributed to their paracrine mechanism, mostly involving the secretion of exosomes (Exos). Exos are cell-secreted vesicles 30–200 nm in diameter with similar functions to parental mesenchymal stem cells [10]. In addition, exosomes are easier to store and transport than cells, avoiding many limitations associated with cell transplantation, such as immunogenicity and tumorigenicity [11]. It has been shown that exosomes contain all kinds of lipids, proteins, and various noncoding RNAs, particularly miRNAs, which exert a regulatory role similar to their source cells by mediating cell-to-cell communication [12]. An increasing body of evidence from recently published studies suggests that exosomes can promote osteoarthritis repair and relieve pain by promoting cartilage regeneration and reducing inflammation [13]. However, the therapeutic effect of pure exosome therapy in vivo is limited, mainly due to insufficient local concentration and transient release of exosomes, which cannot guarantee a sustained effect. In addition, the repair and regeneration of cartilage damage needs a long healing time. Therefore, it is urgent to develop a good carrier to satisfy the need for local exosome release to maintain the bioactivity of exosomes and accelerate the repair of cartilage damage. The advent of tissue engineering offers a promising avenue to resolve these problems. Given their decent biocompatibility and ease of modification, hydrogels are believed to be an appropriate material for delivering various active factors in most tissue engineering materials [14]. Gelatin methacryloyl (GelMA) is synthesized from gelatin by modification and contains numerous RGD (arginine-glycine-aspartic acid) and MMP (matrix metalloproteinase) target sequences involved in cell adhesion and are appropriate for cell remodeling [15, 16]. Due to GelMA's similarity with the extracellular matrix in some aspects and its adjustable mechanical properties, GelMA-based hydrogels have become suitable carriers for exosomes by changing the duration of light exposure, a technique widely used in nerve and growth plate injuries in children [17, 18]. Moreover, the GelMA hydrogel is endowed with injectable and chondroprotective properties after light exposure. However, few reports have been reported on cartilage injuries. Consequently, ECM (extracellular matrix)-mimicking GelMA hydrogels have huge prospects for clinical application to deliver exosomes to boost the repair of cartilage defects. In this study, we synthesized a 3D (three-dimensional) porous hydrogel mimicking the ECM for delivering exosomes. The 3D porous structure of GelMA hydrogel enabled the retention and sustained release of exosomes secreted by bone marrow mesenchymal stem cells (BMSCs). Herein, we report a 3D hydrogel with ECM-mimicking properties that can be used to deliver bioactive exosomes to promote cartilage damage repair. The effects of long-term released exosomes in GelMA hydrogels on chondrocyte regeneration and cartilage defect healing were investigated in vitro and in vivo.
BMSCs were collected by flushing the bone marrow cavity of 2-week-old rats' tibia and femur, as previously described [19]. The isolated BMSCs were put in a low-glucose DMEM (Gibco) mixture containing 10% serum and cultured at 37°C with 5% CO2 in a cell culture incubator. Passage 3 to 5 BMSCs (P3–P5) were used for subsequent experiments. Gibco's low-glucose DMEM complemented with 10% exosome-free serum was used to culture the BMSCs. The supernatant was collected once the cells reached 50–60% confluency for ultracentrifugation. To separate live/dead cells and cell detritus, the supernatant was centrifuged for 10 minutes at 300 g, 3000 g, and 10,000 g. The exosomes at the bottom of the centrifuge tube were then resuspended in phosphate-buffered saline (PBS) and kept at −80°C after centrifugation at 100,000 g for 90 minutes. All the centrifugation procedures above were performed at 4°C. Surface-labeled antibodies, such as CD9 (ProteinTech) and TSG101 (Abcam), were used to identify the collected exosomes. Transmission electron microscopy (TEM) and nanoparticle tracking analysis (qNa006E ® system, Izon Science) were used to examine morphology and size separately. Exosomes were stained with the red fluorescent dye PKH26 (Sigma-Aldrich) according to the manufacturer's instructions.
5 g of gelatin (Gel, Sigma-Aldrich) was added to 50 ml of PBS solution and swirled until thoroughly dissolved at 50°C. The gelatin solution was then progressively added to 4 ml of methacrylic anhydride (MA, Sigma-Aldrich) at 0.5 ml/min, and the reaction was stopped after 3 h of continuous magnetic stirring at the specified conditions. The solution was dialyzed against clean water at 50°C for 6 days in a dialysis bag (12–14 kDa). The dialyzed solution was centrifuged at 2000 rpm for 10 minutes, and the supernatant was collected and lyophilized for 6 days in a freeze dryer to create a foamy methacrylic acid-modified gelatin sample (GelMA). GelMA was dissolved in heavy water (D2O) at 50°C, and the H1 NMR spectrometer was used to verify whether the modification was successful. The lyophilized The GelMA was dissolved in a photoinitiator Irgacure 2959 (Sigma-Aldrich) solution with a concentration of 0.5 w/v % to synthesize a prepolymerization monomer solution with a concentration of 10 w/v %. 200 μg of exosomes were mixed well with 60 μl of hydrogel solution and polymerized under UV irradiation (6.9 mW/cm2, 360–480 nm) for 15 s to obtain an exosome-hydrogel system (GelMA-Exos).
Field emission scanning electron microscopy (FE-SEM, ZEISS) was used to examine the hydrogel's interior shape and structure. Frequency sweep of the hydrogel at constant strain (5%) with an Anton-Paar MCR 301 rheometer from 0.1 to 10 Hz to detect the storage modulus (G′) and loss modulus of the hydrogel (G″).
Enzymatic degradation media were used to study the in vitro degradation of hydrogels. After being dissolved in 6 mL of PBS with 30 g/mL of collagenase type II (Sigma-Aldrich), the hydrogels were left to sit for an hour at 37°C. The remaining hydrogel was rinsed with PBS and its wet weights were recorded at each interval. By dividing the weight of the remaining samples by the weight of the original hydrogels, the percent degradation was computed.
Based on previous studies, cumulative and daily releases were evaluated using the BCA kit (Beyotime). In brief, the GelMA-Exos (n = 3) prepared above were incubated in PBS solution at 37°C. Supernatants were harvested on days 1, 3, 7, and 14, and free exosomes were detected by the BCA method. The total amount of exosomes minus the number of free exosomes in the supernatant was equal to the total amount of hydrogel-loaded exosomes. To assess if the exosomes in the hydrogel exhibited phagocytic activity, chondrocytes were cocultured with GelMA-Exos for 1 d. Then the medium was removed, washed with PBS 3 times, and fixed for 20 min with 4% paraformaldehyde. The phagocytosis of exosomes was detected by laser confocal microscopy (Leica) (actin-Tracker Green (Beyotime) for the cytoskeleton and Hoechst 33342 (Beyotime) for the nucleus).
Chondrocytes were harvested from knee articular cartilage tissue of 2-week-old SD (Sprague–Dawley) rats as previously described [20]. The biocompatibility of the materials was assessed using live/dead staining, cell viability, and cell adhesion assays, respectively. First, 1 × 106 chondrocytes were seeded on a 12-well culture plate and cocultured for 24 h with the samples of each group. The staining solution of PBS : Calcein-AM (Invitrogen): PI (Invitrogen) was supplemented to each group of samples in the ratio of 1 ml: 3 μL: 5 μL and incubated at 37°C for 20 min for laser confocal microscopy (Leica) observation. In addition, after coculturing the above chondrocytes with the samples of each group for 1, 3, and 7 days, the cell activity of each group was evaluated by measuring the absorbance at 450 nm with an enzyme-labeling instrument (BioTech) using CCK-8 reagent (Beyotime). Finally, chondrocytes at a density of 1 × 105 were cocultured with the samples of each group for three days, then stained with Actin-Tracker Green (Beyotime) to evaluate cell adhesion in each group by a confocal microscope (Leica).
An RNA extraction kit (Omega) was used to extract total RNA, which was subsequently reverse transcribed into cDNA using the EVO-MLV RT kit (Accurate Biotechnology). LightCycler 480 SYBR Green Master Mix (TaKaRa) was used for the qRT-PCR analysis. The relative standard curve method (2-△△CT) was used to determine mRNA expression. Table 1 shows the primer sequences.
The samples were homogenized in RIPA lysis (CWBIO), including protease and phosphatase inhibitors (Thermo Fisher). After lysing on ice for half an hour, the supernatants were collected after centrifuging at 12,000 rpm for 30 minutes at 4°C. Then the BCA kit (Beyotime) was used to measure the total protein concentration. Equal amounts of protein were loaded on sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) after loading buffer (Beyotime) was added to the supernatants. After the protein samples were deposited on the PVDF membrane (Thermo Fisher), they were blocked with 5% skimmed milk for an hour. The membranes were then treated overnight at 4°C with the primary antibodies specified in Table 2. The membranes were treated with secondary antibodies for one hour on the next day. Finally, immunoblots were examined using Thermo Fisher's enhanced chemiluminescence (ECL) kit. We cleaned the membranes three times in Tris Buffered Saline with Tween 20 (TBST) before each step. The quantitative analysis of proteins was analyzed using Image J software.
After 30 minutes of fixation in PBS with 4% paraformaldehyde, the samples were treated for 10 minutes with 0.2% Triton X-100 (BioFroxx). Subsequently, a 3% bovine serum album (BSA, BioFroxx) was used to block these samples at room temperature for one hour. The primary antibodies were incubated at 4°C overnight; then, the secondary antibodies were employed for Hoechst (Beyotime) staining for 2 hours at room temperature. Before each step, we washed the samples three times with PBS. Finally, we observe the staining results under a confocal microscope (Leica). The antibodies are listed in Table 2.
To study the effect of GelMA-Exos on cartilage repair, twelve eight-week-old SD female rats were randomly allocated into three groups: a simple injury group (Control), an Exos group, and a GelMA-Exos group. A mixture of 6 mg/kg pyrazine and 70 mg/kg ketamine were intraperitoneally injected to anesthetize these rats. Subsequently, the hairs on the left legs were shaved, and the skin was thoroughly disinfected with iodophor. The medial patella skin and muscle were cut under sterile conditions to expose the femoral condyle. Then a 15 G needle was used to create a central defect with a diameter of about 2 mm by drilling the articular cartilage. After treatment, the incision was sutured and disinfected again. Finally, they were kept in separate cages and given food and water freely.
In order to verify that GelMA-Exos can release exosomes in vivo for a long time, we detected the fluorescence signal intensity of exosomes by an animal in vivo imager 7 days after exosome implantation. Exosomes were stained with the red fluorescent dye PKH26 (Sigma-Aldrich) according to the manufacturer's instructions. The rats were slaughtered, and articular cartilage samples were taken after four weeks of treatment. The tissues were fixed in paraformaldehyde for 24 hours, then decalcified in 10% EDTA (pH 7.4) for 21 days before paraffin embedding and sectioning. The cartilage healing was examined using hematoxylin and eosin (H&E) and Masson staining after the sections were dewaxed in xylene, washed, and rehydrated with a series of graded ethanol. In addition, immunohistochemical staining was accomplished according to the standard protocols previously described. Information on the antibodies used is listed in Table 2.
One-way analysis of variance (ANOVA) with Tukey's test was performed using GraphPad Prism 5 and SPSS 19.0. Experimental data were expressed as the mean ± standard deviation (SD). All experiments were repeated three times, and a Pvalue <0.05 was statistically significant.
The centrifuged exosomes were identified by particle size analysis, TEM, and Western blotting (Figure 1(a)). TEM showed that BMSCs-derived exosomes were round or oval and had a complete membrane structure with a size of about 100 nm (Figure 1(b)). Particle size analysis indicated that the diameters of BMSCs-derived exosomes ranged from 112 to 159 nm (Figure 1(c)), consistent with the literature [21]. A Western blot showed that these nanoparticles also expressed exosome surface-specific proteins CD9 and TSG101 (Figure 1(c)). These findings validated that exosomes produced from BMSCs were successfully obtained.
Two additional proton peaks belonging to methacrylamide (MA) groups in GelMA at 5.3 ppm and 5.5 ppm in the H1 NMR spectra of gelatin after methacrylic acid modification indicated that MA was successfully transferred to the gelatin molecule (Figure 2(b)) [22]. The storage modulus (G′) of GelMA-Exos was substantially larger than the loss modulus (G′) in the rheological property test, indicating that the synthesized hydrogel is a stable viscous solid (Figure 2(c)) [23]. Moreover, the storage modulus of GelMA-Exos was 968 ± 50 Pa, which meets the conditions for in vivo application (Figure 2(d)). Exos were uniformly dispersed on the hydrogel skeleton in SEM photos, validating that Exos were loaded into the 3D GelMA hydrogel (Figure 2(e)). Most importantly, we discovered that exosome release lasted 14 days and that over 80% of the exosomes were released from the hydrogel, which ensured the continuous release and sustained biological effects of exosomes at the cartilage injury site (Figures 2(f)–2(g)). Exos labeled with PKH-26 were also found to be scattered around the nucleus of the chondrocytes in immunofluorescence images, suggesting that Exos released from the hydrogel could be phagocytized by chondrocytes (Figure 2(h)).
A better understanding of the biocompatibility of GelMA-Exos hydrogel is essential for treating cartilage defects. The live/dead assay revealed a large number of live cells (green) and a moderate number of dead cells in each group (red) (Figures 3(a)–3(b)). The CCK-8 assay revealed that chondrocyte proliferation increased with time (Figure 3(c)). On days 3 and 7, the GelMA-Exos group and the Exos group had considerably higher chondrocyte activity than the control group, indicating that exosomes could promote the proliferation of chondrocytes. In addition, there was a difference between the GelMA-Exos and the Exos groups on day 7, with the GelMA-Exos group having a considerably higher OD value than the Exos group, which could be ascribed to the GelMA hydrogel's sustained release action [24, 25]. Current evidence suggests that exos from MSCs can promote cell proliferation and migration while inhibiting apoptosis via the AKT and ERK signaling pathways [25]. Cytoskeleton staining revealed no significant difference between each treatment group and the blank group, and a large spreading area was observed in all cases, which indicated that the GelMA hydrogel had satisfactory affinity for chondrocytes (Figures 3(d)–3(e)). Interestingly, the excellent cell adhesion properties of GelMA have been attributed to its RGD and MMP-degraded sequence [26].
To establish a cartilage injury in an in vitro model, we added IL-1β (10 ng/ml) (PeproTech, USA) to the chondrocyte culture medium for 1 day. A sample/IL-1β-treated chondrocyte coculture system was established, and the repair of chondrocytes in each group was detected by immunofluorescence staining, qPCR experiments, and Western blotting. Briefly, chondrocytes treated with IL-1β at a density of 1 × 104 were cocultured with samples for 7 days, and the chondrocytes were repaired by quantifying catabolic (MMP-13) and anabolic markers (SOX-9, COL-2) in chondrocyte degradation. The expression of chondrogenesis-related genes SOX-9 and COL-2 in the GelMA group was higher than in the control group, but the difference was not statistically significant, indicating that the GelMA hydrogel mimicking chondrocyte ECM has a definite cartilage repair effect (Figure 4(a)) [27]. The GelMA-Exos and Exos groups had significantly higher SOX-9 and COL-2 expressions than the control group, whereas MMP-13 expression was significantly lower, indicating that BMSCs-derived exosomes protected chondrocytes from IL-1β-treated chondrocyte injury with the ability to promote chondrocyte repair [6, 28]. Furthermore, qPCR results revealed that the repair capacity of GelMA-Exos was more significant than the Exos group, indicating that the GelMA hydrogel produced a more significant sustained-release effect than exosome therapy alone. SOX-9 is widely thought to be one of the most important transcription factors in promoting chondrogenesis, as it regulates the production of chondrogenesis-related markers (type II collagen) and the development of the GAG matrix [29, 30]. MCSs-Exos have been documented to increase chondrogenesis, chondrocyte proliferation, and matrix production, primarily via noncoding RNAs such as miR-23b and miR-92a via MAPK, AKT, and ERK pathways [31, 32]. Consistent with the qPCR results, immunofluorescence staining and Western Blot showed that the GelMA-Exos group had the highest COL-2 protein expression and the lowest MMP-13 protein expression (Figures 4(b)–4(c)). Taken together, our results suggest that BMSCs-derived exosomes possess direct chondrogenic properties, while GelMA hydrogel yields a sustained-release effect to synergize exosomes.
The establishment of the rat cartilage defect model and the implantation process of GelMA-Exos hydrogel are presented in Figure 5(a). In vitro degradation testing showed that the degradation rate of GelMA hydrogel was slow in PBS containing collagenase, and the doping of Exos did not affect its degradation rate (Figure S1). In vivo imaging of GelMA-Exos, seven days after implantation, showed that exosome signals were still detectable at the injury site but not with topical exosome treatment alone, indicating that the hydrogel can retain exosomes and enable sustained release (Figure 5(b)). Four weeks after establishing the animal model, the rat knee cartilage samples were collected for H&E and Masson staining (Figure 5(c)). HE staining showed new cartilage formation at the defect site. Collagen deposition and maturation were observed with Masson's trichrome staining. The staining results showed that the articular cartilage in the undamaged area was transparent and the surface was smooth and complete. Large cartilage defects and fractures were observed in the articular cartilage injury area in the control group, with little regenerated cartilage. The Exos group yielded a better repair effect than the control group, indicating that the Exos exert a protective effect at the injury site and promote cartilage regeneration. MSCs-derived exosomes have demonstrated unique therapeutic advantages in cartilage-related diseases in recent years. Zhang et al. discovered that mesenchymal stem cell-derived exosomes mediate cartilage regeneration by boosting chondrocyte proliferation and decreasing apoptosis via AKT and ERK signaling pathways [33]. Additionally, Yubao Liu et al. found that MSC-exosomes could promote chondrocyte proliferation and suppress apoptosis via the lncRNA-KLF3-AS1/miR-206/git1 axis [34]. Moreover, we found that the GelMA-Exos group exhibited better repair efficacy than the Exos group, which may be mainly attributed to the slow-release effect of the hydrogel. On the other hand, an increasing body of evidence suggests that GelMA is a gelatin-based hydrogel that mimics the natural extracellular matrix with tunable biological, degradable, and mechanical properties. It is considered a promising biomaterial for tissue engineering and regenerative medicine and has been applied to tissue engineering of many tissues, including bone, cartilage, and skin [35–37]. In conclusion, the above results corroborate that BMSCs-derived exosomes-loaded GelMA hydrogel can promote cartilage regeneration.
IHC was used to assess the expression of cartilage ECM-associated proteins (COL-2, SOX-9) in knee cartilage to better understand the relationship between cartilage extracellular matrix (ECM) and cartilage restoration (Figures 6(a)-6(b)). The Exos group exhibited significantly enhanced expression of SOX-9 and COL-2 at the injury site compared to the Control group, suggesting that BMSC-derived Exos may yield a chondroprotective effect and promote the expression of chondrogenesis-related proteins. Furthermore, compared to the Exos group, GelMA-Exos boosted the expression of COL-2, demonstrating that GelMA hydrogel enhanced the therapeutic effect of exosomes by sustained release and was consistent with the above results. Moreover, the study also proves that GelMA hydrogel can promote chondrocyte anabolism by supplying ECM essential for cartilage regeneration [15]. An increasing body of evidence suggests that exosomes derived from MSCs exhibit positive effects in regenerative medicine applications due to their ability to deliver regeneration-related RNAs, lipids, and proteins into target cells. MSCs-derived exosomes can mediate critical signaling pathways related to wound healing, such as AKT, ERK, STAT3, and IGF-1, promoting tissue regeneration [38]. On the other hand, besides the unique tissue repair ability of MSCs-derived exosomes, their potent anti-inflammatory effects cannot be ignored [39]. M1 macrophages infiltrate the injury site and release many proinflammatory molecules (such as TNF-, IL-1, and others) following cartilage damage. These inflammatory factors can persist and accelerate cartilage matrix deterioration. In addition, mesenchymal stem cells undergo aberrant differentiation in the inflammatory microenvironment, and chondrocytes begin to convert or dedifferentiate into fibroblast-like cells, resulting in fibrocartilage with poor mechanical qualities [40]. M2 macrophages also called wound repair macrophages, release a variety of anti-inflammatory chemicals such as Arg-1, IL-10, and others, which assist chondrocyte repair by suppressing MMP-13 expression and facilitating the repair of damaged chondrocytes [41]. It is widely believed that promoting the transformation of M1 macrophages to M2 macrophages during the inflammatory stage can aid in cartilage repair [42, 43]. In previous studies, we found that BMSC-derived exosomes carry various immune-regulating miRNAs, especially miR-199a, which can promote the metabolic balance of cartilage by regulating immunity [18]. In conclusion, we found that the GelMA hydrogel-loaded BMSCs-derived exosomes could promote the production of cartilage ECM-related proteins.
In this study, exosomes generated from bone marrow stem cells were loaded onto GelMA hydrogel to repair cartilage damage. The hydrogel can provide a sustained release of exosomes and enhance efficacy. In vitro experiments showed that the hydrogel loaded with exosomes had good biocompatibility and promoted the proliferation of chondrocytes as well as the ability to promote the synthesis of cartilage ECM. In addition, in vivo experiments showed that GelMA loaded with exosomes was beneficial in repairing cartilage injury in rats. This research broadens the therapeutic landscape for patients with cartilage injury in the clinic. |
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PMC9649318 | Shuang-Lan Xu,Jie Liu,Shuang-Yan Xu,Ze-Qin Fan,Yi-Shu Deng,Li Wei,Xi-Qian Xing,Jiao Yang | Circular RNAs Regulate Vascular Remodelling in Pulmonary Hypertension | 03-11-2022 | Circular RNAs (circRNAs) are a newly identified type of noncoding RNA molecule with a unique closed-loop structure. circRNAs are widely expressed in different tissues and developmental stages of many species, participating in many important pathophysiological processes and playing an important role in the occurrence and development of diseases. This article reviews the discovery, characteristics, formation, and biological function of circRNAs. The relationship between circRNAs and vascular remodelling, as well as the current status of research and potential application value in pulmonary hypertension (PH), is discussed to promote a better understanding of the role of circRNAs in PH. circRNAs are closely related to the remodelling of vascular endothelial cells and vascular smooth muscle cells. circRNAs have potential application prospects for in-depth research on the possible pathogenesis and mechanism of PH. Future research on the role of circRNAs in the pathogenesis and mechanism of PH will provide new insights and promote screening, diagnosis, prevention, and treatment of this disease. | Circular RNAs Regulate Vascular Remodelling in Pulmonary Hypertension
Circular RNAs (circRNAs) are a newly identified type of noncoding RNA molecule with a unique closed-loop structure. circRNAs are widely expressed in different tissues and developmental stages of many species, participating in many important pathophysiological processes and playing an important role in the occurrence and development of diseases. This article reviews the discovery, characteristics, formation, and biological function of circRNAs. The relationship between circRNAs and vascular remodelling, as well as the current status of research and potential application value in pulmonary hypertension (PH), is discussed to promote a better understanding of the role of circRNAs in PH. circRNAs are closely related to the remodelling of vascular endothelial cells and vascular smooth muscle cells. circRNAs have potential application prospects for in-depth research on the possible pathogenesis and mechanism of PH. Future research on the role of circRNAs in the pathogenesis and mechanism of PH will provide new insights and promote screening, diagnosis, prevention, and treatment of this disease.
Pulmonary hypertension (PH) refers to a haemodynamic state of abnormally elevated pulmonary artery pressure. The pathological characteristics of PH primarily involve pulmonary vascular remodelling, which is accompanied by varying degrees of wall thickening, lumen stenosis, and increased vascular resistance, eventually leading to right heart failure and death [1]. The occurrence and development of PH is a complex process involving multiple factors and molecules and is affected by genetic susceptibility and various endogenous and exogenous stimuli [2]. PH is characterized by an inflammatory response and pulmonary arterial endothelial cell (PAEC) dysfunction, and pulmonary artery smooth muscle cell (PASMC) proliferation, migration, and apoptosis caused by pulmonary vascular structure reconstruction are key aspects in PH development [3]. Important advances in research on the pathogenesis and prevention of PH have been made in the past decade, and most strategies mainly dilate pulmonary vessels and cannot completely reverse pulmonary vessel remodelling. However, the mechanism of the occurrence and development of PH is complex, and PH-associated morbidity and mortality are still increasing every year. This increase has resulted in a heavy burden on society and families and has become a serious threat to the health of individuals with major underlying diseases [4]. Noncoding RNAs (ncRNAs) were originally considered to be “noise” from transcription that lacked any important biological effects. However, increasing evidence shows that ncRNAs are associated with complex human diseases [5–7]. Circular RNAs (circRNAs), newly identified ncRNA molecules, are involved in the occurrence and development of human diseases, and they can be used as potential biomarkers for diagnosis and prognosis [5, 8, 9]. Notably, circRNAs have a crucial regulatory role in respiratory diseases [10]. Recently, researchers systematically collected and reviewed reports describing the functions of ncRNAs (miRNAs, lncRNAs, and circRNAs) in PH [10–13]. However, to the best of our knowledge, there is no systematic review of circRNAs that regulate vascular remodelling of PASMCs and PAECs in PH. Therefore, in this review, the research status and potential application value of circRNAs in PH are discussed. The abnormal expression of circRNAs is involved in the pathogenic mechanism of vascular remodelling in PH and provides novel biomarkers for diagnosis and treatment.
circRNAs were first discovered in plant viruses by electron microscopy in the early 1970s [14]. As circRNAs do not encode proteins, they initially did not attract the attention of researchers and were considered “junk sequences” resulting from transcriptional “noise” [15]. However, with the rapid development of RNA high-throughput sequencing technology and bioinformatics, circRNAs have been shown to be widespread in different species, where they are highly conserved, tissue-specific, and timing-specific molecules that have important biological functions [16]. circRNAs are not linear structures but rather exhibit a closed-loop structure without a 5′ end cap or a 3′ end tail, exhibiting resistance to exonucleases and a high degree of stability [17].
circRNAs primarily exist in the cytoplasm and are low-abundance RNA molecules formed by the incorrect splicing of exon transcripts. Unlike the typical splicing of mRNA, circRNAs are formed by reverse splicing. Most circRNAs are generated by exon circularization, with some circRNAs being formed by intron circularization, but they are all transcribed from pre-mRNA sequences by RNA polymerase. Thus, circRNAs primarily comprise exon-derived circRNAs, intron-derived circRNAs, and exon–intron circRNAs [18, 19].
Previous studies have shown that the potential functions of circRNAs include acting as miRNA sponges to compete with endogenous RNAs (ceRNAs), regulating gene transcription and protein binding, and encoding proteins. These aspects are discussed in the following sections.
In 2013, Memczak et al. [16] and Hansen et al. [20] proposed, for the first time, that circRNAs are rich in miRNA binding sites that can be used as natural sponges for adsorbing miRNAs. This activity prevents miRNAs from interacting with mRNAs in the 3′ untranslated region, thereby indirectly regulating the downstream target gene expression of miRNAs, a mechanism that is defined as the competitive endogenous RNA (competing endogenous RNA, ceRNA) hypothesis [21]. The proposition of this hypothesis has led to changes in the understanding of the regulatory mode of miRNAs during the regulation of gene expression, from classic miRNAs-mRNAs to circRNAs-miRNAs-mRNAs, which have received increasing attention in the study of disease pathogenesis.
circRNAs can participate in regulation at the transcriptional and posttranscriptional levels. Some studies have noted that intron-derived circRNAs interact with RNA polymerase II and have a cisregulatory effect on the transcription of parental encoding genes [22], while exons and introns composed of circRNAs have been shown to bind to small ribonucleoproteins in a complex that interacts with RNA polymerase II in the promoter region to enhance gene transcription [19]. These two types of RNA primarily play a role in the nucleus. In addition, circRNAs compete with other RNAs through sponge adsorption to bind RNA-binding proteins or miRNAs, thereby inhibiting their function and participating in the regulation of posttranscriptional processes [16, 23].
circ-Foxo3 is a circRNA encoded by the tumour suppressor gene Foxo3 that can combine with different proteins to cause different biological effects. circ-Foxo3 binds to cyclin-dependent kinase inhibitor 1 (p21) and cyclin-dependent kinase 2 (CDK2) to form a ternary complex of protein circ-Foxo3-p21-CDK2, thereby inhibiting CDK2 and cyclin A binding to cyclin E and preventing cell cycle progression [24]. circ-Foxo3 binds to the antiaging protein ID-1, the transcription factor E2F1, and the antistress proteins FAK and HIF1α in the cytoplasm, inhibiting the ageing and antistress effects of these proteins and leading to increased cell ageing [25].
Although circRNAs are noncoding RNAs, some of them also encode proteins. As early as the 20th century, some scholars reported that a class of circRNAs containing internal ribosome entry site elements can be translated by eukaryotic ribosomes and synthesize polypeptide chains [26]. In recent years, additional studies have confirmed this function of circRNAs. circ-ZNF609 in human- and mouse-derived myoblasts contains an open reading frame, similar to a linear transcript, starting from the start codon and ending at the frame. The internal stop codon, under the action of polysomes, is translated into protein, resulting in specific control of myoblast proliferation [27]. In addition, circRNAs can compete to regulate the process of alternative splicing during reverse splicing, subsequently regulating the expression of host genes [28]. Other unknown functions of circRNAs need to be identified in the future.
Pulmonary vascular remodelling is an important part of PH development, and it is essential to understand the progression and mechanism of this process. The pulmonary vascular wall has three layers: the outer layer, which contains fibroblasts; the middle layer, which consists of smooth muscle cells and one or more elastic layers; and the inner layer, which is characterized by a single layer of endothelial cells. Pulmonary vascular remodelling is the process of the thickening of the pulmonary vascular wall due to hypertrophy (cell growth) or hyperplasia (cell proliferation) of one or more cell types and the increase in extracellular matrix components. This process involves all layers of the vascular wall [29, 30], with endothelial dysfunction and abnormal smooth muscle cell proliferation being the most common. Figure 1 shows the regulatory relationship between circRNA and vascular remodelling in PH.
Many differentially expressed circRNAs can be detected in vascular endothelial cells (VECs) from different sources. These circRNAs regulate various functions of VECs and are important molecules for the onset of various diseases, providing a new approach to disease diagnosis and a novel treatment target. circRNAs regulate the proliferation, migration, apoptosis, and angiogenesis of VECs. Furthermore, circRNAs can promote or inhibit the growth, migration, apoptosis, and angiogenesis of VECs derived from umbilical veins, aorta, or other blood vessels and participate in the pathogenesis of diabetes, hypertension, atherosclerosis, and other diseases [31–34]. In addition, the influence of circRNAs on the angiogenesis of VECs promotes the occurrence of diseases closely related to this function, such as corneal neovascularization and gliomas [35, 36]. circRNAs are involved in regulating the epithelial-mesenchymal transition process that involves the transformation of VECs into mesenchymal cells, where VECs lose their original phenotypic characteristics and obtain the motility and contraction characteristics of mesenchymal cells. Neuroinflammatory diseases, bladder cancer, and pulmonary fibrosis play an important role in the occurrence and development of diseases [37–39]. circRNAs promote inflammation in VECs. To date, the role of circRNAs in the inflammatory response of VECs has only been reported in atherosclerotic diseases. The mechanism by which circ-RELL1 promotes the development of atherosclerosis is achieved by a ceRNA regulating the miR-6873-3p/MyD88/NF-κB axis [40], while circ-ANRIL increases the number of inflammatory factors to promote atherosclerosis [41]. In addition, circRNAs can regulate the permeability of the vascular endothelium. Li et al. showed that circ-IARS enters human microvascular venous VECs through exosomes from pancreatic cancer cells, increasing the permeability of the endothelial layer and thereby promoting the invasion and metastasis of pancreatic cancer [42].
VSMCs are highly differentiated cells that are primarily present in the tunica media of arteries and arterioles and have extremely high phenotypic plasticity. Under pathophysiological conditions, these cells can transition from a differentiated/contracted state to a dedifferentiated/proliferative state, resulting in increased migration and proliferation. At present, circRNA research is primarily focused on regulating the biological behaviours of VSMCs, such as their proliferation and migration, and their involvement in the occurrence and development of diseases. Hall et al. observed that 256 circRNAs were specifically expressed in mouse VSMCs by mining the annotated mouse circRNAs in the circBase database [43]. Bioinformatics analysis revealed that the abnormal expression of circ_Lrp6 is most closely related to the function of VSMCs. Later, experimental studies showed that silencing circ_Lrp6 can reduce the proliferation and migration of VSMCs in vitro and promote their differentiation, while inhibiting circ_Lrp6 in mice could reduce the formation of neointimal vasculature [43, 44], which is consistent with previous bioinformatics predictions. In addition, circCHFR was confirmed to be abnormally overexpressed in arterial smooth muscle cells (ASMCs). Cell function experiments showed that the interference of circCHFR can inhibit the proliferation and migration of ASMCs and is involved in the regulation of phenotypic changes in ASMCs. Therefore, circCHFR is considered to be a promoter of atherosclerosis [45], while circACTA2 can regulate the contraction of human ASMCs and the expression of α-smooth muscle actin (α-SMA) through the NRG-1-ICD/circACTA2/miR-548f-5p axis [46].
PH-associated circRNAs were initially identified by microarray analysis. In two experiments, Miao et al. extracted blood samples from individuals with chronic thromboembolic pulmonary hypertension (CTEPH) and healthy control human blood samples and detected abnormally expressed circRNAs. Subsequently, along with the bioinformatics analysis results, these researchers observed that hsa_circ_0002062, hsa_circ_0022342, and hsa_circ_0046159 can competitively target specific miRNAs and enrich them in important signalling pathways related to CTEPH. These circRNAs play an important role in the development and targeted therapy of CTEPH [47, 48]. Huang et al. found that hsa_circ_0003416 in the plasma was significantly downregulated in children with pulmonary arterial hypertension (PAH) caused by congenital heart disease (CHD), and this molecule could be used as a biomarker for the diagnosis of PAH-CHD [49]. Elevated serum circ_0068481 and a lower level of circGSAP in peripheral blood mononuclear cells can be used as new noninvasive biomarkers for the diagnosis and prognosis of idiopathic PAH [50, 51]. In terms of human data, the description of tissue-expressed circRNAs in diseased human tissues is very limited, therefore requiring large-scale studies and reproducible validation of biomarker datasets. In addition, differentially expressed circRNAs were detected in the lung tissue of mice with PH induced by hypoxia [52]. Our previous study identified dysregulated circRNAs from a hypoxic PH rat model and confirmed that circRNAs acting as ceRNAs were involved in PH development [53]. However, the regulatory mechanism of circRNAs in PH has not been clarified. According to the literature, there are few experimental studies on the mechanism of circRNAs in PH, and the dysregulated circRNAs that regulate vascular remodelling in PH are shown in Table 1. circHIPK3, mainly originating from the second exon of the gene HIPK3, is highly expressed and has an important function in cells. Recently, the circHIPK3-miR-328-3p-STAT3 axis was shown to be involved in the pathogenesis of PAH by stimulating hPAEC proliferation, migration, and angiogenesis [54]. hsa_circ_0016070 inhibits miR-942-5p through competitive targeting, increasing the expression of the downstream cyclin D1 (CCND1) gene and promoting the proliferation and cell cycle progression of primary-cultured PASMCs to regulate vascular remodelling in PH. Thus, hsa_circ_0016070 was identified as a potential new biomarker in PH diagnosis and treatment [55]. Zhang et al. performed in vivo and in vitro experiments and showed that circ-Calm4 can be used as an miR-337-3p sponge to regulate myosin 10 (Myo10), which affected the expression of cell cycle-related proteins, promoted PASMC proliferation, and exacerbated hypoxia-induced pulmonary vascular remodelling in the PH mouse model. In contrast, silencing of circ-Calm4 can inhibit the proliferation of PASMCs and reverse pulmonary vascular remodelling in mice [56]. Another study first showed that circ-Calm4 regulated cell pyroptosis of PASMCs via the circ-Calm4/miR-124-3p/PDCD6 axis, which may potentially be useful for therapeutic strategies of PH [57]. Recently, a novel study investigated a typical circRNA, CDR1as, that regulated the pathological process of vascular calcification by targeting miR-7-5p in PH [58]. Currently, dysregulated circRNAs act as ceRNAs to regulate the biological behaviour of PASMCs and PAECs, including proliferation, cell cycle progression, migration, and apoptosis [54–57, 59–63]. Furthermore, recent studies have demonstrated different functions of circRNAs in PH. circRNAs compete with other RNAs through sponge adsorption to bind RNA-binding proteins, thereby inhibiting their function and participating in the regulation of posttranscriptional processes. Downregulated circ-Sirt1 and upregulated circ-Grm1 interacted with the RNA-binding protein and led to changes in the proliferation and migration of PASMCs [64, 65]. m6A plays important roles in various biological processes. A recent study identified a transcriptome-wide map of m6A circRNAs in hypoxia-mediated PH, and m6A circRNAs were mainly from protein-coding gene spanning single exons and influencing the circRNA–miRNA–mRNA coexpression network [66]. However, this potential mechanism remains to be explored. m6A circRNAs regulate pulmonary vascular remodelling by affecting the function of PASMCs and PAECs and contribute to the occurrence and development of PH. To date, most research on the function of PH-associated circRNAs has been limited to studies of these molecules as biomarkers or as molecular sponges that adsorb miRNAs or bind RNA-binding proteins, exert ceRNA activity, and affect the biological behaviour of PASMCs, providing a new perspective in the study of PH pathogenesis. Interestingly, recent studies concluded that computational models could effectively predict potential ncRNA-disease associations for further experimental verification, which would help save resources [67, 68]. This study may provide a new future research direction for the identification of circRNA biomarkers related to PH. Our understanding of the role of circRNAs in PH is still preliminary. Previous studies have shown that the abnormal expression of circRNAs could help us understand the pathogenic mechanism of PH, provide new insights, and promote the screening, diagnosis, prevention, and treatment of this disease. However, to the best of our knowledge, there is no systematic review of circRNAs that regulate vascular remodelling of PASMCs and PAECs in PH. Our review focuses on circRNAs regulating vascular remodelling in PH. However, owing to the limited sample size of the studies and lack of confirmed research in vivo, the hypothesis that circRNA plays important roles in the onset and progression of PH requires further confirmation.
With the rapid development of genomics and bioinformatics analyses, the role of circRNAs has been recognized. The regulatory mechanisms of circRNAs in PH are still unclear. Here, we have reviewed recent studies on circRNAs that revealed their associations with PH and suggested potential mechanisms. Identification of more circRNAs involved in PH and exploration of their functions and targets are needed in the future.
circRNAs, as novel noncoding RNA molecules with a unique closed-loop structure and many potential regulatory functions, have attracted increasing attention in different respiratory diseases, such as lung cancer, asthma, and chronic obstructive pulmonary disease. circRNAs are characterized by high stability, conservation, and abundance in peripheral blood and other body fluids and could be effective clinical diagnostic and prognostic biomarkers for respiratory diseases. At present, studies on the role of circRNAs in PH are just beginning. With the rapid advances in high-throughput sequencing techniques and bioinformatics analysis methods, most studies have identified differentially expressed circRNAs using microarray or next-generation RNA sequencing and verified that circRNAs are likely to become emerging novel clinical biomarkers; however, the function and potential molecular mechanisms of circRNAs remain unclear. Interestingly, in vascular diseases, such as diabetes and atherosclerosis, circRNAs are closely related to the remodelling of vascular endothelial cells and VSMCs. Pulmonary vascular remodelling is an important part of PH development, and it is essential to understand the progression and mechanism of this process. In recent years, research on the function of PH-associated circRNAs has been limited to studies of these molecules as molecular sponges that adsorb miRNA or bind RNA-binding proteins, exert ceRNA activity, and affect the biological behaviour of PASMCs. New functions and molecular mechanisms of circRNAs in PH research need to be determined in the near future, and these studies will provide new insights and promote the screening, diagnosis, and prevention of PH-related diseases. |
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PMC9649319 | Xinglong Dai,Menghua Zeng,Zhen Huang,Jun Zhang,Zhengqiang Wei,Ziwei Wang | A Novel Prognostic Chemokine-Related lncRNAs Signature Associated with Immune Landscape in Colon Adenocarcinoma | 03-11-2022 | Chemokines have been reported to be involved in tumorigenesis and progression and can also modulate the tumor microenvironment. However, it is still unclear whether chemokine-related long noncoding RNAs (lncRNAs) can affect the prognosis of colon adenocarcinoma (COAD). We summarized chemokine-related genes and downloaded RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database. A total of 52 prognostic chemokine-related lncRNAs were screened by univariate Cox regression analysis; patients were grouped according to cluster analysis results. Lasso regression analysis was applied to determine chemokine-related lncRNAs to construct a risk model for further research. This study first investigated the differences between the prognosis and immune status of two chemokine-related lncRNAs clusters by consensus clustering. Then, using various algorithms, we obtained ten chemokine-related lncRNAs to construct a new prognostic chemokine-related lncRNAs risk model. The risk model's predictive efficiency, validity, and accuracy were further validated and determined in the test and training cohorts. Furthermore, this risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), immunotherapy score, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients. | A Novel Prognostic Chemokine-Related lncRNAs Signature Associated with Immune Landscape in Colon Adenocarcinoma
Chemokines have been reported to be involved in tumorigenesis and progression and can also modulate the tumor microenvironment. However, it is still unclear whether chemokine-related long noncoding RNAs (lncRNAs) can affect the prognosis of colon adenocarcinoma (COAD). We summarized chemokine-related genes and downloaded RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) database. A total of 52 prognostic chemokine-related lncRNAs were screened by univariate Cox regression analysis; patients were grouped according to cluster analysis results. Lasso regression analysis was applied to determine chemokine-related lncRNAs to construct a risk model for further research. This study first investigated the differences between the prognosis and immune status of two chemokine-related lncRNAs clusters by consensus clustering. Then, using various algorithms, we obtained ten chemokine-related lncRNAs to construct a new prognostic chemokine-related lncRNAs risk model. The risk model's predictive efficiency, validity, and accuracy were further validated and determined in the test and training cohorts. Furthermore, this risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden (TMB), immunotherapy score, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients.
Colon adenocarcinoma (COAD) is one of the digestive system's most common and deadly cancers [1]. Colonoscopy is an early screening method that can effectively prevent COAD's occurrence. Still, its insidious onset, high malignancy, and easy metastasis often lead to a worse prognosis [2]. COAD is characterized by high biological invasiveness and specific radio- and chemo-resistance, resulting in high recurrence rates and tumor progression [3]. With the advancement of surgery, chemotherapy, targeted therapy, and novel immunotherapy, the efficacy and survival of COAD patients have improved significantly. However, advanced COAD patients are still prone to recurrence and metastasis, and only a small number of patients benefit from the above treatment. The common factors are epigenetic changes and accumulation [4]. Thus, identifying effective prognostic biomarkers and their underlying functional characteristics may contribute to accurate survival prediction and optimal treatment of COAD patients. Long noncoding RNAs (lncRNAs) are composed of sequences >200 bp and lack protein-coding capacity [5]. Many lncRNAs are involved in gene regulation and various biological functions at the transcriptional, posttranscriptional, and epigenetic levels, including the recent discovery that some lncRNAs can encode small peptides/proteins [6]. Accumulating evidence showed that aberrant expression of lncRNAs is not only associated with tumor malignancy but many chemically modified lncRNAs have been validated in various cancers [7, 8]. There may be interactions between these modifications, with some competitive compensation. Notably, multiple lncRNAs have been identified as prognostic biomarkers that can be used for tumor subtype identification, treatment response prediction, and modulation of immune status [9]. Studies on the clinical and biological functions of tumor-related lncRNAs are still being reported. Chemokines are a class of cytokines with the chemotactic activity that has been reported to affect tumorigenesis and serve as potential therapeutic targets. The dysregulation of chemokines and chemokine receptors has been closely associated with tumor progression, including COAD [10]. For example, EMT-mediated CXCL1/5 can modulate resistance to anti-EGFR therapy in colorectal cancer, and CXCL1/5 may be a potential serum biomarker for predicting colorectal cancer resistance to EGFR therapy [11]. Another chemokine, CCL11, exacerbates colitis and inflammation-related colon tumorigenesis [12]. Chemokines can also affect the infiltration of various immune cells and the tumor microenvironment, thereby affecting tumor progression. CXCL14 may act as an important factor in determining the immune microenvironment in gliomas, thereby promoting antitumor CD8+ T cell responses [13]. CCL24 can promote multiple cancer progression, including COAD, through M2 macrophage polarization, angiogenesis, invasion and migration, and eosinophil recruitment [14]. lncRNAs are also involved in the chemokine regulation of colon tumors; for example, chemokine ligand 5 is engaged in tumor-associated dendritic cell-mediated colon cancer progression through noncoding RNA MALAT-1 [15]. These reports suggested that chemokine-related genes or lncRNAs play critical roles in cancers, especially tumor microenvironments (TMEs). Based on this, studying the characteristics of chemokine-related lncRNAs is of great significance for understanding how lncRNAs affect the prognosis, immune status, and tumor-related treatment of COAD patients. The tumor microenvironment (TME), as an essential component of malignancies, plays multiple roles in tumorigenesis, progression, metastasis, recurrence, and therapy resistance [16]. Complex interactions between tumor cells and the TME can promote tumor progression. Xiao et al. found that the tumor-infiltrating immune cells (TIICs) in the TME environment are highly valued in predicting cancer prognosis [17]. Recent studies have reported that immune checkpoint proteins are associated with TME and can regulate immune signaling pathways to evade immune responses and promote tumor progression [18]. Furthermore, some articles showed that tumor mutational burden (TMB) was markedly correlated with 21 tumor patients, and there were specific differences in TMB among different tumors [19]. Jiang et al. reported that immune cell infiltration and TMB scores could synergistically predict survival in gastric cancer patients [20]. To elucidate how the chemokine-related lncRNAs network affects the TME and TMB, it is necessary to understand the crosstalk between different lncRNA patterns. Understanding this network may provide essential insights into COAD patients' survival, tumor immunity, and new therapeutic options. This study first investigated the differences between the survival outcomes and immune status of two chemokine-related lncRNA clusters by consensus clustering. We then constructed a new risk model of prognostic chemokine-related lncRNAs that played a crucial role in predicting immune cell infiltration, immune checkpoint gene expression, TMB, immunotherapy score, and drug sensitivity in COAD patients. Furthermore, we analyzed the prognostic value and expression level of each lncRNA in this model in COAD patients. This study will help to explore the role of prognostic chemokine-related lncRNAs and provide new clues for the occurrence, progression, and treatment of COAD.
Transcriptome sequencing and clinical data of COAD patients were downloaded from The Cancer Genome Atlas (TCGA-COAD) database. Raw data were collected from 473 tumor samples and 41 normal tissues using Perl software (version 5.32.1). We extracted expression data for lncRNAs and mRNAs by annotating gene symbols using human GTF files. We excluded COAD patients with no overall survival value or missing status to reduce statistical bias. The relevant clinical information involved age, grade, stage, TNM stage, survival status, and survival time, as shown in Supplementary Table S1.
Based on previous studies on chemokines, we obtained 64 chemokine-related molecules (Supplementary Table S2) [21, 22]. The chemokine-related lncRNAs were screened and extracted using Pearson correlation analysis with the criteria of |Pearson R| > 0.3 and p < 0.001. lncRNAs associated with one or more of the 64 chemokines regulators were defined as chemokine-related lncRNAs. After obtaining chemokine-related lncRNAs, we combined the survival status and survival time of COAD patients with lncRNA expression data. Univariate Cox regression analysis was performed to determine the prognostic chemokine-related lncRNAs with a p value of 0.05 via the survival package (Supplementary Table S3). In addition, differences in the expression of prognostic chemokine-related lncRNAs between tumor and normal samples were tested using Wilcoxon signed rank and shown as boxplots.
The consensus clusters were determined based on the expression and underlying biological features of prognostic chemokine-related lncRNAs by the ConsensusClusterPlus package (pfeature = 1, resample rate = 0.8, and iterations = 50). The optimal k value (k = 2) was determined to obtain relatively stable clusters, namely, clusters 1 and 2. The prognostic value of COAD patients in subgroups was analyzed using the Kaplan–Meier method and log-rank tests. The Chi-square test or Fisher's exact test was utilized to determine the relationship between clinical characteristics and clusters. In addition, the differential expression and clinical features of prognostic chemokine-related lncRNAs were displayed using the pheatmap package.
We used the CIBERSORT algorithm to assess immune cell infiltration, converting a matrix of gene expression in the sample into the content of immune cells, with a pvalue < 0.05 indicating reliable cellular composition (Supplementary Table S4). Immune, stromal, and ESTIMATE scores were calculated to compare immune infiltration between the subgroups using the ESTIMATE algorithm by the limma and ggpubr packages (Supplementary Table S5). Differences in immune cell infiltration between the two clusters were verified using the vioplot package. Differences in immune checkpoint inhibitor molecules between subgroups were assessed using the Wilcoxon test. In addition, the coexpression correlation between chemokine-related lncRNAs and immune checkpoint inhibitors was detected by corrplot and limma packages.
The 52 prognostic chemokine-related lncRNAs were used to construct the risk model by LASSO regression analysis. COAD patients with survival data were randomly divided into training and testing groups using R caret, glmnet, surviner, and the survival packages. The training cohort was used to build the risk model, and the entire cohort and the test cohort were used to validate the risk model (Supplementary Table S6). We identified ten chemokine-related lncRNAs to build a risk model. The risk score formula was as follows: risk score = ∑i=1nCoefi∗Expi, where Coefi represents the coefficient, and Expi represents the expression value of chemokine-related lncRNA. The training and test groups were divided into the high-risk and low-risk groups based on the median score. The prognostic significance of the high- and low-risk groups was assessed using the survival package. Receiver operating characteristic (ROC) curves were used to evaluate the predictive accuracy and validity of the model via the “survivalROC” package. We plotted risk curves for COAD patients in the training and test groups and evaluated survival status and risk with the training and testing groups.
To investigate whether risk score might be an independent prognostic factor, and the clinical characteristics of COAD patients by univariate and multivariate Cox regression analysis, ROC curve was used to verify the clinical application value of the risk model. The predictive power of risk scores in age, sex, grade, stage, and TNM stage subgroups was validated by stratified survival analysis. We constructed a nomogram to predict the survival time of COAD patients using the “survival” and “regplot” R packages, and the accuracy of the nomogram was assessed by obtaining a calibration curve using the “rms” package. The hallmark (h.all.v6.2.entrez.gmt) and KEGG were acquired from the Molecular Signatures Database (MSigDB) using GSEA V3.0 and the GSEABase, and reshape2 packages. The false discovery rate FDR < 0.05 and p < 0.05 was statistically significant. Furthermore, the potential biological mechanisms of high- and low-risk groups were investigated using gene set variation analysis (GSVA).
The immune cell infiltration in all tumor samples was calculated using different software (XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, CIBERSORT); a pvalue < 0.05 indicated that the inferred cellular composition is reliable (Supplementary Table S7). The immune cell correlation analysis showed which immune cells were associated with the patient's risk score and obtained a correlation bubble plot using the scales and tidyverse packages. We examined the differences between the two groups for immune, stroma, and ESTIMATE scores by Wilcoxon's test and plotted the results as vioplot. The correlation analysis of prognostic chemokine-related lncRNAs and immune checkpoint inhibitory molecules were detected using the R packages “limma,” “reshape2,” “ggplot2,” and “ggpubr,” and then plotted by the corrplot package. Furthermore, single-sample gene set enrichment analysis (ssGSEA) was utilized to evaluate the differences in immune-related pathways between high-risk and low-risk groups by using the R packages “limma,” “GSVA,” “GSEABase,” “ggpubr,” and “reshape2”.
Tumor mutation burden (TMB) data of COAD was downloaded from the TCGA database. The COAD patients were classified into high or low TMB groups based on median values. The correlation between the TMB and risk model was verified by using “ggpubr,” “reshape2,” and “ggplot2” packages. We also visualized the top 20 genes with the highest mutation frequency in high-risk (Supplementary Table S8) and low-risk groups (Supplementary Table S9) using the maftools package. Survival differences among patients with different TMB statuses and risk scores were examined by survival analysis.
We downloaded the data of the immunotherapy score from the TCIA database (Supplementary Table S10) and analyzed the effects of immunotherapy in high- or low-risk groups. Then, the drug sensitivities were assessed in patients with different risk groups using the limma, ggpubr, and pRRophetic packages, which predict 50% inhibitory concentration (IC50) of common drugs for COAD. Subsequently, we determined drug sensitivities in different risk groups and screened for potential therapeutic agents that might affect patient survival. Differences between groups were assessed using the Wilcoxon signed-rank test, with p < 0.001 as the screening criterion.
We collected 20 human COAD tissues and adjacent normal tissues from the First Affiliated Hospital of Chongqing Medical University. This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University, and all patients signed the informed consent. Total RNA from COAD samples was extracted using the Trizol reagent (Takara, Japan) according to the manufacturer's protocol. Total RNA was reverse-transcribed to cDNA using the PrimeScript™ RT Reagent Kit (#RR037A, Takara, Japan). All primers were designed and synthesized by Sangon Biotech (Sangon Biotech, China, Supplementary Table S11). The qRT-PCR assays were performed using TB Green Premix Ex Taq II (Takara, #RR820A). The relative expression (fold change) of the target molecules was calculated using the 2 − ΔΔCT method. GAPDH was the internal control.
All data were analyzed, and images were generated using R (version 4.1.3) and GraphPad Prism (version 8.03, GraphPad Software Inc., USA). Pearson correlation test was used for the correlation analysis. Survival analyses were performed using the Kaplan–Meier method with a log-rank test. Wilcoxon signed-rank test and the Kruskal-Wallis test were used for comparison between groups. The results of PCR experiments were expressed as mean ± SD, and statistical significance was determined by paired t-test. A pvalue < 0.05 indicated statistical significance.
This study's workflow is shown in Figure 1. After obtaining chemokine-related lncRNAs, we initially identified 52 prognostic chemokine-related lncRNAs by using univariate Cox regression analysis (Figure 2(a)). The expression of 52 prognostic chemokine-related lncRNAs in tumor and normal tissues was detected and displayed as heatmaps and boxplots (Supplementary Figures S1A, S1B). Based on the similarity in the expression of prognostic chemokine-related lncRNAs, consensus clustering showed that COAD patients were divided into 2 subgroups, the cluster stability was the best, and the CDF value was the lowest. Therefore, the lncRNAs were divided into clusters 1 and 2 (Figures 2(b)–2(d)). To assess the survival of chemokine-related lncRNAs in different clusters, survival analysis showed that patients in cluster 2 had lower overall survival than those in cluster 1 (Figure 2(e)). The heatmap showed that the clinical parameters of COAD patients in the two clusters were not significantly different (Figure 2(f)). Afterward, we found significant differences in the proportion of infiltrating immune cells (TIICs) in each COAD sample, providing clues for further investigation of chemokine-related prognostic lncRNAs in the tumor microenvironment (Supplementary Figure S1C).
We initially analyzed the differences in immuneScore and immune cell infiltration and exhibited a vioplot between clusters 1 and 2. Immune cells such as Neutrophils and T cells follicular helper were highly clustered in cluster 2, whereas Mast cells resting, Dendritic cells resting, and T cells CD4 memory resting were highly aggregated in cluster 1 (Figure 3(a)). Based on the ESTIMATE algorithm, the immuneScore, stromalScore, and ESTIMATEScore in cluster 2 were dramatically higher than in cluster 1 (Figures 3(b)–3(d)). Then, we verified the correlation among 22 immune cells in COAD. For example, immune cells such as Macrophages M0, Mast cells activated, NK cells resting, and T cells CD4 memory activated were markedly negatively correlated with cells such as T cell CD8, NK cells activated, Mast cells resting, Dendritic cells resting, B cells naïve, and Eosinophils (Supplementary Figure S1D). Next, we examined the expression levels of some immune checkpoint molecules in the two clusters and the association of immune checkpoint molecules with prognostic chemokine-related lncRNAs. The expression levels of PD-L1, CTLA4, LAG3, PDCD1LG2, HAVCR2, SIGLEC15, and TIGIT were remarkably higher in cluster 2 than in cluster 1 (Figures 3(e)–3(k)). Furthermore, PD-L1, CTLA4, LAG3, PDCD1LG2, HAVCR2, and TIGIT were positively correlated with multiple prognostic chemokine-related lncRNAs, and only SIGLEC15 was negatively correlated with the expression of some chemokine-related lncRNAs (Figure 3(l)). Thus, we found that two chemokine-related lncRNAs clusters were observably associated with TME and immune checkpoint molecules.
To identify the most potent prognostic signature, the lasso regression analyses were performed to identify potential survival-related chemokine-related lncRNAs, resulting in the ten best candidates (Figures 4(a) and 4(b)). The 473 COAD patients were randomized into training and test cohorts, and a risk score was calculated for each patient and then divided into high- and low-risk groups based on the median risk score. The training cohort was used for the establishment of the risk model. The coef value of each lncRNA is shown in Figure 4(c). Ten lncRNAs were identified to construct the prognosis signature in COAD (Figure 4(d)). Based on the risk pattern of the risk model, we performed dimensionality reduction for the whole gene, 64 chemokine-related genes, and genes in the risk model by using principal component analysis (PCA) (Figure 4(e)). Survival analysis showed that the prognosis of COAD patients in the high-risk group was worse than that in the low-risk group in both the training cohort (Figure 4(f)) and the test cohort (Figure 4(g)). To test the accuracy of the risk model in predicting survival, the ROC curve revealed that prognostic chemokine-related lncRNAs accurately predicted overall survival in the training cohort, with AUCs of 0.730, 0.773, and 0.806 for 1-, 3-, and 5-year overall survival rates (Figure 4(h)). ROC results also displayed a curve (AUC) of 0.680, 0.781, and 0.697 for the test cohort's 1-, 3-, and 5-year overall survival rates (Figure 4(i)). Subsequently, we achieved risk curves and assessed the survival status and risk of prognostic chemokine-related lncRNAs (Figures 5(a) and 5(b)). As the risk scores increased, the number of deaths and the high-risk patient ratios enhanced. The expression of protective lncRNAs (AC004846.1 and AL137782.1) decreased with increasing risk scores, while the expression of risk lncRNAs (AL513318.2, AP003555.2, VIM-AS1, MYOSLID, SNHG26, AL161935.3, AC004540.2, and AC073611.1) increased with increasing risk scores (Figures 5(c), 5(d)). Thus, our risk model had excellent distinguishing performance in predicting the prognosis and risk of COAD patients. Furthermore, we found ten prognostic chemokine-related lncRNAs were expressed differently in tumor and normal tissues and displayed as a vioplot (Figure 5(e)). The heat map revealed the significant differences in the grade, pT, and clinical stage between the high- and low-risk groups, disclosing a close correlation between clinical features and the risk model. The COAD patients with pT3-4 and G3 had higher risk scores than pT1-2 and G1. Likewise, the risk scores improved obviously as the clinical stage increased from stage I to stage IV (Figure 5(f)). These data suggested that the risk score was dramatically associated with the clinical characteristics of COAD patients. The above findings demonstrated that this risk model has robust and stable predictive power.
We further validated the correlation between the risk model and clinical features of COAD patients. Univariate Cox regression analysis revealed that age, grade, clinical stage, and risk score were associated with the prognosis of COAD patients in the training cohort (Figure 6(a)). Multivariate Cox regression analysis showed that age and risk scores were markedly correlated with the survival outcomes of COAD patients in the training cohort (Figure 6(b)). However, both univariate and multivariate analyses exhibited that the risk score was not associated with the prognosis of COAD patients in the test cohort (Figures 6(c) and 6(d)). To further explore whether the risk model is superior to other clinical features in terms of prognostic predictive role, the ROC curve confirmed that the risk model had higher efficiency than other clinical features in the training and test cohorts (Figures 6(e) and 6(f)). The nomograms and calibration curves were developed to quantify the prediction of individual survival probability at 1-, 3-, and 5 years (Figure 6(g)). The consistency index (C-index) and ROC of the nomogram were acquired to verify the accuracy and validity of the nomogram. We derived a C-index of 0.792 for the nomogram associated with multiple clinical parameters. The calibration curve revealed that the predicted overall survival was largely consistent with the actual observations at 1-, 3-, and 5 years (Figure 6(h)). For the ROC of the overall survival nomogram, the AUC values were 0.680, 0.737, and 0.697 at 1-, 3-, and 5 years, respectively (Figure 6(i)). Subsequently, the stratified survival analysis was applied to evaluate the predictive ability of the risk model for patients with different clinical parameters. Interestingly, we observed that patients in the low-risk group had better survival outcomes than those in the high-risk group in all subgroups. Details were as follows: among COAD patients with aged >60, aged ≤60 years old, female, male, tumor grade 1-2, tumor grade 3, pT3-4, pN0, pN1-2, pM0, pM1, stage I-II, and stage III-IV, the high-risk group had a worse prognosis than the low-risk group (Supplementary Figure S2). These clinical data analyses confirmed the good predictive performance of the risk model.
In this model, multiple active pathways were gained in high-risk or low-risk groups to study the KEGG pathway enriched by risk scores and model lncRNAs. GSVA results revealed numerous carcinogenic- and immune-related signaling pathways were noteworthily associated with chemokine-related lncRNAs and risk scores. For example, there was a positive correlation between the VEGF, Toll-like receptor, TGF-β, T cell receptor, Nod-like receptor, MAPK, JAK-STAT, and B cell receptor and patient risk scores, and these pathways were active in the high-risk group (Figure 7(a)). Then, gene set enrichment analysis (GSEA) was performed to ascertain the enrichment pathways in low-risk patients. These pathways included the base excision repair, DNA replication, citrate cycle, pentose phosphate pathway, protein export, nonhomologous end joining, selenium amino acid metabolism, ribosome, steroid biosynthesis, RNA polymerase, mismatch repair, and endometrial cancer (Figures 7(b)–7(h)). We found that this risk model was associated with tumor- and immune-related pathways in COAD.
Here, we used various software to calculate the infiltration status of COAD samples and obtain immune cell infiltration values. First, the correlation analysis of immune cell infiltration and risk score was calculated, showing that multiple immune cell infiltrations were associated with patient risk scores (Supplementary Table S12). The correlation bubbles displayed that the following immune cell infiltration levels were positively correlated with the risk score: memory B cells, naive B cells, naive CD4+ T cells, CD8+ T cells, monocyte, macrophage M1, myeloid dendritic cells activated, and activated mast cells. However, infiltration levels of resting NK cells and resting mast cells were inversely associated with the risk score (Figure 8(a)). We then examined significant positive associations between expression levels of multiple immune checkpoints and risk scores (Figure 8(b)). In the TME, the average immuneScore, stromalScore, and ESTIMATEScore were markedly higher in the high-risk group than in the low-risk group (Figure 8(c)). Thus, the immune-related data of the risk model were partially consistent with the chemokine-related lncRNAs cluster analysis. Next, we identified differences in 13 immune-related pathways between the high- and low-risk groups. The ssGSEA analysis indicated that 12 of the 13 pathways dramatically differed between the high-risk and low-risk groups, and these 12 pathways were more active in the high-risk group (Figure 8(d)). In addition, we examined numerous model molecules associated with immune cell infiltration, including VIM-AS1, AC004846.1, MYOSLID, and AL161935.3 (Supplementary Table S13). Our findings suggested that the risk model was closely correlated to immune cell infiltration and TME, which could predict immune cell infiltration and TME in COAD to a certain extent.
We downloaded TMB data from TCGA-COAD samples using R's “maftools” and divided the TMB data into high-risk and low-risk data based on the risk score. The TMB status was then calculated and analyzed in the high-risk and low-risk groups. Except for APC, TP53, and LRP1B molecules, the mutation rate in the high-risk group was more than 5% higher than that in the low-risk group (Figures 9(a) and 9(b)). We observed the risk score was positively associated with TMB levels (Figure 9(c)). We also compared the differences in TMB between low-risk and high-risk groups, and the results revealed that patients in the high-risk group had higher TMB levels than in the low-risk group (Figure 9(d)). We divided the patients into the high-TMB and low-TMB groups based on the TMB levels and analyzed survival outcomes. The results indicated that patients with high TMB had a poor prognosis compared with patients with low TMB (Figure 9(e)). COAD patients with high-risk scores in the high TMB group had the worst survival outcomes. COAD patients in the low TMB group with high-risk scores also had worse survival outcomes than low TMB with low-risk scores (Figure 9(f)). Thus, the risk model was associated with TMB and prognosis.
Chemotherapy and targeted therapy are current strategies to treat COAD; it is critical to understand the effectiveness and sensitivity of these drugs to different risk groups. We predicted the sensitivities to common anticancer drugs, chemotherapeutics, and targeted agents in high- and low-risk groups of COAD patients. The IC50 values of Camptothecin, Cisplatin, Docetaxel, Vinblastine, Elesclomol, Pazopanib, Bexarotene, and Temsirolimus in the high-risk group were lower than those in the low-risk group, indicating that these drugs are more sensitive to the high-risk patients (Figures 10(a)–10(h)). In contrast, the low-risk group was more sensitive to BIRB.0796 (Doramapimod) (Figure 10(i)). Risk stratification also revealed remarkable differences in drug sensitivity between high- and low-risk groups for many other drugs (Supplementary Figure S3). Next, we downloaded immunotherapy score data from the TCIA database and obtained the difference in immunotherapy scores between high- and low-risk groups. The results exhibited that low-risk patients who were single positive for CTLA4 and negative for both PD-L1 and CTLA4 had higher immunotherapy scores, indicating that the patients in the low-risk group would benefit from immunotherapy (Figures 10(j)–10(k)). Therefore, our risk model was a potential target for multiple drugs and had vital implications for guiding the personalized treatment of patients with COAD.
To validate more valuable lncRNAs in the risk model, we further examined each lncRNA's prognostic value and expression in COAD patients. Except for AL137782.1, the remaining high-expressing lncRNAs had a worse prognosis in COAD patients than low-expressing lncRNAs, indicating the expression levels of most lncRNAs in the risk model guide patient prognosis (Figure 11(a)). Next, we collected 20 COAD tumors and adjacent normal samples and then conducted the qRT-PCR assays to examine the expression levels of these lncRNAs in clinical samples. Six of ten lncRNAs were differentially expressed between tumor and normal samples, including AL513318.2, AP003555.2, VIM-AS1, MYOSLID, AL137782.1, and AC073611.1 (Figure 11(b)). The expression trends were consistent with those listed in TCGA-COAD data. These results suggested that the most lncRNAs of this risk model might exert a more vital function in COAD.
Numerous studies have reported that chemokine modification events are involved in tumor progression, including promoting cancer cell differentiation or regulating tumor formation and metastasis potential [10, 23]. Studies have also emphasized that chemokines regulate multiple biological processes, including mammalian development, stem cell renewal, immune responses, drug resistance, and tumor progression [24]. For example, Zeng et al. discovered that the CCL5/CCR5 axis is involved in the pathological processes of different diseases such as inflammation, chronic diseases, cancer, and infection of COVID-19 and the related signaling pathways of its regulatory axis [25]. Chen et al. found that the CXCL2/CXCR2 axis induced cancer stem cell signatures in CPT-11-resistant LoVo colon cancer cells [26]. Due to the limited predictive power of general prognostic models, a novel prognostic chemokine-related lncRNAs model could improve the monitoring and management of malignancies such as COAD. The study of prognostic chemokine-related lncRNAs is of great significance for guiding the direction and goals of COAD research. In this study, we first explored the differences between the survival outcomes and immune status of two chemokine-related lncRNAs clusters by consensus clustering. We then constructed a risk model of prognostic chemokine-related lncRNAs and validated the validity and accuracy of the model in predicting survival and clinical parameters in COAD patients. Our further analysis showed that the risk model played a vital role in predicting immune cell infiltration, immune checkpoint gene expression, tumor mutational burden, immunotherapy score, and drug sensitivity in COAD patients. Furthermore, we analyzed the prognostic value and expression of each lncRNA in this model in COAD patients. This study provided clues for COAD progression and treatment by comprehensively analyzing the characteristics of novel prognostic chemokine-related lncRNAs associated with the immune landscape. We first obtained RNA-seq profiles of 473 COAD samples from the TCGA dataset and extracted the chemokine-related lncRNAs data. Fifty-two prognostic chemokine-related lncRNAs were identified in COAD patients by univariate Cox regression analysis. By consensus clustering, COAD patients were classified into subgroups based on the consistent expression of prognostic chemokine-related lncRNAs. COAD patients in cluster 2 had worse overall survival than patients in cluster 1, suggesting that the prognostic chemokine-related lncRNAs cluster affects the survival of COAD patients. Clinical correlation analysis revealed no significant differences in the clinical parameters of COAD patients between the two clusters. Afterward, we demonstrated that COAD patients in cluster 2 had higher immune scores, stromal scores, and ESTIMATE scores than those in cluster 1, suggesting a higher degree of immune infiltration in cluster 2 than in cluster 1. These findings were similar to previous studies that demonstrated lower overall survival in patients with tumors with high immune and stromal scores [27, 28]. We also found that multiple immune checkpoint molecules were expressed at higher levels in cluster 2 than in cluster 1, implying that the clustering pattern of chemokine-related lncRNAs is closely related to TME. Jin et al. reported that various lncRNAs could indirectly regulate the expression of immune checkpoint molecules, thereby affecting the survival outcomes of tumor patients [29]. Thus, we speculated that cluster 2 might enhance the expression of immune checkpoint molecules through numerous pathways, causing the decreased overall survival of COAD patients. To further explore the role and value of chemokine-related lncRNAs in COAD, the 10 chemokine-related lncRNAs were identified to construct the risk model using LASSO Cox regression analysis. Survival analysis found that in the training set, the survival outcomes of patients in the high-risk group were worse than those in the low-risk group. The AUC value of the ROC curve confirmed the risk model's efficiency and accuracy for the training and the test cohorts. The high-risk and the low-risk groups also showed significant differences in grades, pT, and clinical stage, and the risk model also showed close correlations between clinical parameters. Then, the expression levels of 10 prognostic chemokine-related lncRNAs differed between tumor and normal tissues. Tu et al. showed that TCF4 enhanced colorectal cancer liver metastasis by regulating tumor-associated macrophages through the CCL2/CCR2 signaling pathway [30]. Jie et al. reported that targeting KDM4C enhanced CD8 T cell-mediated antitumor immunity by activating the transcription of the chemokine CXCL10 in lung cancer [31]. The above findings nicely explained that some chemokine-related lncRNAs are overexpressed in tumors to act as oncogenes, while others are highly expressed in normal tissues as tumor suppressors. Subsequently, we explored that the risk score of the risk model was an independent prognostic factor in predicting the survival outcomes of COAD patients. ROC curves were also performed to validate risk score accuracy in independent prognostic functions. All patients with different clinical characteristics in the high-risk group had worse survival outcomes than in the low-risk group. Next, the nomogram predicted survival time was almost consistent with the actual survival time. For example, Liang et al. reported that the chemokine signature was identified for predicting overall survival in gastric cancer and showed good predictive efficiency, similar to our model [32]. In total, our risk model has sufficient efficiency and accuracy in predicting the survival outcomes of COAD patients. Recent studies have reported that chemokines modifications and multiple lncRNAs can modulate the process of cancer immunity, including immune cell infiltration and immune resistance and activation in the TME, causing tumor progression [33, 34]. Thus, to explore whether the risk model played a role in tumor and TME, we first performed GSEA and GSVA analyses. Multiple cancer- and immune-related pathways were associated with the risk model, such as the VEGF, Toll-like receptor, TGF-β, T cell receptor, Nod-like receptor, MAPK, JAK-STAT, and B cell receptor, base excision repair, DNA replication, citrate cycle, pentose phosphate pathway, protein export, nonhomologous end joining, selenium amino acid metabolism, ribosome, steroid biosynthesis, RNA polymerase, mismatch repair, endometrial cancer, and these pathways were enriched in the high-risk group. Also, some studies have proved that these pathways could regulate immune cell infiltration and TME [23, 35]. Based on this, we considered that the risk model was likely to affect cancer immune processes in COAD, including immune cell infiltration, immune resistance and activation, and immune checkpoint molecules. Then, we found that the infiltration levels of memory B cells, naive B cells, naive CD4+ T cells, CD8+ T cells, monocyte, macrophage M1, myeloid dendritic cells activated, and activated mast cells were positively associated with the risk score, suggesting that these cells were more infiltrated in high-risk patients. Other resting NK cells and resting mast cells were increased in the low-risk patients, meaning that low-risk patients have more infiltration of these cells. Studies have reported that tumor patients have a variety of immune cell infiltration involved in tumor progression. For example, patients with more CD4+ and CD8+ T cell infiltration responded better and benefited from immunotherapy [36]. The massive infiltration of macrophages in solid tumors can promote tumor progression and distant metastasis, resulting in poor patient survival and weak treatment outcomes [37]. Furthermore, the high-risk patients had higher immune, stroma, and ESTIMATE scores than the low-risk patients, indicating that the TME in the high-risk group had more immune infiltration than the low-risk group. These data were similar to previous studies showing that malignancies with high immune and stromal scores had a worse overall survival [38]. The literature also suggested that more tumor-infiltrating immune cells in the high-risk group were associated with an increased risk of recurrence and poorer survival [39]. Thus, we speculated that lower immunoreactivity and higher immunosuppression in the TME would cause worse survival in high-risk patients. These results supported this risk model as a predictor of immune landscape in COAD patients. The expression levels of immune checkpoint molecules and TMB are considered effective immunotherapy indicators. Studies have shown that gastric cancer patients with higher immune checkpoint gene expression and higher somatic mutations have better effects on immunotherapy [40]. We determined the expression of immune checkpoint molecules corresponding to risk score and model lncRNAs; the data revealed that the most immune checkpoint molecules were significantly associated with high-risk patients, suggesting that risk score was closely related to immune status. Therefore, we speculate that high-risk patients may be more sensitive to immunotherapy. Risk scores were subsequently found to be positively correlated with TMB, suggesting that patients with high-risk scores had higher levels of TMB. Meanwhile, high-risk patients with high TMB also displayed the worst prognostic outcomes. Kim et al. constructed a novel TME signature and found that gastric cancer progression may be affected by TME and frameshift mutations, similar to our model [41]. Thus, this risk model was strongly associated with immune cell infiltration and TMB. However, further study is required to investigate whether immune cell infiltration is affected by TMB. Jung et al. reported that the clinical utility of chemotherapy and targeted agents for COAD had been extensively studied [42]. Afterward, we assessed the sensitivity and efficacy of immunotherapy in high- and low-risk groups. The low-risk patients who were single positive for CTLA4 and negative for both PD-L1 and CTLA4 had higher immunotherapy scores, meaning that these low-risk patients would benefit from immunotherapy. The high-risk group was also markedly associated with susceptibility to multiple targeted drugs, including commonly used clinical chemotherapy cisplatin, docetaxel, vinblastine, and some novel drugs. These data suggested that this prognostic chemokine-related lncRNAs risk model has potential utility in estimating efficacy and sensitivity to various medications. To validate a reliable and accurate risk model, we not only investigated the correlation between the expression of each lncRNA and patient prognosis but also detected the expression levels of lncRNAs in clinical samples. Many highly expressed lncRNAs have a worse prognosis in COAD patients than lowly expressed lncRNAs. Many lncRNAs in the risk model were expressed differently between tumor and normal tissues, and these expression trends were consistent with the trend in the TCGA-COAD data. These findings further demonstrated that lncRNAs in the risk model have more excellent research value. Nonetheless, the current study has some limitations. The risk model was created using public data and lacks enough clinical samples and data. The expression of 10 chemokine-related lncRNAs containing this signature was validated on only 20 pairs of clinical samples. Furthermore, the biological functions and mechanisms of prognostic chemokine-related lncRNAs in COAD remained uncertain, and experimental studies were needed to verify these findings. Based on this, we will expand the sample size for validation and conduct further experimental studies.
In conclusion, we first explored the differences between the survival outcomes and immune status of two chemokine-related lncRNAs clusters by consensus clustering. We then constructed a novel risk model of prognostic chemokine-related lncRNAs and validated the validity and accuracy of the model in predicting survival and clinical parameters in COAD patients. The risk model also played a vital role in predicting immune cell infiltration, TME, TMB, immunotherapy, and drug sensitivity in COAD patients. These findings elucidated the critical role of novel prognostic chemokine-related lncRNAs in prognosis, immune landscape, and drug therapy, thereby providing valuable insights for prognosis assessment and personalized treatment strategies for COAD patients. |
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PMC9649321 | Yong Jiang,Qian Yan,Miaofen Zhang,Xueying Lin,Chenwen Peng,Hui-ting Huang,Gang Liao,Qiong Liu,Huili Liao,Shao-feng Zhan,Xiaohong Liu,Xiufang Huang | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome | 03-11-2022 | Background Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. Methods Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. Results The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells (P = 0.013), T follicular helper cells (P = 0.002), resting mast cells (P = 0.004), and neutrophils (P = 0.002) between patients with SA and mild-moderate asthma patients. Pearson's correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. Conclusion Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy. | Identification of Molecular Markers Related to Immune Infiltration in Patients with Severe Asthma: A Comprehensive Bioinformatics Analysis Based on the Human Bronchial Epithelial Transcriptome
Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy.
Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration.
The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells (P = 0.013), T follicular helper cells (P = 0.002), resting mast cells (P = 0.004), and neutrophils (P = 0.002) between patients with SA and mild-moderate asthma patients. Pearson's correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets.
Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy.
Severe asthma (SA) is a chronic respiratory disease that worsens with a reduction in treatment with high-dose inhaled corticosteroids and long-acting β2 agonist (ICS-LABA), or symptoms persist despite treatment with high-dose therapy targeting the causative agent, as defined by the Global Initiative for Asthma (GINA) 2022 [1]. Unfortunately, approximately 300 million people suffer from asthma worldwide, of which 3-10% are diagnosed with SA [1, 2]. According to statistics, the death rate of patients with asthma has not significantly decreased in the past 30 years, indicating that asthma/SA has not been well controlled [3]. Unsurprisingly, SA has imposed substantial physical and economic burdens on patients and society [4–6]. SA is clinically characterized by chronic and persistent inflammation and airway hyperreactivity (AHR). The inflammatory phenotype of SA may be dominated by type 2 inflammation, neutrophilic inflammation, and mixed inflammation, which is possibly regulated by T lymphocytes, neutrophils, eosinophils, and airway epithelial cells [7]. Airway epithelial cells, frontline guardians of the body's defense system, play important roles in regulating innate and adaptive immunity [8]. Proinflammatory cytokines may lead to the degradation of the rhythmic circadian repressor REV-ERBα in airway epithelial cells, resulting in a rhythmic inflammatory response that may be associated with asthma [9]. In addition, the mechanism underlying immune cell infiltration in patients with SA has attracted increasing attention from researchers. For example, type 2 innate lymphoid cells (ILC2s) induce eosinophilic infiltration and AHR, which are associated with the onset of type 2 asthma [10]. Therefore, approaches targeting airway epithelial cells and mechanisms of immune cell infiltration may reveal important strategies to control and improve SA. Weighted gene coexpression network analysis (WGCNA) is a system biology method that has been used to find the important modules with highly correlated genes in microarray samples. WGCNA can identify therapeutic targets for SA by linking genes in modules with external sample features [11]. CIBERSORTx, a commonly used technical method in the field of immunology, uses a deconvolution algorithm to evaluate the infiltration of immune cells in tissues [12]. The aim of this study was to identify novel mechanisms underlying immune cell infiltration and biomarkers of SA using transcriptomic data from airway epithelial cells. The workflow of the bioinformatics analysis is shown in Figure 1.
We obtained the genomic mRNA profiles and corresponding clinical data for patients with SA from the NCBI Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database [13] with the following criteria: (“asthma” [MeSH Terms] OR asthma [All Fields]) AND (“gse” [Filter] AND “Homo sapiens” [Organism] AND “Expression profiling by array” [Filter]). In addition, the dataset must contain clinical information for patients with varying levels of asthma severity, and the samples must originate from bronchial epithelial cells. We also excluded childhood asthma and datasets with fewer than 30 samples. Hence, the GSE43696, GSE76226, and GSE89809 datasets were selected in subsequent studies. Because the data in this study were derived from public databases, the approval of the ethics committee was not needed.
The ComBat function was used to normalize the datasets from different batches for further analysis, and the R package “SVA” was used to eliminate the batch effect [14]. Then, the principal component analysis (PCA) was performed to evaluate whether the batch effect was removed.
The top 5000 genes in three datasets (sorted by adjusted P value) and the “WGCNA” package in R were used to construct WGCNA network and determine the coexpression modules related to SA [11]. First, the goodSampleGenes function in WGCNA was used to find missing values, and we performed a cluster analysis on 225 samples to identify free samples. An R-square value of 0.85 was set as the screening criterion, scatter plots of the fitting index, average connectivity, and soft threshold (power) were constructed to determine the best scale-free network, and the best weighting coefficient β was selected. The dynamic hybrid cutting method was used to identify modules, and similar modules were clustered and merged. The following parameters were set: minModuleSize = 60, mergeCutHeight = 0.3, deepSplit = 2, and verbose = 3. Finally, we performed Pearson's correlation analysis between modules and clinical features. The characteristic genes associated with SA were selected for subsequent analysis, and P < 0.05 was set as the filtered criterion.
The Gene Ontology (GO) enrichment analysis included biological processes (BPs), cellular components (CCs), and molecular functions (MFs). The R package “clusterProfiler” was used to perform GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes associated with significant modules and further explore the molecular mechanism associated with SA [15]. GO terms or KEGG terms with the criterion of P < 0.05 were considered significantly enriched.
The protein-protein interaction (PPI) network was constructed using the STRING database (version 11.0, https://www.string-db.org/) to show the correlations of various protein targets in important modules. Molecular Complex Detection (MCODE) analysis in Cytoscape software 3.8.2 (https://www.cytoscape.org/) was performed to screen central nodes in the key modules, and the screening criteria were set as follows: degree cutoff = 2, node score cutoff = 0.2, k − core = 5, and max depth = 100 [16]. The “degree” value in the “CytoHubba” plugin in Cytoscape was used to screen key targets in the module. Finally, the Network Analyst (version 3.0, https://www.networkanalyst.ca/) online website was used to visualize the key modules [17].
We uploaded the normalized gene expression profiles from the GSE43696, GSE76226, and GSE89809 datasets to CIBERSORTx (https://cibersortx.stanford.edu/) [12]. CIBERSORTx used the deconvolution algorithm to calculate the abundances of immune cells in each sample based on the expression levels of 22 immune cell-related genes and the matrix of gene expression [18].
The “ggstatsplot” R package (https://CRAN.R-project.org/package=ggstatsplot) was used to perform Pearson's correlation analysis between the identified genes and the levels of infiltrating immune cells with R and to further analyze the immune mechanism involved in the development of SA.
Network Analyst (version 3.0), a visualization platform, was used to construct the interaction network between TFs and genes [17]. The basic data for the TF-gene network were obtained from the ENCODE database (https://www.encodeproject.org/) and visualized using Cytoscape software 3.8.2 [16].
The ToppGene Database (https://toppgene.cchmc.org/help/help.jsp) was utilized to identify the potential pharmacological drugs for SA [19]. The list of 11 hub genes was used to capture drug-gene interactions by using the ToppGene Database.
A ROC curve analysis was conducted for each hub gene in patients with mild asthma (MA) and SA. The area under the curve (AUC) was calculated to evaluate the diagnostic accuracy of the six hub genes for SA by using the “pROC” R package [20].
All statistical analyses were performed using R (version: 4.1.0, https://www.r-project.org/). The Wilcoxon test was used to judge whether the two groups had differences in immune cell infiltration. Pearson's correlation coefficients were calculated to determine the associations between modules and clinical factors, and Spearman's correlation analysis was performed to evaluate the associations between candidate genes and immune cells. The Bonferroni method was used to correct the P values, and an adjusted P value < 0.05 was set as the filtering criterion.
GSE43696 was obtained from the GPL6480 platform (Affymetrix Human Genome U133 Plus 2.0 Array), including 50 patients with moderate asthma and 39 patients with SA. GSE76226 was derived from the GPL13158 platform and included 36 patients with moderate asthma and 63 patients with SA. GSE89809 originated from the GPL13158 platform and comprised 28 patients with mild asthma, 13 patients with moderate asthma, and 11 patients with SA. The normalized results of the PCA based on the scatter plot showed that the batch effects generated by the three datasets corresponding to different platforms were significantly removed (Figure 2).
As a method to identify SA-related coexpression modules, the R package “WGCNA” was used to construct signed networks based on the expression levels of 11105 genes in the three datasets. First, the cluster analysis of samples did not reveal free samples. The correlation coefficient was greater than 0.85 when β equaled to 6 (Figure 3). The coexpression network was a scale-free network, and 14 coexpression modules were identified (Figure 4(a)). The blue module (correlation coefficient = −0.26, P = 9e − 05) and salmon module (correlation coefficient = 0.26, P = 6e − 05) had the strongest correlations with SA. The blue module was negatively correlated with the asthma control questionnaire (ACQ), allergic rhinitis (AR), inhaled corticosteroid (ICS) dose, oral corticosteroid (OCS) dose, and Global Initiative for Asthma (GINA) control. The blue module positively correlated with the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and reversibility. The salmon module was negatively correlated with gender, AR, nasal polyps (NP), FEV1, and FVC, while the correlation between the salmon module and age, ACQ score, smoking, ICS, OCS, reversibility, and GINA showed a positive relationship (Figure 4(b)). Each module was associated with specific symptoms of asthma, except for the genes in the gray modules that had no clinical significance.
We performed GO (BP) and KEGG enrichment analyses of 14 coexpression modules. The blue module was mainly enriched in actomyosin structure organization, muscle cell differentiation, cilium organization, striated muscle cell differentiation, sodium ion transmembrane transport and positive regulation of the catabolic process, and signaling pathways involved in regulating pluripotency of stem cells. The salmon module was mainly enriched in cornification, skin development, and various metabolic processes, as well as glycosphingolipid biosynthesis-lacto and neolacto series, mucin-type O-glycan biosynthesis, and the estrogen signaling pathway. The top five pathways in all modules were ribosome, coronavirus disease-COVID-19, antigen processing and presentation, graft-versus-host disease, and allograft rejection, as ranked by adjusted P values, all of which were associated with the regulation of the host's immune system (Figure 5).
The genes in the blue and salmon modules were uploaded to the STRING database for the PPI network analysis. The blue module contained 1731 nodes and 4228 edges, and the salmon module contained 144 nodes and 70 edges with a combined score greater than 0.7 and deletion of discrete targets (Figure 6). Then, we used the MCODE function in Cytoscape software 3.8.2 to analyze the key modules, and CytoHubba was used to screen the critical nodes with the highest degree values in the module. The blue module contained 10 modules, among which the 10 hub genes were RNF126, PLK1, ADCY4, RAB6A, CPSF4, IFT57, CYFIP1, MRPL1, SMAD4, and CAT. The salmon module contained a module that met the requirements, and the central gene was KRT18 (Figure 7).
CIBERSORTx was used to calculate the deconvoluted P value for each sample to provide a measure of confidence in the result, and P < 0.05 was considered statistically significant. We filtered out the immune cells that were not abundant in the samples (gamma delta T cells). The results of the analysis of differential infiltration analysis of the 21 types of immune cells showed that the differences in the ratios of CD8 T cells (P = 0.013), follicular helper T cells (P = 0.002), resting mast cells (P = 0.004), and neutrophils (P = 0.002) between patients with SA and mild-moderate asthma were statistically significant. This result implied that the contents of CD8 T cells, follicular helper T cells, and resting mast cells in SA samples were lower than those in mild-moderate asthma samples, and greater neutrophil infiltration was observed in samples from patients with SA (Figure 8).
Pearson's correlation analysis was performed to calculate the correlation coefficients between 11 hub genes and 21 immune cells. M0 macrophages were positively correlated with CYFIP1 and MRPL1 and negatively correlated with KRT18. M1 macrophages were positively correlated with PLK1 and negatively correlated with SMAD4 and CAT. M2 macrophages were negatively correlated with ADCY4 and positively correlated with MRPL1 and CAT. Activated CD4 memory T cells were positively correlated with ADCY4 and negatively correlated with IFT57. Resting memory CD4 T cells were positively correlated with IFT57 and SMAD4. T cell follicular helper was negatively correlated with RAB6A and CAT. Naive CD4 T cells were positively correlated with CPSF4 and negatively correlated with SMAD4 and CAT. Tregs was negatively correlated with MRPL1. Memory B cells were positively correlated with IFT57. Activated NK cell activation was negatively correlated with MRPL1 and SMAD4. Resting NK cells were positively correlated with KRT18. Resting mast cells were positively correlated with RAB6A, and mast cell activation had a negative correlation with RAB6A. Activated dendritic cells were negatively correlated with RAB6A and positively correlated with KRT18. Statistically significant correlations were not observed between RNF126 and the 21 types of immune cells (Figure 9).
We constructed a regulatory network of 11 key genes and TFs to further explore the potential functions of key genes. TF-gene network included 178 nodes, 263 edges, and 10 seeds, indicating the possible regulatory roles between the hub genes and TFs. Since only subnetworks with at least 3 nodes were chosen to be displayed, the regulatory networks of 10 key genes and 263 TFs are shown in Figure 10 and Supplementary Table 1.
A total of 1985 drugs were screened from the DrugBank, STITCH, CTD, and Broad Institute CMAP Down databases by targeting these 11 genes. We selected the top 30 drugs as candidates according to the FDR value and found that lupenol, thymoquinone, and naringin had potential therapeutic effects on asthma. Surprisingly, the result showed that CAT was the common target gene for all predicted drugs. Lupenol and naringin were mapped with CPSF4 and CAT, while thymoquinone may directly target RAB6A and CAT. The above drugs had the great potential to develop into the therapeutic drugs against SA (Table 1).
Higher AUC values indicated the more promising diagnostic performance. ROC curves were constructed to assess the diagnostic abilities of 11 hub genes to distinguish moderate asthma from SA in the GSE69683 and GSE136587 datasets. The AUC values (>0.6) for the hub genes in two datasets were listed as follows: SMAD4 (0.606, Figure 11(a)) and CPSF4 (0.619, Figure 11(b)) in the GSE69683 dataset. PLK1 (AUC = 0.619) and KRT18 (AUC = 0.610) had higher AUC values in the GSE136587 dataset. Other genes had AUCs between 0.5 and 0.6 (Figures 11(c) and 11(d)).
SA is a highly heterogeneous disease characterized by uncontrolled symptoms, frequent deterioration, and varying degrees of decreased lung function. Dysregulated inflammation is a key feature of asthma and is regulated by the innate and adaptive immune responses of the immune system [21]. Considering the important role of SA in asthma, the identification of molecular biomarkers and differences in immune cell infiltration between patients with mild-moderate asthma and SA is very important for predicting progression and potential treatments for SA. Therefore, we aimed to use bioinformatics to identify the hub modules strongly related to SA and explore the immune-related mechanisms that may affect the deterioration of SA. As more than 15 samples are recommended for WGCNA, we obtained a total of 225 SA and non-SA samples from the GSE43696, GSE76226, and GSE89809 datasets to increase the robustness of the results [11]. Next, we normalized the three datasets and evaluated them with PCA analysis. A total of 11105 genes in the three datasets were used to construct 14 coexpression modules with the WGCNA method. We subsequently focused on the pathways and molecular markers of asthma deterioration and determined the correlations of each module with the important clinical factors. We found that the blue module (P = 9e − 05) and salmon module (P = 6e − 05) had the strongest correlations with SA among the 14 modules. The blue module was positively correlated with SA, and the salmon module was negatively correlated with SA. The analysis of correlations between these two modules and clinical factors further revealed the important value of the blue and salmon modules. Therefore, we continued to analyze the BPs and essential pathways related to these two modules. The blue module was mainly enriched in actomyosin structure organization, muscle cell differentiation, cilium organization, striated muscle cell differentiation, sodium ion transmembrane transport, positive regulation of the catabolic process, and signaling pathways involved in regulating pluripotency of stem cells. The salmon module was mainly related to cornification, skin development, various metabolic processes, glycosphingolipid biosynthesis-lacto and neolacto series, mucin-type o-glycan biosynthesis, and the estrogen signaling pathway. Mesenchymal stem cells (MSCs) can interact with a variety of immune cells, including T lymphocytes, B lymphocytes, and dendritic cells, and exert a strong regulatory effect on the immune system [22–24]. Gao et al. show that human-induced pluripotent stem cell-derived mesenchymal stem cells (iPSC-MSCs) play important roles in the differentiation and function of dendritic cells [25]. As shown in the study by Fang et al., it shows that iPSC-MSCs can regulate Th17 cells, thereby exhibiting the potential to attenuate neutrophil-mediated airway inflammation and inhibit the differentiation of human T helper cells in vitro [26]. Keratin, a member of fibrous structural proteins, can protect epithelial cells from stress and damage [27]. Disruption of epithelial barrier function, such as cornification or keratinization, might result in allergic diseases [28]. Inoue et al. find that downregulation of epithelial defense genes and keratinization occurs in SA mouse sensitized by Alternaria [29]. However, Lee et al. demonstrate that keratin KB40 shows an upward trend in asthma mice [30]. Epithelial damage and aberrant repair are present in adult asthmatic airways [31], and there might be potential association between keratinization and epithelial in SA, which needs to be explored and confirmed by more relevant studies. A study has reported that estrogen can enhance the polarization of M2 macrophages induced by IL-4 [32]. Ambhore et al. reveal that estrogen receptors can reduce the deposition of extracellular matrix through the NF-κB signal pathway and can be used as a target to regulate airway smooth muscle remodeling [33]. Going further, PPI and module analyses were conducted to determine the key genes in the blue and salmon modules. We obtained 11 key modules from the blue and salmon modules with the highest degree values, including RNF126, PLK1, ADCY4, RAB6A, CPSF4, IFT57, CYFIP1, MRPL1, SMAD4, CAT, and KRT18. PLK1, a serine/threonine-protein kinase, contributes to reducing airway resistance and airway hyperresponsiveness in asthmatic mice by regulating vimentin phosphorylation at Ser-56 [34]. Phosphorylated SMAD2 and SMAD3 integrate with SMAD4 and are involved in regulating gene transcription in the nucleus [35]. Wortley and Bonvini and Singh et al. suggest that TGF-β1 attenuates the relaxation of airway smooth muscle induced by β2 agonists in a SMAD2/3-dependent manner, and the TGF-β-SMAD4 axis may be a new therapeutic target for SA [36, 37]. Although the effect of RNF126 on asthma has not been previously reported, it is confirmed that RNF126 plays an oncogene role in a variety of cancers [38, 39]. Xu et al. report that PTEN as an inhibitor of PI3K/AKT signaling pathway can be used as a new substrate of RNF126 [38]. The study reveals that the potential molecular pathways for asthma include FOXC1-miR-PI3K/AKT, which indicates that RNF126 and PI3K/AKT pathways may have therapeutic effect in asthma [40]. Oxidative stress is the main feature of asthma, and genetic variants in the key oxidative defense gene-CAT may have the potential to regulate the risk of new-onset asthma [41]. Targeting oxidative imbalance may help to provide new therapies and researches for asthma treatment [42]. It is well recognized that ADCY4 is also known as AC4. Adenylyl cyclases (ACs) are important regulators of airway smooth muscle function, as β-adrenergic receptor (AR) agonists can enhance the activation of AC and increase airway diameter [43]. Bogard et al. assess the levels of AC isoforms in human bronchial smooth muscle cells (hBSMC) and find that AC2, AC4, and AC6 are expressed in hBSMC [44]. High levels of autoantibodies, such as anti-cytokeratin (CK) 18, are found in airway epithelial cells (AECs) [45] and the serum of SA patients [46]. However, the pathogenic mechanism of autoantigens in SA is still poorly understood, which may provide new strategies for the treatment of SA. In addition, there is currently no relevant research disclosing the relationship between RAB6A, IFT57, MRPL1, CYFIP1, CPSF4, and SA, which requires further research in the future. To further explore the immune infiltration mechanism of SA exacerbation, we used CIBERSORTx to analyze the difference in immune cell infiltration between SA and common asthma and the correlation with hub genes in SA. We found the lower levels of CD8 T cells, T cell follicular helper, and resting mast cells in SA samples than in common asthma samples, but there was more neutrophil infiltration in SA samples. Li et al. find a significant increase in the levels of CD8+ T cells in the bronchoalveolar lavage fluid (BALF) of SA patients through single-cell sequencing [47]. Transcriptome analysis conducted by Tsitsiou et al. also shows that SA is associated with circulating CD8(+) T cell activation [48]. Research confirms that activated CD8+ T cells show an upward trend in postmortem lung tissue samples of patients who died of acute asthma [49]. TFH cells are a subset of CD4+ T cells, which specifically assist the activation and differentiation of B cells to regulate humoral immunity [50]. TFH cells and iconic cytokine IL-21 are related to asthma progression and affect the production of IgE in asthma [51]. Clinical studies have shown that the frequency of TFH cells in peripheral blood mononuclear cells and/or IL-21 levels is positively correlated with IgE levels, which may be promising diagnostic biomarkers for asthma [52]. However, the heterogeneity between patients with moderate asthma and SA still needs further research. Mast cells are present in the skin and most of the mucosal tissues, but the number of mast cells significantly increases in the lungs of asthma patients [53]. And mast cells may play key roles in airway remodeling by releasing tryptase to smooth muscle and epithelial cells [54]. SA often manifests as airway mucosal neutrophil inflammation, and neutrophils can activate inflammatory pathways in AECs through oxidative stress, leading to the exacerbation of neutrophil asthma [7, 55]. More importantly, the formation of neutrophil autophagy and extracellular traps in peripheral neutrophils can increase the severity of asthma by triggering the inflammatory response of the airway epithelium [56]. Taken together, we confirm that our results originated from the analysis of immune infiltration of bronchial epithelial cell transcriptome are not completely consistent with the existing research progress, and further studies need to be carried out to explore the immune infiltration mechanism of SA. As immune response requires the synergy of immune-related genes and immune cells, we analyzed the correlation between 11 biomarkers of SA and 21 types of immune cells. Macrophages are divided into two types: classical activation (M1) and alternate activation (M2). Studies indicate that the presence of M1 macrophages is associated with disease progression and airway remodeling, and M2 macrophages are associated with type 2 asthma [57, 58]. Tiotiu et al. prove that the number of macrophages in patients with SA significantly reduces compared with mild-moderate asthma patients and healthy volunteers [59]. Takeshita et al. point out that the high expression of PLK1 is significantly related to M0 and M1 macrophages and low levels of M2 macrophages [60]. RAB6A was positively correlated with resting mast cells and negatively correlated with T cell follicular helper and dendritic cells. The resting mast cells and T cell follicular helper in the SA group were significantly lower than those in the non-SA group. RAB6A was the key gene in the blue module, and we speculated that RAB6A can improve the severity of SA via making mast cells quiescent or inhibiting TFH cell expression or reducing dendritic cell levels. However, basic experiments are needed to further confirm. Duvall et al. propose that SA patients show increased proinflammatory granulocytes and CD4+ T cells in BALF but decreased NK cells. Corticosteroids can destroy the activity of NK cells leading to NK cell protection deficit, which may be the important mechanism to regulate continuous airway inflammation and dysfunction of SA [61]. Palikhe et al. present that elevated levels of CD4(+) CRTh2(+) T cells are one of the characteristics of SA [62]. TFs can regulate gene expression and are closely related to the occurrence and development of diseases. Notably, SMAD4 was the key target and one of the TFs of ADCY4, indicating that approaches targeting SMAD4 are important in the development of an intervention for SA. PLK1 is a common therapeutic target and overexpressed in a variety of cancers, and targeting PLK1 may enhance the host's innate immune response [63]. However, the molecular mechanism of PLK1 in SA is unclear. The ToppGene Database makes it possible to screen potential drugs for SA, and we found several potential drugs that may inhibit PLK1. Lupeol, a triterpenoid, can be purified from many plant species and used in popular medicine [64]. Vasconcelos et al. [65] investigate the effect of lupeol in asthmatic mice sensitized by OVA and confirm that lupeol performs potent anti-inflammatory effect by reducing eosinophil numbers in BALF, Th2-associated cytokine levels (IL-4, IL-5, and IL-13), and mucus production with no sign of toxicity. The above research suggests that aiming at lupeol may help to develop new drugs for the treatment of SA. Thymoquinone (TQ, 2-methyl-5-isopropyl-1,4-benzoquinone), a monoterpene molecule present in Nigella sativa L., exerts anti-inflammatory and antioxidant effects by releasing cytokines and regulating PI3K/AKT pathway [66]. Surprisingly, it is suggested that TQ decreases the levels of Th2 cytokines in mice with allergic asthma [67]. In addition, TQ suppresses the expression of iNOS and TGF-β1 mRNAs to prevent inflammatory modification associated with asthma [68]. Naringin can promote the proliferation of AECs by regulating cell cycle progression and activating taste receptor intracellular signaling with no toxic effect to the airway epithelial structure and function [69]. In addition, naringin can reduce the airway resistance of OVA-induced asthmatic mice in a dose-dependent manner to effectively relax murine airway smooth muscle cells in vitro and in vivo [70]. All of the above researches suggest that naringin is a promising drug for the development of new bronchodilators for SA.
In summary, the results of our research are of great significance for understanding the mechanism underlying the infiltration of immune cells in patients with SA based on the transcriptome data of bronchial epithelial cells and multiomics or single-cell sequencing analysis. Second, the results of our research contribute to guiding the further verification in clinical samples, which will promote achievements in the treatment of SA and analyses of the precise mechanisms of key genes and immune cells in SA. However, relevant literature and basic experiments are relatively lacking at present. Studies aiming to further elucidate and verify the mechanism of the hub genes and immune cell infiltration patterns in patients with SA are still necessary.
This study initially revealed the key genes and immune cell infiltration patterns involved in SA progression. WGCNA showed that the blue and salmon modules were most closely related to SA. RNF126, PLK1, ADCY4, RAB6A, CPSF4, IFT57, CYFIP1, MRPL1, SMAD4, CAT, and KRT18 were crucial genes identified in these two modules. CIBERSORTx algorithms suggested that the differences in CD8+ T cells, T cell follicular helper, resting mast cells, and neutrophils between patients with SA and mild-moderate asthma were statistically significant and may be linked to the expression of the 11 key genes. A series of drugs acting on key targets may have strong potential as treatments for SA in the near future. However, more rigorous experiments and verification are needed to be performed to confirm our predicted results. |
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PMC9649330 | Siyuan Wang,Yiling Li,Ning Liu,Wei Shen,Wenhao Xu,Peng Yao | Identification of Glucose Metabolism-Related Genes in the Progression from Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma | 03-11-2022 | Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma. | Identification of Glucose Metabolism-Related Genes in the Progression from Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma
Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma.
Globally, nonalcoholic fatty liver disease (NAFLD) affects approximately 25% of the adult population, making it the most common chronic liver disease [1]. As part of NAFLD, there are several types of liver disease, such as simple steatosis and nonalcoholic steatohepatitis with varying levels of fibrosis and even cirrhosis [2]. Since obesity and metabolic syndrome are becoming more prevalent, NAFLD has become the leading cause of abnormal liver enzymes in the United States [3]. About 25% of the world's population may suffer from NAFLD, which affects 1 billion people worldwide [4]. A substantial difference exists in the prevalence of NAFLD in different parts of the world. NAFLD prevalence is highest in the Middle East and South America and lowest in Africa [5]. As many as 80 million people in the U.S. may have NAFLD. An individual with 5% hepatocyte infiltration with steatosis is considered to have NAFLD when they undergo imaging or liver biopsy testing [6]. The majority of people with NAFLD are asymptomatic, and they may remain silent until they develop cirrhosis [7]. Patients with NAFLD often suffer from fatigue and pain in the right upper quadrant when they are initially referred. Individuals with NAFLD may have an echogenic liver on ultrasound or evidence of liver fat on imaging studies [8]. Cardiovascular disease is the leading cause of death among NAFLD patients, followed by cancer and liver disease [9]. The adjusted hazard ratio for cardiovascular disease in nonobese people with NAFLD was approximately 10 times higher than in individuals without NAFLD in a Japanese cohort [10]. Glucose metabolism in the liver is critical to protein and lipid glycosylation. Diabetes and other chronic diseases may have metabolic changes due to alterations in glucose metabolism in the human liver. Understanding the glucose metabolism pathways in the healthy liver may help to shed light on these changes [11]. It is believed that NAFLD results from the imbalance in the hepatic energy metabolism, where excessive energy enters the liver relative to its ability to oxidize it into carbon dioxide or very low-density lipoprotein [12]. Therefore, energy is accumulated in the liver in the form of triglycerides, which may explain the common occurrence of NAFLD in obese and lipodystrophic patients [13]. Although excessive consumption of any food can lead to the development of NAFLD, monosaccharides and disaccharides, especially fructose, sucrose, and high fructose corn syrup, which are prevalent in processed foods, can further exacerbate NAFLD by activating de novo lipogenesis programs in the liver [14]. Moreover, fructose is almost entirely metabolized by the liver, and dietary fructose is converted into triglycerides by de novo lipogenesis [15]. NAFLD has become the leading cause of hepatocellular carcinoma and end-stage liver disease in the past decade. It is now well established that hepatocellular carcinoma can develop in NAFLD without cirrhosis, even though it has previously been considered the end stage of liver disease progression [16]. It is estimated that liver cancer cells consume an enormous amount of energy during proliferation and escape from apoptosis [17]. Glucose metabolism and fatty acid oxidation are altered to support proliferation and escape apoptosis [18]. It is also possible that altered glucose metabolism can result in elevated levels of saturated and monounsaturated fatty acids, which may prevent oxidative damage to cancer cells [19].
Genome-wide analysis of gene expression in NAFLD patients and healthy livers is downloaded from GSE89632 (https://www.ncbi.nlm.nih.gov/, GEO). In addition, glucose metabolism-related genes were downloaded from the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/). The gene expression data as well as the clinical information of hepatocellular carcinoma patients were downloaded from the Cancer Genome Atlas database (https://portal.gdc.cancer.gov/, TCGA). Single-cell RNA expression data from multiregional sampling in hepatocellular carcinoma were downloaded from GSE112271 in the GEO database.
Data on gene expression were obtained from the TCGA and GEO databases, and differential expression of mRNA was investigated using the Limma package in R. An adjusted P value of 0.05 in TCGA or GEO was defined as a threshold to distinguish between mRNAs, while |log2(fold change) | > 1 was defined as a threshold for mRNA differential expression screening. A gene annotation tool, the Gene Ontology (GO), is widely used to annotate genes with functions, particularly molecular functions (MFs), biological pathways (BPs), and cellular components (CCs). A KEGG enrichment analysis can be effective for analyzing gene function and related genomic functional information at a high level. An analysis of the KEGG pathway enrichment and GO function of underlying mRNAs was conducted using the ClusterProfiler package in R to better understand the oncogenic potential of target genes.
A consistency analysis was performed using the package ConsensusClusterPlus (v1.54.0), and heatmaps for gene expression were generated using genes with a variance greater than 0.1. R is used to implement all the above analysis methods.
An analysis of the correlation between immune infiltrating cells and tumor immunity was performed with the TIMER module (https://cistrome.shinyapps.io/timer/). Additionally, we used CellMarker to search for immune gene markers (https://biocc.hrbmu.edu.cn/CellMarker/). The correlations between gene expression levels and markers for immune genes can be visualized using expression plots.
Data and clinical information on hepatocellular carcinoma are downloaded from the TCGA dataset repository (https://portal.gdc.com). After extracting the data in TPM format from it and normalizing it to log2(TPM + 1), we retained samples with RNAseq data and clinical information. A KM survival analysis was conducted using the log rank to determine whether there was a statistically significant difference between the groups above in terms of survival. For the prediction model's accuracy, a timeROC analysis was performed. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for feature selection, and 10-fold cross-validation was used. The log-rank test and univariate Cox regression were used for calculating P-values and hazard ratios (HR) with 95% confidence intervals (CI) for Kaplan–Meier curves. Statistical significance was defined as a P < 0.05 for all of the above analysis methods and R packages, which were performed using R software version 4.2.1.
MSigDB was used to retrieve gene sets. GSEA was performed on the gene sets to identify enriched GO terms and KEGG pathways. The 50 best terms were selected from each subtype based on their significance.
In order to explore the immune scores, we used immunedeconv, which is an R package integrating six state-of-the-art algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and quanTIseq. Based on the TCGA dataset, we obtained clinical information about patients with hepatocellular carcinoma. SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 are genes related to immune checkpoints, and the expression of genes related to immune checkpoints was evaluated in R. In addition, the TIDE algorithm is used to predict possible immunotherapy responses.
The single-cell RNA-seq dataset was derived from the GEO database's supplementary file. In addition to filtering out poor-quality cells using the Seurat package, standard data preprocessing pipelines were used to generate the objects. Genes with fewer than three cells detected were filtered, as were genes with fewer than 200 genes detected. A minimum of 10,000 cells were used in the analysis, and cells with fewer than 200 or more than 2,500 genes detected, as well as cells with a high mitochondrial content, were filtered out. By adjusting the scale factor to 10,000, we normalized each cell. The ScaleData function from Seurat is used to normalize the data after it has been log-transformed. A normalized set of data measures was applied to standard analyses, as described in the Seurat R package. In UMAP, the first 30 principal components are used for visualization and clustering. A cell clustering procedure was performed using the FindClusters function (resolution = 0.2) in the Seurat R package.
A total of 24 normal liver tissues and 19 liver tissues of NAFLD patients were involved in the GSE89632 cohort (Figure 1(a)). The differential expression analysis between NAFLD patients and control groups was performed in R. The results demonstrated that 925 genes were upregulated and 1158 genes were downregulated in the NAFLD patients compared with normal people (Figures 1(b)-1(c)). The GO and KEGG enrichment analysis revealed that many pathways were closely correlated with NAFLD (Figure 1(d)). In addition, in order to explore the role of glucose metabolism in the NAFLD patients, we then obtained a total of 81 glucose metabolism-related genes were downloaded from the CTD database. The Venn diagram demonstrated that 9 key genes are involved in both the NAFLD and glucose metabolism pathways, including GCK, PPP1R3C, NHLRC1, ENO3, PPP2R5D, PFKFB3, PGM2, SLC25A12, and PFKP (Figure 1(e)).
Subsequently, based on the expression level of immune-related genes, the expression data of the NAFLD cohort were divided into high- and low-immune score groups (Figures 2(a)-2(b)). The results revealed that the immune cells were differentially expressed between the G1 and G2 groups. In addition, the G2 group shows a higher stromal score compared with the G1 group (Figure 2(c)). While the immune score and estimate score show no difference between the G1 and G2 groups (Figure 2(d)).
In order to explore the relationship between NAFLD and hepatocellular carcinoma and figure out the role of the glucose metabolism pathway in hepatocellular carcinoma induced by NAFLD, the patients involved in hepatocellular carcinoma were divided into C1 and C2 groups based on the expression level of 9 key genes. For concordance clustering, delta area curves indicate the change in the area under the cumulative distribution function (CDF) curve for each category number k compared to k − 1 (Figure 3(a)). The ConsensusClusterPlus consistent clustering heat map shows red for high expressions and blue for low expressions when k = 2 (Figures 3(b)-3(d)). There are significant differences between the overall survival rates of the C1 group and the C2 group according to the KM survival curves of different subgroup samples in the dataset. The results revealed that the glucose metabolism-related genes involved in NAFLD are closely associated with the prognosis of hepatocellular carcinoma patients (Figure 3(e)).
Subsequently, in order to further obtain the genes that are closely associated with the prognosis of hepatocellular carcinoma patients, we then performed the lasso regression analysis. The lasso regression analysis revealed that three glucose metabolism-related genes involved in NAFLD were applied to the prognosis prediction model (the risk score = (0.1177) × SERPINE1 + (0.0046) × VCAN + (0.0141) × TFPI2) (Figure 4(a)). Depending on the median risk score, patients were categorized as either low-risk or high-risk groups. In addition, the Kaplan–Meier curve showed that the prognostic model was closely related to the prognosis of hepatocellular carcinoma patients. Furthermore, the ROC curve results show that the AUCs are all greater than 0.6 at 1, 3, and 5 years, which indicates that the model is of good predictive value (Figure 4(b)). The expression level of B cells and CD4+ T cells is positively correlated with the risk score. In addition, the expression levels of endothelial cells, macrophages, and NK cells were negatively correlated with the risk score (Figure 4(c)). The clinical correlation analysis revealed that the risk score is closely related to the T stage, stage, and grade of hepatocellular carcinoma patients (Figure 4(d)). We then evaluated the expression of SERPINE1, VCAN, and TFPI2 in the hepatocellular carcinoma cohort of the TCGA cohort. The results demonstrated that VCAN was downregulated in the hepatocellular carcinoma samples compared with normal samples (Figure 5(c)). While SERPINE1 and TFPI2 were upregulated in hepatocellular carcinoma samples compared with normal samples (Figures 5(a)-5(b)). The KM survival curve revealed that VCAN is associated with the prognosis of hepatocellular carcinoma patients (P < 0.05) (Figures 5(d)–5(f)). The time-dependent ROC curve showed that the AUC value for TFPI2, SERPINE1, and VCAN was 0.866, 0.791, and 0.637, respectively (Figure 5(g)). Our next step was to examine differences in immune checkpoint expression between the groups. A significant difference was observed between high- and low-risk groups in the expression of CD274, CTLA4, HAVCR, LAG3, PDCD1, and TIGIT, which may be the potential targets for immunotherapy (Figure 6(a)). An assessment of tumor immune escape mechanisms was conducted using the TIDE score. According to the TIDE score results, the low-risk group received immune checkpoint blockade therapy with low efficacy, indicating that they received an immune checkpoint blockade therapy that was not effective (Figure 6(b)). According to the immune cell scores, high-risk and low-risk groups had significantly different scores for B cells, CD4+ T cells, neutrophils, macrophages, and myeloid dendritic cells (Figure 6(c)).
A total of 6 samples from patients with hepatocellular carcinoma were involved in this study. A description of quality control can be found in materials and methods. Following the removal of batch effects and the regressing of unique molecular identifier (UMI) numbers and mitochondrial UMI counts, 27,350 cells passed quality control (Figure 7). These cells are grouped into 13 major cell lineages, including CD8+ T cells, CD4+ T cells, M0 macrophages, endothelial cells, liver bud hepatocytes, M1 macrophages, myofibroblasts, B cells, monocytes, mesenchymal cells, Treg, mesenchymal stem cells, and exhausted CD8+ T cells (Figure 8). Figure 9 shows the distribution of cell proportions in different groups. Then, we evaluated the expression level of SERPINE1, VCAN, and TFPI2 in human hepatocellular carcinoma cells. The results demonstrated that SERPINE1 is rarely expressed in hepatocellular carcinoma cells. VCAN is specifically expressed in B cells of hepatocellular carcinoma. In addition, TFPI2 is specifically expressed in the monocytes of hepatocellular carcinoma.
Finally, in order to explore the function of 3 key genes (SERPINE1, VCAN, and TFPI2) in hepatocellular carcinoma patients, we then performed the GSVA enrichment analysis. The results revealed that SERPINE1 is mainly enriched in a structural constituent of ribosome, ribosomal subunit, sensory perception of smell, organic acid catabolic process, and oxidative phosphorylation (Figure 10(a)). For VCAN, the GSEA enrichment analysis demonstrated that VCAN is closely associated with external encapsulating structure organization, collagen-containing extracellular matrix, extracellular matrix structural constituent, plasma membrane signaling receptor complex, skeletal system development, T cell receptor complex, and immune response regulating signaling pathway (Figure 10(c)). In terms of TFPI2, the results of GSEA enrichment analysis revealed that many pathways are involved in TFPI2, including immunoglobulin complex, structural constituent of ribosome, external encapsulating structure organization, antigen binding, large ribosomal subunit, T cell receptor complex, complement activation, humoral immune response, and ribosomal subunit (Figure 10(b)).
Approximately 25% of the world's adult population suffers from NAFLD, which is the most common chronic liver disease [20]. The prevalence of NAFLD has been found to increase with age and may even lead to cirrhosis or hepatocellular carcinoma in some studies [21]. Individuals maintain health by maintaining glucose homeostasis in order to meet the energy requirements of vital organs [22]. In addition to glycogenesis, glycogenolysis, glycolysis, and gluconeogenesis, the liver plays a vital role in controlling glucose homeostasis [23]. However, few studies focused on the role of glucose metabolism in hepatocellular carcinoma induced by NAFLD. In this work, we first explore the genes that are closely related to NAFLD and glucose metabolism. The results revealed that a total of 9 genes were closely correlated with NAFLD and glucose metabolism, including GCK, PPP1R3C, NHLRC1, ENO3, PPP2R5D, PFKFB3, PGM2, SLC25A12, and PFKP. The underlying problem with NAFLD is insulin resistance, a key factor in metabolic syndrome, which is also linked to type 2 diabetes and hypertriglyceridemia [24]. Patients with obesity may be at risk for NAFLD due to abnormal lipid and glucose metabolism [25]. Currently, most basic research appears to focus on insulin resistance as well as the failure of the liver to process glucose loads from a pathophysiological perspective [26]. A former study has discovered that the JKW modulates insulin signaling and glucose metabolism to alleviate NAFLD [27]. In addition, this study identifies scientific evidence supporting the potential efficacy of JKW for the prevention and treatment of NAFLD [28]. In order to further explore the role of glucose metabolism in hepatocellular carcinoma induced by NAFLD, we then constructed a prognostic prediction model based on 9 key genes. We finally discovered that SERPINE1, VCAN, and TFPI2 play an important role in hepatocellular carcinoma. Recent studies have discovered that SERPINE1, VCAN, and TFPI2 are associated with many human tumors. The former study revealed that sh-TARBP2 cells with miR-145 overexpression were rescued from SERPINE1 inhibition and functional hepatoma cells were restored, which could be an important new intervention target in aggressive hepatocellular carcinoma. Many studies have found that VCAN may be a risk factor in gastric cancer, breast cancer, and colorectal cancer [29]. In addition, VCAN is a promising biomarker for the prognostic prediction of gastric cancer patients, breast cancer patients, and colorectal cancer patients [30]. Zhao et al.have discovered that TFPI2 inhibits breast cancer progression by inhibiting the TWIST-integrin pathways, presenting a new therapeutic target [31]. As a biomarker used in the colorectal cancer cohort, VCAN may assist in identifying patients at high risk for postoperative complications during stages II and III [32]. According to another study, TFPI2 gene methylation is an independent predictor of poor prognosis in nonsmall cell lung cancer patients [33]. In addition, our further research has revealed that 3 key genes are associated with immune checkpoint blockade therapy and immunotherapy of hepatocellular carcinoma, which may suggest that immunotherapy could be an effective way to treat hepatocellular carcinoma induced by NAFLD [34]. Previous studies have focused on the screening of differentially expressed biomarkers between tumor and nontumor tissues. It is possible to lose important genes when analyzing bulk transcriptome data from cell populations. Single cell-RNA sequencing analysis is therefore more useful in elucidating the underlying mechanisms of NAFLD and hepatocellular carcinoma. In this work, in order to explore the expression level of key genes in the different cells of hepatocellular carcinoma, we then performed single cell-RNA sequencing of hepatocellular carcinoma samples. The results demonstrated that VCAN is specifically expressed in B cells and is specifically expressed in monocytes. Liu et al. discovered that hepatocellular carcinoma is more responsive to immunotherapy by targeting monocyte-intrinsic enhancer reprogramming. Furthermore, an assessment of the lymphocyte-to-monocyte ratio predicts prognosis in hepatocellular carcinoma patients undergoing radiofrequency ablation and transcatheter arterial chemoembolization. Additionally, we also evaluated the potential function of SERPINE1, VCAN, and TFPI2 in a hepatocellular carcinoma cohort. The results revealed that the humoral immune response is closely associated with TFPI2. According to growing evidence, the peripheral immune response to hepatocellular carcinoma affects how the disease develops, how it responds to therapy, and how long patients live. Furthermore, an immune-suppressive response was also found among patients with NAFLD-hepatocellular carcinoma, as determined by functional and metabolomic evidence [35]. An additional study demonstrated that AKR1B10 and SPP1 were closely related to NAFLD and NAFLD-hepatocellular carcinoma immune cell infiltration and immunosuppressive cytokine expression [36]. SERPINE1 is closely associated with immune checkpoint molecule expression in the GC cohort as a hypoxia-related gene [37]. In addition, there is a good correlation between VCAN and immune checkpoint blockade response [38]. In recent years, many studies have focused on the role of bioinformatics analysis methods in human health [39]. The bioinformatics analysis could lead to higher-quality research and provide new directions for researchers. However, there are also some limitations to bioinformatics analysis. First, without experimental verification, the results need to be verified by experiments [40]. In addition, high heterogeneity often leads to large bioinformatics analysis errors, so unifying the methods is essential to reducing errors [41]. Therefore, corresponding experimental validations are needed to be performed to further confirm the accuracy of our results. Taken together, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma. |
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PMC9649345 | Wei Liu,Wanci Song,Yang Luo,Hanxiong Dan,Li Li,Zhouyang Zhang,Daonian Zhou,Pengtao You | Angelica Yinzi alleviates 1-chloro-2,4-dinitrobenzene-induced atopic dermatitis by inhibiting activation of NLRP3 inflammasome and down-regulating the MAPKs/NF-kB signaling pathway | 19-07-2022 | Angelica Yinzi,Atopic dermatitis,MAPK,NLRP3,Inflammation | Background Atopic dermatitis (AD), characterized by eczema as a chronic pruritic inflammatory skin disease, has become a serious health problem with recurrent clinical episodes. However, current clinical treatments have limited relief and are accompanied by adverse effects. Therefore, there is a necessity to develop new effective drugs for AD treatment. Angelica Yinzi (AYZ) is a classic ancient prescription for nourishing blood, moistening dryness, dispelling wind, and relieving itching. However, its mechanism for alleviating atopic dermatitis remains unknown. Therefore, this study aimed at determining the effects of AYZ and its potential mechanism in alleviating AD-like symptoms. Methods In the present study, we used 1-chloro-2,4-dinitrobenzene (DNCB) to establish a mouse model of atopic dermatitis, where DNCB readily penetrates the epidermis to cause inflammation. Histopathological analysis was performed to examine the thickening of dorsal skin and infiltration in the inflammatory and mast cells in C57BL/6 mice. Additionally, the immunoglobulin E (IgE) levels in serum were determined by enzyme-linked immunosorbent assay (ELISA) kits. The IL-1β and TNF-α expression were detected using qRT-PCR. Next, the Western blotting and immunohistochemistry assays were performed to assess the contribution of MAPKs/NF-κB signaling pathways and the NLRP3 inflammasome in AD responses. Results Histopathological examination revealed that AYZ reduced the epidermal thickness of AD-like lesioned skin and repressed the infiltration of mast cells into AD-like lesioned skin. AYZ significantly decreased the phosphorylation of p38 MAPK, JNK, ERK and NF-κB and downregulated serum IgE levels and IL-1β and TNF-α mRNA levels. Additionally, the NLRP3, ASC, Caspase-1, and IL-1β expression in dorsal skin were effectively down-regulated following AYZ treatment (p < 0.05 and p < 0.01). Conclusion These findings revealed that AYZ effectively suppressed AD-induced skin inflammation by inhibiting the activation of the NLRP3 inflammasome and the MAPKs/NF-kB signaling. Therefore, AYZ is a potential therapeutic agent against AD in the clinical setting. | Angelica Yinzi alleviates 1-chloro-2,4-dinitrobenzene-induced atopic dermatitis by inhibiting activation of NLRP3 inflammasome and down-regulating the MAPKs/NF-kB signaling pathway
Atopic dermatitis (AD), characterized by eczema as a chronic pruritic inflammatory skin disease, has become a serious health problem with recurrent clinical episodes. However, current clinical treatments have limited relief and are accompanied by adverse effects. Therefore, there is a necessity to develop new effective drugs for AD treatment. Angelica Yinzi (AYZ) is a classic ancient prescription for nourishing blood, moistening dryness, dispelling wind, and relieving itching. However, its mechanism for alleviating atopic dermatitis remains unknown. Therefore, this study aimed at determining the effects of AYZ and its potential mechanism in alleviating AD-like symptoms.
In the present study, we used 1-chloro-2,4-dinitrobenzene (DNCB) to establish a mouse model of atopic dermatitis, where DNCB readily penetrates the epidermis to cause inflammation. Histopathological analysis was performed to examine the thickening of dorsal skin and infiltration in the inflammatory and mast cells in C57BL/6 mice. Additionally, the immunoglobulin E (IgE) levels in serum were determined by enzyme-linked immunosorbent assay (ELISA) kits. The IL-1β and TNF-α expression were detected using qRT-PCR. Next, the Western blotting and immunohistochemistry assays were performed to assess the contribution of MAPKs/NF-κB signaling pathways and the NLRP3 inflammasome in AD responses.
Histopathological examination revealed that AYZ reduced the epidermal thickness of AD-like lesioned skin and repressed the infiltration of mast cells into AD-like lesioned skin. AYZ significantly decreased the phosphorylation of p38 MAPK, JNK, ERK and NF-κB and downregulated serum IgE levels and IL-1β and TNF-α mRNA levels. Additionally, the NLRP3, ASC, Caspase-1, and IL-1β expression in dorsal skin were effectively down-regulated following AYZ treatment (p < 0.05 and p < 0.01).
These findings revealed that AYZ effectively suppressed AD-induced skin inflammation by inhibiting the activation of the NLRP3 inflammasome and the MAPKs/NF-kB signaling. Therefore, AYZ is a potential therapeutic agent against AD in the clinical setting.
Atopic dermatitis (AD) is a chronic, pruritic, recurring inflammatory disease of the skin, affecting 20% of children and 10% of adults in many high-income countries (Czarnowicki et al., 2019, Langan et al., 2020). Its primary symptoms include lichenification on dry skin and eczematous lesions linked to mental health problems, such as sleep disorders and fatigue. Damaged skin is characterized by elevated serum IgE levels and infiltration of inflammatory cells (lymphocytes, macrophages, eosinophils and mast cells) (Barton and Sidbury, 2015). For instance, the activation of mast cell infiltration contributes to AD and is easily noticed in skin tissue with AD (Gonzalez-de-Olano and Alvarez-Twose, 2018). In addition, the MAPKs phosphorylation induces the production of inflammatory mediators and allergic inflammatory responses (Huang et al., 2019, Park et al., 2019). For example, the NLRP3 inflammasome activated by the activation of the NF-κB pathway regulates contact allergy (Meng et al., 2009). Furthermore, the immune system produces high levels of proinflammatory mediators, including cytokines (IL-1β, TNF-α), which play an important role in host cell defense (Liu and Ding, 2019). Although AD is associated with immune system disorders, genetics, skin barrier disruption, and environmental factors, its diverse pathogenesis has not been clearly elucidated (Torres et al., 2019, Yan, et al., 2019). Currently, AD is treated with topical corticosteroids, topical calcineurin inhibitors, and systemic immunotherapies (Newsom et al., 2020). However, these treatments have a rebound phenomenon, adverse side effects, and intermittent recurrencies (Hou et al., 2017). For example, the local cutaneous atrophy, striae, and stinging are side effects of the long-term use of topical calcineurin inhibitors (Lee et al., 2020, Paller et al., 2016). Therefore, safer and more effective AD treatments against AD, such as traditional Chinese medicine (TCM), have recently attracted increased and widespread interest (Eyerich and Novak, 2013). The TCM has unique advantages in AD routine management and treatment. The efficacy of its classic herbal formulation AYZ is scientifically and clinically proven and has been widely used in the treatment of chronic urticaria, hypersensitivity, pruritus and AD without any severe adverse events (Qin et al., 2020). The AYZ formula comprises of 11 different herbs including, Angelica sinensis (Oliv.) Diels, Paeonia lactiflora Pall., Ligusticum chuanxiong Hort., Rehmannia glutinosa Libosch., Tribulus terrestris L., Saposhnikovia divaricate (Trucz.) Schischk., Schizonepeta tenuisfolia Briq., Polygonum multiflorum Thunb., Astragalus membranaceus (Fisch.) Bge., Glycyrrhiza uralensis Fisch. and Zingiber officinale Rosc. Recent pharmaceutical studies have demonstrated that A. sinensis (Oliv.) Diels and L. chuanxiong Hort. have anti-inflammatory effects induced via the NF-κB and MAPKs signaling pathways (Gu et al., 2018, Lee et al., 2010). In addition, P. lactiflora Pall. and S. tenuisfolia Briq. have been used to reverse the effects of AD (Choi et al., 2013, Jo et al., 2018). A study has shown through a network pharmacology approach that AYZ may intervene in AD by acting on MAPKs/NF-kB signaling pathway(Wang, 2021). Therefore, based on the above studies, this study focused on establishing the underlying mechanism of AYZ in ameliorating DNCB-induced AD in mice.
AYZ was obtained from the Mayinglong Pharmaceutical Co., Ltd (China). ShiDuQingPian (SDQP) was purchased from Guangxi Yulin Pharmaceutical Group Co., Ltd (China). Cetirizine hydrochloride tablet (CHT) was purchased from Dong rui Pharmaceutical Co., Ltd (China). 1-chloro-2,4-dinitrobenzene (DNCB) and olive oil were purchased from Shanghai McLean Biochemical Technology Co., LTD.
Male C57BL/6 mice (6–8 weeks old) weighing 18–20 g were purchased from Hubei Provincial Center for Disease Control and Prevention (SCXK2017-0012). The mice were housed in individually-ventilated cages maintained at 22 ± 2 °C, the humidity of 50 ± 10%, and 12:12-h light: dark cycle and were provided with adequate food and water. Atopic dermatitis-like immunological and skin lesions were induced on the dorsal skin, face, and back of both ears on each mouse using DNCB. Specifically, approximately 3 cm2 patch was shaved on each mouse back using an electric clipper a day before DNCB treatment. The 75 mice were randomly assigned into five groups (n = 15 per group), namely the control, DNCB, DNCB plus oral AYZ, DNCB plus oral SDQP, DNCB plus oral CHT groups based on the treatments given. The experimental design is summarised in Fig. 1. For AD sensitization, 200 μl of 1% DNCB solution (dissolved in acetone and olive oil in the ratio 3:1) was applied repeatedly on the face and backs of both ears twice on days −4 and 0. To induce AD-like lesions, 200 μl of 0.5% DNCB solution was applied on the dorsal skin thrice weekly for three weeks (days 1–21). Upon sensitization, 20 ml/kg or 6.24 g/kg of AYZ was orally administered daily to the AYZ treated group for three weeks. The SDQP treated group (20 ml/kg, daily oral 0.96 g/kg) and CHT treated group (20 ml/kg, daily oral 1.3 mg/kg) served as the positive controls. The DNCB group was orally administered with the same volume of pure water. All operations were orderly carried out in accordance with the serial number of mice. The mice were sacrificed and samples were collected. Mice in the control group were treated with vehicles.
Dermatitis was scored weekly following the previously described criteria with slight modifications (Kim et al., 2014). Erythema/bleeding (I), edema (II), scratch/erosion (III) and scale/dryness (IV), were scored as 0 (none), 1 (mild), 2 (moderate) or 3 (severe). The total score of dermatitis ranged from 0 to 12. To minimize technical differences, a single investigator performed all the measurements across the experiment.
The mice's skin tissues were fixed in 10% neutral buffered formalin (NBF) for 24 h then embedded in paraffin and sectioned into blocks with a 5 μm thickness. To evaluate the tissue architecture and the degree of mast cell infiltration, the tissue sections were stained with a hematoxylin and eosin (H&E) solution and toluidine blue (TB) (Servicebio). The Stained sections were visualized and images captured under a light microscope (Olympus, Japan).
The paraffinized skin tissue was dewaxed, dehydrated then antigen retrieval was performed. Next, the non-specific binding was blocked by placing the tissues in 0.3% hydrogen peroxide for 15 min then in 5% bovine serum albumin (BSA) for 30 min. The expression of NLRP3, ASC, caspase-1, or p-NF-κB proteins were primarily labeled with NLRP3 antibody (1: 100 dilution; Abways, China) (CY5651), ASC antibody (1: 100 dilution; Abways, China) (AY3812), Caspase-1 antibody (1: 200 dilution; Bioss, China) (bs-0169R) or p-NF-κB antibody (1: 200 dilution; CST, USA) respectively overnight at 4 °C. The tissues were then incubated with the secondary horseradish peroxidase (HRP)-conjugated anti-rabbit IgG antibody (1: 500 dilution; DAKO, K5007) for 60 mins to localize the primary antibody binding. The immunohistochemistry assessment was performed using the DAB (3, 3′-diaminobenzidine) staining kit (Boster, Wuhan, China). The stained tissues were observed, and images were captured under an Olympus microscope.
The mice blood samples were centrifuged at 2000 g at 4 °C for 20 min. The obtained serum was stored at −80 °C awaiting further analysis. The mouse IgE ELISA kits were purchased from Shanghai Fusheng Industrial Co., LTD. (A105159). The total serum levels of IgE were measured using mouse IgE ELISA kits according to the manufacturer’s instructions.
The dorsal skin proteins were analyzed by western blotting. First, the skin tissues were homogenized using a radioimmunoprecipitation assay buffer supplemented with protease inhibitors. Next, the protein concentration in each sample was detected using an enhanced BCA protein assay kit. The proteins were then denatured in sodium dodecyl sulphate (SDS) buffer, separated on a 10% SDS-polyacrylamide gel electrophoresis, and transferred onto the polyvinylidene difluoride (PVDF) membranes. The membranes were then blocked with 5% BSA at room temperature for 2 h, and incubated overnight at 4 °C. The membranes were then incubated with several primary antibodies, including GAPDH (1:1000 dilution, Cell Signaling Technology, #5174), P-ERK (1:1000 dilution, Cell Signaling Technology, #4695), P-JNK (1:1000 dilution, Cell Signaling Technology, #4668), P-P38 (1:1000 dilution, Cell Signaling Technology, #9910), P38 (1:1000 dilution, Cell Signaling Technology, #8690), NF-κB (1:1000 dilution, Cell Signaling Technology, #8242), and P-NF-κB (1:1000 dilution, Cell Signaling Technology, #3033) overnight at 4 °C. The membranes were rinsed in three changes of tris buffered saline + Tween (TBST) then incubated with the anti-rabbit secondary antibody (1:2000 dilution, Cell Signaling Technology, #4412) diluted in 5% non-fat milk at room temperature for 1 h. The membranes were then washed in three changes of TBST for 10 min. Proteins were visualized using ECL (Thermo), and membranes were scanned and imaged by the FluorChem FC3 system (ProteinSimple, USA).
The total RNA was extracted from dorsal skin samples using the Trizol reagent (Thermo Fisher Scientific, USA). The spectrophotometric values of A260/280 ranged from 2.0 ∼ 2.4 and the values of A260/230 were 2.1–2.2, indicating that the isolated RNA was free of polyphenols, polysaccharides and protein contaminants. Next, 1 μg of total RNA was reverse transcribed into cDNA using HiScript® III-RT SuperMix (Vazyme biotechnology, Nanjing, China). The primers were designed using the Primer Premier 5.0 design software (Premier, Canada) and synthesized by Sangon Biotech (Shanghai, China). The mRNA expression levels of target genes were then determined using ChamQ Universal SYBR qPCR Master Mix (Vazyme) with the thermal cycling conditions: pre-denaturation at 95 °C for 30 s, denaturation at 95 °C for 10 s, denaturation at 60 °C for 30 s, and then melting curves were generated at 95 °C for 15 s, 60 °C for 60 s, and 95 °C for 15 s. The mRNA expression with GAPDH as an internal control was normalized. The relative quantification was performed using the 2-ΔΔCT method. The primers for IL-1β are as follows, Forward primer: 5′-CATCCAGCTTCAAATCTCGCAG-3′; reverse primer, 5′-CACACACCAGCAGGTTATCATC-3′. The primers for TNF-α are as follows, Forward primer: 5′-CATCTTCTCAAAATTCGAGTGACAA-3′; reverse primer, 5′-CATCTTCTCAAAATTCGAGTGACAA-3′. The primers for GAPDH are as follows, Forward primer: 5′-CATGGCCTTCCGTGTTCCTA-3′; reverse primer, 5′-CCTGCTTCACCACCTTCTTGAT-3′.
The Chinese herbal formula AYZ was prepared from a TCM concoction consisting of 11 Chinese medicinal plants extracts. The chemical compounds present in AYZ were then determined using an UHPLCQ-TOF-MS consisting of a quaternionic pump (LC-20 AT), an array detector (DAD), electrospray ion source (ESI) At an ultimate UHPLC XB C18 spectrum of 2.1 × 100 mm, 1.8 μm, and wavelengths set at 230 nm. The column thermostat was maintained at 35 °C and the mobile phase comprised of 0.05% Formic acid acetonitrile mixture (A) and 0.05% formic acid (B). The elution gradient was as follows: 0–3.5 min, 5–15% A; 3.5–6.5 min, 15–26% A; 6.5–7.5 min, 26–27% A; 7.5–10 min, 27–40% A; 10–14.5 min, 40–90% A, and 14.5–17 min, 90–5% A. An injection volume of 1 μl, with a flow rate of 0.4 ml/min was used. The LC-MS data were collected by Agilent Mass Hunter (B.08.00) software and processed using the Agilent software, Qualitative Navigator (B.08.00), and Qualitative Workflows (B.08.00).
All quantitative data derived from this study were analyzed statistically. The results are expressed as the mean ± standard deviation (SD) of at least three separate tests. All data analyses were performed using the two-tail, equal variance Independent-Samples t-test and one-way ANOVA in the SPSS 22.0 software (IBM, USA). p < 0.05 were considered statistically significant.
The DNCB group exhibited severe erythema, hemorrhage, erosion, and dryness compared to the control. However, after treatment with AYZ 3 weeks, the DNCB-induced AD severity was significantly decreased in the AYZ group (Fig. 1A). The dermatitis score was also significantly higher in the DNCB group than in the control group (p < 0.01; Fig. 1B). Similarly, AYZ treatment significantly decreased the dermatitis score (p < 0.01). The serum IgE levels were also increased in the DNCB group compared to the control group but were significantly reduced by AYZ treatment, suggesting that it suppresses the IgE synthesis associated with AD (p < 0.01; Fig. 1C). The repeated DNCB exposure also induced potent inflammatory changes, including the skin dermis and epidermis thickening in the DNCB group compared to the control group (Fig. 1D). Therefore, there were few mast cells in the AYZ group and positive groups 21 days after treatment. These findings imply that AYZ has an alleviatory effect against AD clinical symptoms and prevents severe AD pathological states.
The p-p38, p-ERK, p-JNK, p-p65 expressions were increased in the DNCB group relative to the control group (p < 0.01; Fig. 2A-B). However, AYZ alleviated the DNCB-induced increase in p-p38, p-p65, p-ERK, and p-JNK expressions relative to the DNCB group (p < 0.05 and p < 0.01). In addition, there were no significant differences in p65 expression between the experimental groups. The immunohistochemical staining to determine whether p-p65 was involved in DNCB-induced AD-like skin lesions revealed that the p-p65 expression was significantly increased in the DNCB group compared to the control (Fig. 2C). However, the AYZ treatment significantly decreased the p-p65 expression. Thus, the immunohistochemical analysis of p-p65 expression was consistent with the findings in the Western blot analysis. Besides, a significant increase in IL-1β, TNF-α was observed in the DNCB group compared to the control group (p < 0.01; Fig. 2D-E). In contrast, the TNF-α, IL-1β expressions were significantly decreased in the AYZ group compared to the DNCB group (p < 0.01).
The IHC analysis revealed that the stains in mice skin stained with NLRP3, ASC, Caspase-1 antibodies were more intense in the DNCB group compared to the control group (Fig. 3A). However, NLRP3, ASC, and Caspase-1 contents in the skin were significantly decreased following AYZ treatment. In addition, the IL-1β expression in the DNCB group was significantly up-regulated compared to the control group (p < 0.01), but the AYZ treatment significantly alleviated this increase (p < 0.05; Fig. 3B and C).
The material standard of AYZ is 11 medicinal flavors. A comparative analysis of the material standard and liquid quality for each component and in a drug formulation revealed the source of the drug flavor by characterizing the main chromatographic peaks. In addition, the UPLC-UV and UPLC-TOF-MS total ion flow chromatography of Angelica reference solution (positive and negative modes) was performed, identifying chromatographic peaks of 1–38, which have been separated and tested well (Fig. 4, Table 1).(See Fig. 5.).
Atopic dermatitis is a complicated chronic inflammatory disease caused by the interaction of genetic factors through the stimulation, triggering IgE-mediated forms of skin inflammation and allergic reaction (Xiong et al., 2021). In the present study, AYZ effectively alleviated DNCB-induced AD-Like symptoms and reduced AD-induced inflammation by suppressing the activation of the NLRP3 inflammasome and the MAPKs/NF-kB signaling pathway. Traditional herbal extracts are widely used in Asia as folk remedies for inflammatory diseases, including AYZ (Choi et al., 2011). Therefore, this study identified and characterized the compounds present in AYZ UPLC-TOF-MS. Angelica Yinzi contains 38 chemical components, including citric acid, gallic acid, ferulic acid, and cimifuginin (Table 1). Citric acid alleviates DNCB-induced AD in animals (Inoue et al., 2010), while gallic acid contributes to the in vivo anti-inflammatory activities of PHF against AD, where it exert its anti-atopic and anti-inflammatory activity on the skin and in the immune system (Tsang et al., 2016). Ferulic acid also alleviated AD-like symptoms in mice through its potent anti-inflammatory effect (Zhou et al., 2020). Besides, cimifugin suppresses allergic inflammation by reducing the epithelial-derived initiative key factors by regulating the tight junctions (Wang et al., 2017). Therefore, AYZ possibly alleviated DNCB-induced AD because it contains these compounds. Repeated allergic inflammatory reactions cause skin surface remodeling and hardening, epidermal thickening, and rupture, which are histological characteristics of AD, due to the mast cells and macrophages infiltration into the skin tissue (Fujii et al., 2009, Modena et al., 2016). The histamine released by activated mast cells induces skin itching, dryness, scab, and bleeding, which are AD indicators Xiong et al., 2021). In the present study, the dermatitis score, thickening with scabbing, hemorrhage, and edema in the dorsal skin were significantly reduced in the AYZ group. Besides, H&E and TB staining revealed that the mice skin thickening and inflammatory cells infiltration were significantly relieved in the AYZ group. At the same time, AYZ markedly down-regulated the IgE levels in the serum. The IgE hypersecretion is the primary AD etiology (Park et al., 2021). IgE binds to high-affinity for IgE-Fc receptor type I on the surface of mast cells, releasing various types of inflammatory mediators (Dupuy, 1994, Werfel et al., 2016). Overall, AYZ alleviated AD symptoms by repressing IgE accumulation and infiltration of mast cells in the skin. Moreover, MAPKs and NF-κB signaling pathways are closely associated with AD(Sur et al., 2019). The MAPK signaling modules are divided into three groups, including ERK, JNK, and p38 (Duan and Wong, 2006; Huang et al., 2019), which increase intracellular pro-inflammatory cytokines and responses through inflammatory responses in various immune cells. These signaling modules were significantly downregulated by AYZ in this study. Besides, NF-κB is an essential downstream target of MAPK signaling, which regulates many inflammatory cytokines, including TNF-α and IL-1β (Fann et al., 2018, Zhao et al., 2019). TNF-α and IL-1β drive the inflammatory cascade, activating innate immunity and subsequent inflammatory responses (Koga et al., 2008) and repressing the TNF-α and IL-1β expressions positively influence AD (Chen et al., 2020). Inflammation, as an important response to infection in the organism, is of great importance for the development of disease. It has been shown that inhibition of p38 (Nadeem et al., 2017), JNK (Chamcheu et al., 2019), ERK (Chen et al., 2021) and NF-κB (Nadeem,Ahmad, 2017) signaling pathways can alleviate other chronic inflammatory skin diseases including psoriasis(Nadeem et al., 2015). This is consistent with the results of the present study, where AYZ blocked ERK, JNK, p38 phosphorylation and NF-κB signaling pathways, while inhibiting TNF-α and IL-1β expression in the DNCB group. The NF-κB signaling pathway is a critical pathway triggering the NLRP3 transcription (Li et al., 2021). It is commonly phosphorylated at the Ser536 position, then translocated to the nucleus up-regulating the NLRP3 and IL-1β mRNA expressions (Wu et al., 2017). The NLRP3 interacts with the ASC via a thermal protein domain leading to the cleaving of pro-caspase-1 into mature caspase-1, which, when activated, converts IL-1β into mature IL-1β with proinflammatory functions, subsequently leading to cell death (Zhao et al., 2019). Besides, acne induces inflammation by activating the NLRP3 inflammasome through the MAPK/NF-κB signaling pathway (Fang et al., 2020). Thus, the decreased activated NLRP3, Caspase-1and ASC induced by DNCB implies that the AYZ treatment markedly repressed the activation of the NF-κB pathway and NLRP3 inflammasome. Nevertheless, there were some limitations in this study. At present, only 38 compounds have been identified. However, it is impossible to determine which compounds exert an effect on AD.
We found that AYZ decreased serum IgE levels, reduced the epidermal thickness of AD-like lesioned skin, and inhibited the infiltration of mast cells into AD-like lesioned skin. AYZ also reduced the expression of pro-inflammatory factors TNF-α and IL-1β by inhibiting MAPKs/NF-kB signaling pathway. In addition, we demonstrated that NLRP3 inflammasome activation was significantly downregulated after AYZ treatment. In conclusion, AYZ ameliorated the symptoms of AD. AYZ inhibited the proliferation of mast cells, suppressed the activation of NLRP3 inflammasome and MAPKs/NF-kB signaling pathway, reduced the infiltration of inflammatory cells, and ameliorated DNCB-induced AD-like skin inflammation in mice. These findings provide a theoretical basis for the potential application of AYZ in AD treatment.
This work was supported by the Hubei Provincial Natural Science Foundation of China (No.2018CFB657).
Wei Liu: Conceptualization, Methodology, Software. Wanci Song: Methodology, Data curation, Writing – original draft. Yang Luo: Visualization, Investigation. Hanxiong Dan: Supervision. Li Li: Software, Validation. Zhouyang Zhang: Software, Validation. Daonian Zhou: Supervision. Pengtao You: Writing – review & editing.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649349 | Faizul Islam Chowdhury,Tahmina Yasmin,Raushanara Akter,Md Nurul Islam,Mohammed Maksud Hossain,Ferdous Khan,Adil Aldhahrani,Mohamed Mohamed Soliman,Nusrat Subhan,Md. Areeful Haque,Md Ashraful Alam | Resveratrol treatment modulates several antioxidant and anti-inflammatory genes expression and ameliorated oxidative stress mediated fibrosis in the kidneys of high-fat diet-fed rats | 25-07-2022 | Resveratrol,High fat diet,Oxidative stress,Inflammation,Fibrosis | Objective Resveratrol is a polyphenolic compound that possesses strong antioxidant and anti-inflammatory activities. This study evaluated the effects of resveratrol on oxidative stress, fibrosis and multiple genes regulation in the kidneys of high fat (HF) diet-fed rats. Methods Wistar rats were fed with HF diet for eight weeks. These rats were also treated with resveratrol for eight weeks. Finally, kidney tissue samples were isolated from all sacrificed rats. The histological changes, creatinine and uric acid levels, oxidative stress parameters such as malondialdehyde (MDA), nitric oxide, and advanced oxidation protein product (AOPP) levels were analyzed. The antioxidant enzymes such as catalase, superoxide dismutase (SOD) activities and reduced glutathione (GSH) levels; gene expression of inflammatory and fibrosis-related genes namely, inducible nitric oxide synthase (iNOS), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), transforming growth factor beta1 (TGF-β1), and collagen-1 were assessed. Moreover, gene expression of oxidative stress-related genes such as nuclear factor erythroid 2–related factor 2 (Nrf-2), SOD, catalase, and glutathione reductase, were also assessed. Results HF diet-fed rats showed increased creatinine and uric acid levels in plasma which were lowered by resveratrol treatment. The study findings also revealed that resveratrol counterbalanced the oxidative stress and prevented the expression of the inflammatory genes; restored the catalase and SOD activities followed by the up-regulation of antioxidant genes expression in the kidneys of HF diet-fed rats. HF diet caused the Nrf-2 down-regulation followed by the decreased expression of HO-1 and HO-2 genes, which was restored by resveratrol treatment. Moreover, the histological assessment showed lipotoxicity and increased fibrosis in the kidneys of HF diet-fed rats. Resveratrol prevented the kidney fibrosis probably by limiting oxidative stress, inflammation, and down-regulating TGF-β1 mediated signaling pathway. Conclusion In conclusion, resveratrol treatment showed beneficial effects in preventing oxidative stress and fibrosis in the kidneys of HF diet-fed rats probably by modulating the gene expression of oxidative stress and inflammation related factors and enzymes. | Resveratrol treatment modulates several antioxidant and anti-inflammatory genes expression and ameliorated oxidative stress mediated fibrosis in the kidneys of high-fat diet-fed rats
Resveratrol is a polyphenolic compound that possesses strong antioxidant and anti-inflammatory activities. This study evaluated the effects of resveratrol on oxidative stress, fibrosis and multiple genes regulation in the kidneys of high fat (HF) diet-fed rats.
Wistar rats were fed with HF diet for eight weeks. These rats were also treated with resveratrol for eight weeks. Finally, kidney tissue samples were isolated from all sacrificed rats. The histological changes, creatinine and uric acid levels, oxidative stress parameters such as malondialdehyde (MDA), nitric oxide, and advanced oxidation protein product (AOPP) levels were analyzed. The antioxidant enzymes such as catalase, superoxide dismutase (SOD) activities and reduced glutathione (GSH) levels; gene expression of inflammatory and fibrosis-related genes namely, inducible nitric oxide synthase (iNOS), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), transforming growth factor beta1 (TGF-β1), and collagen-1 were assessed. Moreover, gene expression of oxidative stress-related genes such as nuclear factor erythroid 2–related factor 2 (Nrf-2), SOD, catalase, and glutathione reductase, were also assessed.
HF diet-fed rats showed increased creatinine and uric acid levels in plasma which were lowered by resveratrol treatment. The study findings also revealed that resveratrol counterbalanced the oxidative stress and prevented the expression of the inflammatory genes; restored the catalase and SOD activities followed by the up-regulation of antioxidant genes expression in the kidneys of HF diet-fed rats. HF diet caused the Nrf-2 down-regulation followed by the decreased expression of HO-1 and HO-2 genes, which was restored by resveratrol treatment. Moreover, the histological assessment showed lipotoxicity and increased fibrosis in the kidneys of HF diet-fed rats. Resveratrol prevented the kidney fibrosis probably by limiting oxidative stress, inflammation, and down-regulating TGF-β1 mediated signaling pathway.
In conclusion, resveratrol treatment showed beneficial effects in preventing oxidative stress and fibrosis in the kidneys of HF diet-fed rats probably by modulating the gene expression of oxidative stress and inflammation related factors and enzymes.
Oxidative stress is the abnormality of tissues that failed to manage the free radical-mediated damage to the cells due to a lack of antioxidant defense (Kurutas 2016). Several mechanisms such as mitochondrial electron transport chain, xanthine oxidase system, nicotinamide adenine dinucleotide oxidase system, myeloperoxidase system etc. are involved in reactive oxygen species (ROS) generation in tissues (Di Meo et al., 2016, He et al., 2017). Scavenging enzymes such as superoxide dismutase (SOD), catalase, and glutathione peroxidase activities were reported to be diminished in oxidative stress (He et al., 2017). The SOD can catalyze superoxide into hydrogen peroxide, which is further catalyzed by catalase to water (Di Meo et al., 2016, He et al., 2017). The development of obesity and diabetes are now related to the oxidative stress and, may further deteriorate due to the excessive free radicals mediated tissue damage (Burgos-Morón et al., 2019). Earlier investigations also suggested that obese individual faces chronic kidney dysfunction due to ROS production (Hamza and Dyck, 2014, Ratliff et al., 2016). Our previous investigation showed that the HF diet may also develop kidney dysfunction and fibrosis in rats (Panchal et al., 2011). One of the early signs of tissue damage in kidneys is the increment of the lipid peroxidation product such as malondialdehyde (MDA) and the development of fibrosis (Gyurászová et al., 2020, Mamun et al., 2020). ROS is able to stimulate the collagen secretion from fibroblasts by regulating several growth factors including transforming growth factor-beta (TGF-β) (Isaka 2018). In agreement with this finding, a previous review work showed that the TGF-β expression is increased in the glomerulus and tubular spaces due to increased oxidative stress, leading to fibrosis (Sureshbabu et al., 2016). Transgenic mice having over-expressed TGF-β lead to the development of glomerular immune deposits, mesangial expansion, and matrix proteins deposition, and interstitial fibrosis (Kopp et al., 1996). Therefore, inhibition of ROS production and key gene regulation for antioxidant enzymes in tissues will provide essential protection against oxidative stress-mediated fibrosis in kidneys (Nlandu Khodo et al., 2012, Ruiz et al., 2013). Activation of a transcription factor known as nuclear factor erythroid-derived 2-related factor 2 (Nrf-2) is responsible for the increased antioxidant enzymes levels and phase-2-detoxifying enzymes such as heme oxygenase-1 (HO-1) (Ruiz et al., 2013, Nabavi et al., 2016). A previous study found that reduced Nrf-2 expression may increase ROS generation and kidney injury (Chen et al., 2019). Moreover, oxidative stress, inflammation, immune cell infiltration, and nuclear factor- kappa B (NF-κB) activation have been found in the remnant kidney of a partial nephrectomy–induced renal damage in rats (Fujihara et al., 2007). Restoration of Nrf-2-HO-1 axis by antioxidants may protect the kidney from injury and fibrogenic process (Selim et al., 2021). A previous study reported the renoprotective activity of resveratrol, a prominent antioxidant compound (Kitada and Koya 2013). Moreover, resveratrol may be able to modulate the function of NF-кB and Nrf-2 in various tissues to prevent oxidative stress and inflammation (Hou et al., 2019). Resveratrol (3,5,4ˈ-tri hydroxyl stilbene) is a natural polyphenolic compound and is well documented for its anticancer, antitumor, and anti-aging properties (Baur and Sinclair 2006). Resveratrol is mainly found in grape skin, red wine, apples, peanuts, soy, and other various fruits and belongs to the stilbene family (Chastang et al., 2018). Previous report suggested that resveratrol could suppress oxidative stress and ameliorate renal injury in HF diet-fed rats (Pan et al., 2014). Resveratrol may also alleviate HF diet-induced kidney injury by decreasing the inflammation, lowering tumor necrosis factor-α, and interleukin-6 concentrations and lipid peroxidation (Cheng et al., 2019). However, resveratrol-mediated prevention of fibrosis in the kidneys of HF diet-fed rats was not appropriately explained before. Thus, this investigation will address the molecular mechanism of resveratrol-mediated preventive effect against oxidative stress and fibrosis in the kidneys of HF diet-fed rats.
The beef tallow that was used in the HF diet formulation, was purchased from Dhaka New Market, Bangladesh. The reagent kits for assaying creatinine and uric acid were purchased from DCI diagnostics (Budapest, Hungary). Thiobarbituric acid (TBA) was purchased from Sigma (USA), and resveratrol was bought from Xi'an Surnature Biological Technology Co. Ltd, China. The kits for mRNA isolation and RT-PCR were purchased from Thermo-Fisher Scientific (Massachusetts, USA), and Bio-Rad, USA.
In this experiment, Wistar male rats were divided into four groups, where each group had seven rats. All rats were about ten to twelve weeks old, and the range of their body weights were about 185–200 g. All rats were kept in the Animal house unit of the Department of Pharmaceutical Sciences, North South University, where the rats were given free access to water and food. All rats were kept in an individual cage, and the room temperature was (22 ± 3 °C) with 55% humidity. Light and dark circle was maintained for about 12 h each. All experimental protocols were approved by the animal research Ethical Committee of North South University for animal care and experimentation (AEC 006-2017). The groups are assigned as Control, Control + RSV, HF, and HF + RSV. The Control group only received control food and water for 56 days. Control + RSV group took control food and water with daily oral gavage of resveratrol with a dose of 100 mg/kg body weight. HF group received only a high-fat diet and water for 56 days. Finally, HF + RSV group received a high-fat diet and regular water with daily oral gavage of resveratrol, and the dose was 100 mg/kg body weight. This dose was selected based on the previous reports suggesting that a 3000 mg/kg dose may develop nephrotoxicity in rats (Crowell et al., 2004). The HF diet was prepared and transformed into a pellet form in the laboratory (Table 1). In control food, calories from fat, carbohydrates and proteins are 13.5%, 57% and 14% respectively. In HF diet, calories percentages were about 48% from fats, 14% from proteins and 37 % from carbohydrates. For all 56 days, body weight, food, and water intake were recorded for all groups. On the 57th day, all rats were sacrificed using ketamine hydrochloride (60 mg/kg), administered intraperitoneally. Blood and kidney samples were collected. The kidney samples were divided into three parts, one for biochemical assay and the cortex was used to isolate the mRNA expression. Another kidney part was used for the histological assessment through various staining processes. Biochemical assay samples were preserved at −20 °C, and the histological samples were kept in neutral buffer formalin of pH 7.4.
The blood was collected from large abdominal veins and taken into heparinized tubes. After the collection of blood samples, they were centrifuged for 15 min at 8000 g and at 4 °C. The plasma was separated, transferred into 1.5 mL microcentrifuge tubes, and stored at −20 °C for biochemical assay.
Kidney tissues were homogenized in phosphate buffer saline (pH 7.4). The homogenates were then centrifuged at 8000 g (at 4 °C) for 15 min. The clear supernatant was collected for the determination of enzymatic activities and protein content. By following the previously described method, the malondialdehyde (MDA) level was measured for lipid peroxidation in kidney tissue (Rahman et al., 2017). MDA solution was used as the standard curve of malondialdehyde while the unit was expressed as nmol/g tissue. By measuring nitrate, nitric oxide content was measured in the tissue homogenates (Rahman et al., 2017). Compared to a blank solution, reagent solutions' absorbance with tissue homogenates was measured at 540 nm. The unit of nitric oxide level was expressed as nmol/g tissue using a standard curve. The advanced oxidation protein product (AOPP) level was also determined by following the previously described method (Rahman et al., 2017). In this assay, chloramine-T was used in different concentrations as standard. The absorbance of chloramine-T was taken at 340 nm, where the range was about 0 to 100 nmol/mg. The concentration unit of AOPP was expressed as nmol/mg chloramine-T equivalents.
The previously published literature described the catalase activity assay in detail (Rahman et al., 2017). An absorbance change of 0.01 units/min is considered as one unit of catalase activity. SOD activity was also determined by the previously described method (Rahman et al., 2017). In the assay system, auto-oxidation of epinephrine was measured. 50% inhibition of auto-oxidation of epinephrine is defined as one unit enzyme activity. GSH was also determined using the method previously described by Rahman et al. (Rahman et al., 2017). The yellow color was developed from the reagent mixture, and immediately absorbance was taken at 412 nm. Unit of GSH was measured as ng/mg protein.
After sacrificing rats of all groups, kidneys were collected immediately, maintaining an RNAse free environment. The total mRNA was isolated from the cortex part of the kidneys, and for the purification of total mRNA; Thermo-Fisher Scientific (Massachusetts, USA) GeneJET RNA purification kit was used. The mRNA concentration was measured in a NanoDrop 2000 spectrophotometer (Thermo-Fisher Scientific, Massachusetts, USA). In T100 Thermal Cycler (Bio-Rad, USA), a cDNA synthesis kit (RevertAid First Strand cDNA Synthesis Kit, Thermo-Fisher Scientific, USA) was used to synthesize cDNA. Two parameters were measured such as inflammation-related proteins and oxidative stress-related proteins where cDNA was quantified in transcript level related qRT-PCR, which was used with SYBR Premix Ex Taq (Tli RNaseH Plus) and it was analyzed with CFX96 C1000 Touch Real-Time PCR Detection System (Bio-Rad, USA) and data were analyzed by CFX Manager TM Software (CFX Manager TM Software) according to the manufacturer’s protocol. In Table 2, oligonucleotides of forwarding and reverse primers in the qRT-PCR were enlisted, in which Primer3 online software was used for primer design. Three processes were involved in a polymerase chain reaction: denaturation, annealing, and extension. Protein denaturation was done at 95 °C for 1 min after that it was amplified by 40 cycles at 95 °C, which stayed for 5 s. The final step was performed for 1 min at 72 °C and after that, the last extension was done for 5 min at 72 °C. β-actin was used as a control for gene expression for normalization and the target gene of transcript level was measured.
For histological assessment, two staining processes were selected such as hematoxylin/eosin and Sirius red staining. Kidney tissues were kept in neutral buffered formalin for a week to get complete fixation. The tissues were then undergone graded alcohol and xylene treatment and were embedded in paraffin blocks. These blocks were then sectioned carefully at 5 µm thickness, using a microtome. Hematoxylin/eosin staining was used to assess inflammatory cell infiltration, and Sirius red staining was used to see collagen deposition in the kidney section. A light microscope (Zeiss Axioscope, Germany) was used to take the picture at 40X magnification. Image J free software (Version 1.50i) from the National Institute of Health, United States of America was used to semi-quantitatively measure the percentage of fibrosis in the kidney sections.
For statistical calculation, mean ± standard error of mean and mean ± standard deviation were used. All results were evaluated by using One-way ANOVA followed by Tukey post hoc test. Two way ANOVA was also performed to see the effect of diet and treatment on these animals. Graph Pad Prism software (Version 6.2) was used for all the analysis. In all cases, statistical significance was considered at p < 0.05.
HF diet caused the rise in kidney wet weight in rats compared to the control rats (Fig. 1). The HF diet-fed rats treated with resveratrol showed decreased kidney weight, but the reduction is not significantly different from HF diet-fed rats (Fig. 1). Resveratrol treatment did not change the wet weight of kidneys in control rats. Creatinine level in plasma is increased in HF diet-fed rats compared to the control (P < 0.05). Resveratrol treatment lowered the creatinine level in plasma of HF diet-fed rats (Fig. 2A), however, resveratrol treatment in control rats did not alter the creatinine level in plasma compared to control rats only (Fig. 2A). HF diet fed rats also showed increased uric acid levels (P < 0.05) compared to the control rats (Fig. 2B). Resveratrol treatment prevented the rise of uric acid levels in the plasma of HF diet-fed rats (Fig. 2B). However, no significant changes were observed in uric acid levels in the plasma of control rats treated with resveratrol.
Lipid peroxidation is a crucial parameter for measuring oxidative stress. MDA is known as a byproduct of lipid peroxidation, which was increased significantly (p < 0.05) in the HF diet-fed rats compared to the control rats (Fig. 3A). Resveratrol treatment reduced MDA concentration in the kidneys of HF diet-fed rats significantly (P < 0.05) (Fig. 3A). In line with this evidence, nitric oxide levels were also increased significantly in the kidney of HF diet-fed rats significantly (P < 0.05) while comparing to the control rats (Fig. 3B). Resveratrol treatment normalized the nitric oxide levels in the kidney homogenates of HF diet-fed rats (Fig. 3B). HF diet-fed rats also showed increased AOPP levels significantly (p < 0.05) compared to control rats (Fig. 3C). Resveratrol treatment prevented the rise in AOPP levels in the kidney homogenates of HF diet-fed rats compared to HF diet-fed rats (Fig. 3C).
HF diet feeding in rats showed decreased antioxidant enzyme activities in tissues. Compared to control rats, catalase and SOD activities were significantly declined in the kidneys of HF diet-fed rats (p < 0.05). Resveratrol treatment in HF diet-fed rats restored the catalase and SOD activities in the kidneys (Fig. 4A and 4B). Moreover, GSH level was also decreased in the kidneys of HF diet-fed rats significantly (p < 0.05), which was restored by resveratrol treatment (Fig. 4C).
In this experiment, inflammation and fibrosis triggering genes expression were evaluated in the kidneys of HF diet-fed rats. Expression of six genes such as interleukin-1 (IL-1), interleukin-6 (IL-6), transforming growth factor beta-1 (TGF-β1), tumor necrosis factor-alpha (TNF-α), nuclear factor-kappa B (NF-κB), and inducible nitric oxide synthase (iNOS) were analyzed (Fig. 5 and Fig. 6). The study findings revealed that the expression of IL-1, IL-6 and TNF-α genes were significantly (P < 0.05) increased in the kidneys due to HF diet feeding in rats (Fig. 5). TGF-β1, iNOS and NF-кB expressions were also raised significantly in HF diet-fed rats compared to the control rats (Fig. 6). This investigation also revealed that resveratrol treatment successfully suppressed the expression of all these pro-inflammatory and inflammatory genes expression in the kidneys of HF diet-fed rats. It is to be noteworthy that the genes expression of main fibrosis-related proteins such as TGF-β1 and IL-1 were decline significantly (P < 0.05) due to resveratrol treatment in HF diet-fed rats (Fig. 5 and Fig. 6).
Transcript levels of Nrf2 in the kidneys of HF diet-fed rats were declined compared to the control rats (Fig. 7). Resveratrol treatment restored the Nrf2 expression in the kidneys of HF diet-fed rats (Fig. 7). Significant (p < 0.05) up-regulation of HO-1 and HO-2 transcript levels were also detected in HF diet-fed rats treated with resveratrol (Fig. 7). Further, gene expression of endogenous antioxidant enzymes including SOD, catalase, and GPx was also reduced in the kidneys of HF diet-fed rats (Fig. 8). Further, resveratrol treatment augmented the gene expression of these antioxidant enzymes in the kidneys of HF diet-fed rats significantly (Fig. 8).
In assessing potential consequences of HF diet induced lipotoxicity on the kidney such as cellular damage, inflammation and fibrosis, hematoxylin and eosin staining, and Sirius red staining revealed that control rats showed no lipid accumulation and necrosis zone in the kidneys (Fig. 9A, Upper panel). Resveratrol-treated control rats also showed similar kidney structures as in the control rats (Fig. 9B). HF diet-fed rats showed lipid accumulation and necrosis in the kidneys (Fig. 9C) which was ameliorated by resveratrol treatment (Fig. 9D). Moreover, control rats and control rats treated with resveratrol showed no fibrosis in the kidney sections (Fig. 9 E and F, Lower panel). HF diet-fed rats showed substantial collagen deposition and fibrosis in the kidneys (Fig. 9 6G, Lower panel) which was prevented by resveratrol treatment (Fig. 9 H, Lower panel). The percentage fibrosis was quantified semi-quantitatively and is presented in Fig. 10. HF diet fed rats showed an increased percentage of fibrosis compared to the control rats, whereas, resveratrol treatment decreased the percentage of fibrosis in the kidneys (Fig. 10).
Chronic kidney dysfunction is increased in metabolic diseases induced by HF diet (Rangel Silvares et al., 2019). This investigation revealed that HF diet feeding in rats developed oxidative stress and declined antioxidant capacities in the kidney. Resveratrol treatment in HF diet-fed rats showed restoration of antioxidant enzymes such as SOD, GPx, and catalase through transcriptional regulation via Nrf-2. This study also revealed that resveratrol treatment prevented fibrosis in the kidneys of HF diet-fed rats. ROS-induced oxidative stress and lipid peroxidation are significant contributors to HF diet-induced tissue damage in the kidneys (Noeman et al., 2011). In this study, it was evident that HF diet-fed rats showed increased tissue lipid peroxidation. And it is noteworthy to mention that resveratrol treatment may prevent lipid peroxidation in the kidney tissues. This result is supported by previous investigations, which reported that resveratrol might prevent lipid peroxidation in diabetes and kidney dysfunction (Palsamy and Subramanian 2011). Another notable oxidative stress marker is nitric oxide, which may turn into peroxynitrite radicles and cause irreversible cellular damage after reacting with superoxide anions. Nitric oxide may play a dual role in the kidney pathophysiology and may be considered as a harmful marker of chronic kidney disease (Carlström 2021). HF diet-fed rats showed an increased nitric oxide level and iNOS gene expression, which is supported by the previous research reports (Ulla et al., 2017, Martin et al., 2018). It has been reported that iNOS inhibitor, N6-(1-iminoethyl)-l-lysine hydrochloride (l-NIL) administration in HF diet-fed mice prevented the metabolic syndrome and ameliorated proteinuria, decreased N-acetyl-β-d-glucosaminidase excretion and lowered renal triglyceride content (Martin et al., 2018). In the present study, resveratrol normalized the nitric oxide level. Resveratrol also decreased iNOS expression in kidneys of HF diet-fed rats, and our findings are in alignment with the previous study that showed that resveratrol treatment inhibited the iNOS expression (Youn et al., 2009). The benefit could be attributed to the restoration of antioxidant enzyme activities and lowered inflammatory state (Saldanha et al., 2016). However, one of the limitations of this study was the lack of protein expression measurement for iNOS. The mRNA expression sometime may not correlate with the protein synthesis and functional activities. In this study the NO level is significantly increased in the kidneys of HF diet-fed rats which could be attributed to the increased iNOS activity (Cao et al., 2012). Resveratrol administration stimulates antioxidant function through transcriptional regulation via nuclear factor E2-related factor 2 (Nrf-2) mediated way (Saldanha et al., 2016). This study also revealed that resveratrol augmented Nrf-2 expression in kidneys, leading to increased expression of anti-oxidant genes including HO-1, HO-2, SOD, catalase, and GPx in the kidneys of HF diet-fed rats. In agreement with this, the activities of SOD and catalase were also increased in HF diet-fed rats treated with resveratrol. HO-1 and HO-2 are isoforms of heme-oxygenase enzymes and HO-1 is a more potent antioxidant than the HO-2 (Funes et al., 2020). The previous report suggests that HO-1 provides an antioxidant activity by increasing SOD and catalase activities or decreasing iNOS expression in diabetic rats (Turkseven et al., 2005). Moreover, another study demonstrated that treatment of resveratrol could effectively restore an antioxidant enzyme function which was declined due to kidney dysfunction (Pan et al., 2014). Other study also provided evidence that HF diet may down-regulate MnSOD expression in the kidney of mice which was restored by resveratrol treatment (Zhang et al., 2016). Lipid accumulation and infiltrating macrophages are the sources of inflammation, leading to the production of inflammatory cytokines in the damaged kidneys. In this study, glomerular disorientation, podocyte loss and lipid accumulation have been noticed in kidney sections of HF diet-fed rats. Inflammatory genes such as IL-1, IL-6, TNF-α, TGF-β, iNOS and NF-κB expression were increased in the kidneys of HF diet-fed rats. Inflammatory cytokines play a crucial role in developing kidney dysfunction in HF diet-induced obesity (Stemmer et al., 2012, van der Heijden et al., 2015). Resveratrol is a potent anti-inflammatory molecule that prevents inflammatory responses (de Sá Coutinho et al., 2018). In this study the transcript levels of inflammatory cytokines such as IL-1, IL-6, TNF-α, and TGF-β and associated factor (NF-кB) and enzyme (iNOS) were increased, in renal tissues, due to the consumption of HF-diet. (Li et al., 2020). Cytokines like IL-6, TNF-α may give signaling for the activation of TGF-β (Kany et al., 2019). Moreover, it has been reported that IL-6 signaling and NF-кB activation lead to the cellular proliferation and thickening of the glomerular basement in diabetic kidney (Navarro-Gonzalez and Mora-Fernandez 2008). TGF-β is considered the master key regulatory for activating fibroblasts cells to stimulate the production of extracellular matrix (ECM) (Loeffler and Wolf 2013). Moreover, in glomerulosclerosis, TGF-β expression has been increased followed by the increased TGF-β receptors in the glomeruli and the tubulointerstitium region in kidneys (Yamamoto et al., 1996). In this study, HF diet-fed rats showed increased interstitial collagen deposition and TGF-β expression in kidneys. Resveratrol treatment prevented collagen deposition and lowered cytokine expression in the kidneys, including NF-кB and TGF-β expressions. This finding is also supported by a previous study which reported that resveratrol inhibits TGF-β and decreases kidney fibrosis in chronic kidney diseases (Huang et al., 2014). In this study, a 100 mg/kg dose of resveratrol was used. The daily recommended intake of resveratrol in a human was not found in any literature. The dose used in this study was approximately equal to ∼0.6 g/day based on body surface area comparisons between rats and humans (Reagan-Shaw et al., 2008). However, the total intake of polyphenols is ∼1 g/day (Scalbert and Williamson 2000). Thus, the dose of resveratrol used in this study is realistic in humans. These findings suggest that resveratrol exerted a remarkable kidney protective effect against HF diet-induced renal dysfunction and fibrosis in rats. This study also provided experimental evidence that resveratrol is a regulator of antioxidant and inflammatory genes and brought down lipid peroxidation levels in kidneys. This effect of resveratrol is a plausible impact and urges further clinical investigation in renal illness.
This study was supported by the Taif University Researchers Supporting Project (TURSP-2020/197), Taif University, Taif, Saudi Arabia.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649357 | Ahmed Mohsen Elsaid Hamdan,Zuhair M. Mohammedsaleh,Aalaa Aboelnour,Sherif M.H. Elkannishy | Preclinical study for the ameliorating effect of l -ascorbic acid for the oxidative stress of chronic administration of organic nitrates on myocardial tissue in high sucrose/fat rat model | 19-07-2022 | Methemoglobin,Organic nitrates,Oxidative stress,l -ascorbic acid,ALDH-2, Aldehyde Dehydrogenase–2,CAT, Catalase Activity,GTN, Glyceryl trinitrate,HSF, High Sucrose/Fat,NO, Nitric Oxide,RNS, Reactive Nitrogen Species,SOD, Superoxide Dismutase,TIBC, Total Iron Binding Capacity,XO, Xanthine Oxidase | Graphical abstract | Preclinical study for the ameliorating effect of l -ascorbic acid for the oxidative stress of chronic administration of organic nitrates on myocardial tissue in high sucrose/fat rat model
Hypertension, congestive heart failure, angina pectoris, ischemic heart disease and cerebrovascular disease are the worldwide main reason for deaths and aliment adjusted life years (Steven et al., 2018, Gori, 2020). ISMN is an organic nitrate vasodilator that represents a promising therapeutic option for both treating and preventing such cardiovascular diseases. On the molecular level, GTNs, including the organic nitroglycerins, act as pro-drugs for nitric oxide which is a highly effective vasodilator drug. Bioactivation of nitrate occurs when inert nitrite is converted to NO by mitochondrial aldehyde dehydrogenase–2 (Signorelli et al., 2020, da Silva et al., 2021). NO plays an important role in relaxing the vascular smooth muscle and increasing blood flow to the myocardium (Mullane et al., 2020). GTNs are recommended for the treatment of stable coronary artery disease that is still symptomatic despite treatment with aspirin, beta-adrenergic receptor blockers or ACE inhibitors (Kim et al., 2020). The use of GTN is widely limited by its chronic administration procedure due to the rapid emergence of tolerance and/or endothelial dysfunction. In most cases, tolerance is explained by the increased intracellular oxidative stress. The anti-ischemic effect of GTNs is rapidly lost with chronic administration of low doses. This phenomenon is explained by the reactions of RNS; NO, peroxynitrite and Nitric dioxide, with lipids and other biomolecules under increased intracellular oxidative stress (Daiber and Münzel, 2015, Knuuti et al., 2020). The undesirable and poorly tolerated hemodynamic side-effects of GTNs such as headache and orthostatic hypotension can often occur owing to systemic vasodilatation (Ivy, 2019). Treatment with GTNs raises the cellular level of generated reactive oxygen species (ROS) within the vascular tissue. SOD, CAT and GSH are cellular defense mechanisms providing protection against the damage effect of free radicals and ROS (Pearson and Butler, 2021). GTNs chronic administration leads to biotransformation into NO in the reticulocytes of the vascular tissues which in turn causes oxidative stress, SOD inhibition, depletion of GSH pool and decreased bioavailability of NO (Awad et al., 2018). Tolerance due to chronic administration of GTNs, reduction of its clinical response, leads to the urgent need of increasing the GTNs dosages in order to maintain the vasodilating effect (Tauzin et al., 2018). The molecular mechanism of this tolerance includes that GTNs interferes with purine metabolism and drain intracellular ATP and GTP. GTNs block xanthine oxidase (XO), a rate-limiting enzyme for the metabolic bioactivation of GTNs into NO. l-ascorbic acid prevented XO blockage which results in an increased NO production from GTN (Axton et al., 2018, Marini et al., 2022). Nitrate tolerance is handled by generating a 12-h nitrate-free interval. Although this strategy can manage nitrate tolerance, it results in an increased risk of cardiovascular problems during these free periods, especially in the early light phase (Axton et al., 2018). NO metabolism mainly occurs in the RBCs (Graham et al., 2021, Vaz-Salvador et al.,2022). Besides, NO bioactivity is preserved by RBCs. Metabolism of GTNs leads to the formation of inorganic nitrates that can oxidize hemoglobin into methemoglobin (Awad et al., 2018). Organic nitrates cause oxidation of hemoglobin (Hb) resulting in methemoglobin (MetHb) formation and O2 liberation. This autoxidation reaction of oxyhemoglobin is super rapid (Tai et al., 2020). Such elevated MetHb content is a dose-dependent on the organic nitrates concentrations (Hathazi et al., 2018). MetHb production catalysis binding of oxygen to normal hemoglobin, so less oxygen will be available at the tissue level (Wang et al., 2013). Iron is present in the ferric state (Fe3+) in methemoglobin, so it cannot bind to oxygen. This procedure is irreversible resulting in NO oxidation to nitrate and NO resistance. Subsequently, serious hypertension, lipid peroxidation, and vascular injury are followed. Both cellular oxidative stress and inflammatory tissue injury can be occurred due to the released heme and globin as a result of the catabolism of NO and/or oxyhemoglobin and methemoglobin (Fidanzio et al., 2021, Cao et al., 2021). GTN tolerance is accompanied by raising the cellular level of free radical-induced lipid peroxidation (Wang et al., 2021, Meegan et al., 2021). Nitrate tolerance has been considered to be a limitation of nitrate therapy. The effectiveness and benignity of nitrate therapy could be dramatically improved (two to three times) upon concomitant administration of antioxidants which prevents the need for nitrate-free intervals (Marini et al., 2022, Knuuti et al., 2020). Ascorbic acid is a potent electron donor water-soluble antioxidant. It efficiently preserves the body functions against oxidative stress and is a cofactor in numerous essential enzymatic reactions (Jurt et al., 2001). Ascorbic acid enhances endothelial vasodilation and prevents endothelial dysfunction (Cartaya et al., 2019, Doseděl et al., 2021). Antioxidant agents are responsible to convert methemoglobin back to hemoglobin (Plotnick et al., 2017, Cartaya et al., 2019).
ISMN was obtained from Sigma-Aldrich Chemical Co., Merck KGaA, Darmstadt, Germany. It was freshly dissolved in double-distilled water (0.3, 0.6 and 1.2 mg/kg). l-ascorbic acid was obtained from El-Gomhoria Co., Cairo, Egypt (20 mg/kg). It was dissolved in double-distilled water. All other chemicals and reagents were analytical grade and were purchased from El-Nasr Co., Cairo, Egypt.
As previously described in Kottaisamy et al. (2021), Forty-eight 4-week-old male Wistar rats (experimental animal house, Cairo University, Egypt) fed with HSF diet (body weight, 192.19 ± 8.12 g) placed in conventional cages (3 rats/cage) were divided into eight groups in a random manner. The HSF diet consisted of a 79% normal diet, 10% sucrose, 5% lard, 5% cholesterol, and 1% lithocholic acid. They were kept for two weeks before the experiments started in rooms with controlled temperature (23 ± 2 °C) and humidity (50 ± 5%) and a 12/12 h light/dark cycle starting at 6:00 am. Both their weight and their caloric intake were recorded weekly. The methods got approval by the ethical research project committee in the University of Tabuk (UT-44-10-2019) and all steps were performed in accordance with its relevant guidelines and regulations. The ethical committee in the Faculty of Pharmacy, University of Mansoura, Mansoura, Egypt (2019–62), approves all animal facilities and animal protocols. All animals were fed with HSF diet for twelve weeks. ISMN was dissolved in water and given orally at three different doses (0.3 mg/kg, 0.6 mg/kg and 1.2 mg/kg body weight). These range of doses has been used previously for a wide range of oral administered doses of ISMN for different conditions of heart diseases in the rat model (Li et al., 2018, Arnaud et al., 2017, Tai et al., 2020). In this study, we studied the oxidative stress effect after the chronic administration of this wide range of doses. An oral doses of l-ascorbic acid was dissolved in water and given orally (20 mg/kg body weight). Such dose was previously reported to have a cardioprotective effect against chronic administration of doxorubicin for doxorubicin-induced cardiotoxicity in the rats when they were used per oral route (Buttros et al., 2009).
Group (1): Control group which fed with HSF diet only. Water and food were given freely. Group (2): l-ascorbic acid group which was given l-ascorbic acid 20 mg/ kg body weight orally once daily for twelve weeks. Group (3) was given ISMN 0.3 mg/kg orally once daily for twelve weeks. Group (4) was given ISMN 0.3 mg/kg followed by an oral dose of l-ascorbic acid. Group (5) was given ISMN 0.6 mg/kg orally. Group (6) was given ISMN 0.6 mg/kg followed by an oral dose of l-ascorbic acid. Group (7) was given ISMN 1.2 mg/kg orally. Group (8) was given ISMN 1.2 mg/kg followed by an oral dose of l-ascorbic acid. Animals were sacrificed by cervical dislocation under light ether anesthesia.
Blood samples were drained from each rat in each group and centrifuged for isolation of the serum. Samples were stored at –70 °C. The following investigations were done:
Methemoglobin, Carboxyhemoglobin, oxygen saturation, and oxygen content were measured using (Arnaud et al., 2017, Li et al., 2018). Briefly, Methemoglobin is measured spectrophotometrically at 540 nm after complete transformation into Cyanmethemoglobin using potassium cyanide (Li et al., 2018. Carboxyhemoglobin was assayed spectrophotometrically with automated multi-wavelength method (Kozlova et al., 2020). A double‐beam Shimadzu model UV-1900i UV–Vis Spectrophotometer (1.0 cm quartz cells) was used for all absorbance measurements. Oxygen saturation and oxygen content were measured using Radiometer OSM3; Hemoximeter adjusted for rat blood.
The activity of the serum lactate dehydrogenase was measured using a commercial kit (Sangong Biotech, Shanghai, China) according to the instruction of the manufacturer. Briefly, 10 μl of serum was mixed with a working reagent containing Tris buffer (80 mM; pH 7.4), pyruvate (1.6 mM), NaCl (200 mM) and NADH (240 mM), mixed well and incubated at 37 °C for 1 min and measured the change in absorbance per minute for 3 min duration at 340 nm.
Serum creatinine kinase-MB activity was estimated using a commercial kit (Sangong Biotech, Shanghai, China) according to the instruction of the manufacturer. Briefly, 40 μl of serum was mixed with 1 ml of a working reagent. The contents were mixed thoroughly and incubated at 37 °C for 100 s and the change in absorbance per minute was recorded spectrophotometrically at 340 nm for 5 min.
The activity of SOD was also measured spectrophotometrically at the same wavelength (540 nm) as described by Boriskin et al. (2019). Briefly, the blood serum was centrifuged in for 10 min at 6000 rpm. Afterwards, we added phosphate buffer (pH 7.4) to the supernatant with ration 3:1. Then, we centrifuged at 5000 rpm for 15 min, precipitated the hemoglobin using of 2:1 chloroform: methanol solution with a ratio of 2:1 for 10 min, centrifuged the reaction mixture, diluted with phosphate buffer 20 times and incubated the mixture for 10 min with 57 μm nitro blue tetrazolium, 98.5 μm NAD·N and 16 μm fenasintrasalud in a 0.5 M phosphate buffer with EDTA (pH 8.3) at a temperature of 25 °C in aerobic conditions. The activity of SOD was calculated by the formula (Huo et al., 2021):
It was done using the standard method of Boriskin et al. (2019). Briefly, we stated ca reaction of 0.1 ml of blood serum to 2 ml of 0.03% hydrogen peroxide solution and leave for 10 min. Afterwards, we stopped the reaction with 1 ml of 4% ammonium molybdenum. The Color intensity was measured spectrophotometrically at a wavelength of 410 nm. CAT is calculated by the formula [Rosa et al., 2021]:
The amount of MDA was measured as previously mentioned in Hamdan et al (Kumar and Gill, 2018). Briefly, we added trichloroacetic acid to the serum in order to precipitate all proteins. Then, we added thiobarbituric acid in order to form thiobarbituric acid. Then, we measured the color intensity at a wavelength of 532 nm.
The serum NO concentrations were measured indirectly spectrophotometrically in a two-step process. Initially, we determined the total serum NO for both nitrate and nitrite concentrations using the previously mentioned method (Csonka et al., 2015). Briefly, we added modified Griess’s reagent (1% sulphanilamide and 0.1% naphthyl ethylenediamine in 5% phosphoric acid) to the serum for 10 min till developing of a deep purple azo mixture and the absorbance was measured at 546 nm. Then, we subtracted the directly measured serum nitrite from the measured total nitrite nitrate concentrations to calculate the serum nitrate concentrations.
We measured the serum activity of GSH-Px also spectrophotometrically at 412 nm as described by Han et al. (2021). Briefly, we added 171 mM of K2HPO4/KH2PO4, 4.28 mM of NaN3, 2.14 mM of EDTA, 6 mM of reduced GSH, 0.9 mM of NADPH, and 2 U./mL of glutathione reductase. We added 50 µl of reaction mixture containing 5 mg of 5,5′, dithiobisnitrobenzoic acid in 5 ml assay buffer stored on ice to form a complex.
Serum iron and TIBC were measured using immunoassay studies which were carried out using semi-auto analyzer Beckman Coulter AU480.
We homogenized 100 mg frozen myocardium of each rat in each group of rats in 1 ml of TRIzol® (Thermo Fisher Scientific, Inc., USA). We performed all the processes for total RNA extraction according to the manufacturer's protocol. Total RNA concentration was adjusted spectrophotometrically at a wavelength of 260 nm using nuclease-free water. Real-Time was performed using a high capacity cDNA reverse transcription kit (Thermo Fisher Scientific, Inc., USA) as directed in the manufacturer's protocol.
All genetic expressions of the following genes; nrf2, NF-κB and caspase-3 were assayed as described previously (Hamdan et al., 2019). We used the sequences of the primers as shown in Table 1. The primers designed using PrimerQuest for spanning exon junctions’ mode.
Total protein was extracted from cardiac lysates in RIPA buffer containing protease inhibitor cocktail (Sigma-Aldrich, St Louis, MO, USA). After incubation at 4 °C for 1 h, the cell suspension was centrifuged at 12,000 rpm for 15 min at 4 °C, and the supernatant was collected. Protein content in the supernatant was quantified using the BCA protein assay kit (Pierce, Rockford, IL, USA) as per the manufacturer’s instructions. An equal amount of protein (50 μg) was separated by 10% SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membranes using turbo trans-blot apparatus (BD Bioscience, USA). The membrane was blocked with 5% BSA in TBST for 1 h at room temperature. It was further washed three times with TBST for 10 min each. The membrane was incubated at 4 °C overnight in 5% BSA in TBST containing primary antibodies (Santa Cruz Biotechnology, USA) to one of the following: Nrf2 (1:1000), NF-κB (1:1000), caspase-3 (1:800) and anti-β-actin (1:1000). After washing with TBST, the membrane was incubated with peroxidase-conjugated corresponding secondary antibodies for 1 h at room temperature. After washing, images were captured on films, which were placed in Pierce ECL Western Blotting Substrate.
Directly after dissection, the heart was taken out, cleaned and fixed in 10% neutral buffered formalin solution for the preparation of histopathological slides. After fixation, tissues were dehydrated in different series of ethanol, cleared in xylene and embedded in paraffin wax. The solid sections were prepared at 5 mm thickness using a microtome, stained with haematoxylin-eosin (H&E)/ Masson’s trichrome. The sections were examined under a light microscope and photographs were taken.
All results were shown as mean ± standard deviation (SD). We used a one-way analysis of variance (ANOVA). It was preceded by Tukey’s post hoc test using GraphPad Prism® software version 5. For all analysis, the level of statistical significance was set at P < 0.05.
A significant increase in methemoglobin and carboxyhemoglobin, after continuous exposure to ISMN to rats (p < 0.05) in a concentration-dependent manner compared to the control group (Fig. 2A, B). Meanwhile, continuous administration of ISMN to rats leads to a decrease in oxygen saturation, and oxygen content, hemoglobin content (p < 0.05) in a concentration-dependent manner (Fig. 2C, D). Concomitant administration of the l-ascorbic acid leads to a decrease in the increased levels of methemoglobin and carboxyhemoglobin and to restoration of the decreased level of the increased levels of the hemoglobin content, oxygen saturation, and oxygen content for the ISMN treated groups.
Continuous exposure of rats to ISMN leads to a significant increase in the level of the MDA activity (p < 0.05) in a concentration-dependent manner (Fig. 3A). Meanwhile, continuous exposure to ISMN leads to the reduction of the activity of all of the following; SOD, CAT, GSH and NO content level (p < 0.05) in a concentration-dependent manner (Fig. 3B). Concomitant administration of l-ascorbic acid leads to restoration the increased level of MDA and to ameliorating the decreased levels of SOD, CAT, GSH and NO content for the ISMN treated group (Fig. 3).
Continuous administration of organic nitrates to rats leads to a reduction of both the serum iron level and TIBC (p < 0.05) in a concentration-dependent manner. Concomitant administration of l-ascorbic acid leads to restoration the decreased level of the serum iron and the TIBC in the rats (Fig. 4A, B).
Continuous administration of organic nitrates to rats resulted in an increase of lactate dehydrogenase and CK-MB in a concentration-dependent manner (p < 0.05) to normal rats. Concomitant administration of l-ascorbic acid leads to restoration the increased level of the lactate dehydrogenase and the CK-MB in the rats (Fig. 5 A and B).
Continuous exposure to organic nitrates caused a significant reduction of the genetic expressions of both nrf2 mRNA and the protein NRF2 as well in the myocardium tissues in a concentration-dependent manner. However, l-ascorbic acid restored the normal level of nrf2 and NRF2 in the myocardium tissue (Fig. 6A). On the same way, continuous exposure of rats to organic nitrates led to a significant increase in the genetic expressions of nuclear factor NF-κB mRNA and protein in the myocardium tissue in a concentration-dependent manner (Fig. 6B). Besides, continuous exposure of rats to organic nitrates led to a significant increase in the genetic expressions of apoptotic marker caspase-3 mRNA and protein in the myocardium tissue in a concentration-dependent manner (Fig. 6C). However, l-ascorbic acid restored the increased levels of both mRNA and proteins of NF-κB and caspase-3 in the myocardium tissue to their original levels.
Continuous exposure to the HSF diet and 20 mg/kg body weight of l-ascorbic acid has no effect on the normal cardiac tissues (Fig. 7A1, 7A2, 7B1 and 7B2) and has no fibrosis (7C1 and 7C2). Exposure to 1.2 mg/kg body weight of ISMN leads to a moderate Zenker’s necrosis (thin black arrows) (6A3 and 6B3) accompanied by mononuclear inflammatory cells aggregation (green head arrows), cytoplasmic vacuoles (yellow arrows) (7A3), with mild degeneration of myofibrils (thick black arrows) (7B3) and mild intermuscular fibrosis (7C3). The group treated with ISMN (1.2 mg/kg) followed by l-ascorbic acid (7A4 and 7B4) showed mild recovery of myocardial lesions with normal myofibril structures (7B4), but some remained mild necrosis (black arrow) (7A4) and a gradual decrease in intermuscular fibrosis (7C4).
The act of NO is multifaced. It can be as a messenger for either a pro-oxidant or an antioxidant in biological systems (Han et al., 2021). All organic nitrates undergo biotransformation releasing NO (Lu et al., 2019). Chronic exposure to nitrates induces oxidative stress and exerts disastrous pathophysiological effects including oxidative stress, inflammation, autoimmune disease and cancer (Meera et al., 2020). This may be directly due to the potential liability of mediating DNA damage or indirectly through the production of RNS free radicals. NO has a short half-life and is widely formed through the vascular endothelium, resulting in direct relief of the smooth muscles of the vascular tissues (Lu et al., 2019). Serum levels of both Nitrates and Nitrites were used to estimate the level of NO due to its very short half-life (Csonka et al., 2015). We chose the rat model since rats are more similar to human physiology that the mice model, making them better models for the studying pathological conditions in pre-clinical trials (Costa et al., 2019, Lu et al., 2019). Moreover, HSF diet induces hypertension rats; called spontaneously hypertensive rats (SHR) which are extensively used as an in vivo model for essential hypertension and cardiovascular disease (Plotnick et al., 2017). This SHR rat model has elevated basal myocardial NO content may be due to an increase in the protein-bound of dinitrosyl nonheme iron complexes which liberates the NO to the peripheral circulation that helps in treating the hypertensive state. Moreover, it has been previously published that SHR rats showed increased NO synthase activity III in the cardiac and aortic endothelium. Both of these two enzymes help in regulating vasoreactivity in the SHR rats (Plotnick et al., 2017). Our data go along with the previously published data for using antioxidant for treating nitrate tolerance and they gave similar clinical data (Csonka et al., 2015, Stewart et al., 2018, Lu et al., 2019, Meera et al., 2020). Meanwhile, the authors stressed the oxidative stress markers. Here, we studied a very important stress marker; nrf2, has a great role in the antioxidant metabolism pathway and has an important role against reactive oxygen species producing the cellular injury in the myocardium muscles (Bryda, 2013). This gives a molecular explanation of the previously published results and quantitatively determined the dose of l-ascorbic acid that can be used clinically. Moreover, we studied the activity of the pro-inflammatory mediator gene; NF-kB. Our results showed another mechanism for the protective effect of l-ascorbic acid against NO deleterious effect on the myocardium tissue. Besides, our data on the apoptosis marker; caspase-3, gives an explanation for the end result of the protective effect of l-ascorbic acid on the myocardium tissue. SOD, GSH, CAT and MDA are the first-line defense mechanisms against oxidative stress. They give an indication of the stressful conditions inside the cells (Lu et al., 2019). It has been previously reported a significant decrease in the activity of SOD, GSH and CAT, and a significant elevated level of plasma MDA (Plotnick et al., 2017). The suggested mechanism is that this may be an indirect effect of the significant increased NO plasma level or directly due to the significant elevated level of ROS and RNS. Meanwhile, the previous study (Plotnick et al., 2017) has not proved the tightly bound to that hypothesized subcellular event. In our study, we proved these suggestions by finding the dose-dependent response according to the plasma NO levels. Yet, l-ascorbic acid; a free radical scavenger, restored the change of these first-line defense mechanisms against oxidative stress. We measured the cellular levels of the pro-inflammatory cytokines marker (NF-κB), apoptosis markers (caspase-3) and mitochondrial oxidative stress marker (nrf2) in the myocardium muscles. Nrf2 is a suppressive mitochondrial oxidative stress marker. If this marker is increased, this indicates an increased oxidative stress condition inside the cell. We found that ISMN elevated the cellular activity of nrf2 in a concentration-dependent manner. Moreover, ISMN reduced the cellular activity of both the pro-inflammatory cytokines marker (NF-κB) and the apoptosis markers (caspase-3) in a dose-dependent manner. l-ascorbic acid succeeded to restore the cellular levels of the three markers in all tested concentrations of ISMN. We expected that the vasodilatory effect of ISMN will remain in chronic administration of ISMN due to the restoration of the endothelial function of the myocardium and no need to establish the NO free period. This nitrate free period for patients may cause an elevation of the risk of cardiovascular events during such nitrate-free periods, specifically in the early light phase. Reduction of the plasma hemoglobin level and increase in the level of both methemoglobin and carboxyhemoglobin may be the consequence of the liberated nitric oxide and nitrate ions during isosorbide mononitrate metabolism which can oxidize hemoglobin to methemoglobin and carboxyhemoglobin (Stewart et al., 2018, Bryda, 2013). Increased methemoglobin levels can cause congenital enzymatic defects, variation in hemoglobin molecule (Amdahl et al., 2019, Kottaisamy et al., 2021). Methemoglobin does not bind to oxygen efficiently causing a reduction in the oxygen-carrying capacity of the blood and a reduction in saturated oxygen and oxygen content this decrease is significant in high doses of nitrates (Rochon et al., 2020). l-ascorbic acid protects the blood from the oxidant effect of nitric oxide (antioxidant effect) and induces decreasing the levels of met-hemoglobin concentration (Meera et al., 2020, Ahluwalia et al., 2021). This reduction in methemoglobin levels induced by l-ascorbic acid concluded that erythrocyte alone had a negligible ability to reduce methemoglobin in the absence of exogenous ascorbate. Ascorbic acid preserves the Hb in a reduced ferrous redox state (Rochon et al., 2020, Ahluwalia et al., 2021). It has been previously reported that carboxyhemoglobin showed a significant increase with high doses of organic nitrates only (Leo et al., 2021). Our results showed that low doses of ISMN can significantly increase the plasma level of MetHb. This may be due to the impairment in the antioxidant enzymes defense system of the erythrocyte may cause an elevation of the abnormal hemoglobin derivatives as carboxyhemoglobin (Brunauer et al., 2016). An elevation in carboxyhemoglobin levels were noticed in all ISNN treated groups in a concentration-dependent manner. However, following l-ascorbic acid administration, a significant reduction of the carboxyhemoglobin was obtained in all treated groups. A reduction in hematocrit, RBC, and WBC may be a result of hemoglobin reduction and oxidative stress induced by nitric oxide (Brunauer et al., 2016, Tang et al., 2021). The present study showed a significant decrease in both serum iron level and serum TIBC in all ISMN treated groups in a concentration-dependent manner. This result can be explained by the increased serum nitric oxide by the increased ISMN dose and by the induced oxidative stress decreasing the serum iron and TIBC (Luo et al., 2021). It has been reported that iron deficiency anemia increases through NO production, and elevated NO concentrations in iron deficiency anemia. This effect can be reversed by iron supplementation to regain its normal levels (Tang et al., 2021). In this study, an improvement in the serum iron level and TIBC was observed by the effect of l-ascorbic acid. This improvement may be due to the beneficial effect of l-ascorbic acid to enhance iron absorption (Tang et al., 2021, Luo et al., 2021). In our study, all groups treated with ISMN showed a significant increase in serum nitric oxide concentrations. It has been previously recorded that l-ascorbic acid significantly decreases nitric oxide concentrations. l-ascorbic acid can decrease the accumulation of superoxide and peroxynitrite by directly scavenging superoxide (Leo et al., 2021). l-ascorbic acid safeguards against oxidative stress that induces pathological vasoconstriction and destruction of the endothelial barrier through blocking tetrahydrobiopterin oxidation, the cofactor of the endothelial nitric oxide synthase, thereby inhibiting endothelial nitric oxide depletion and endothelial nitric oxide synthase uncoupling. Ascorbate blocks inducible nitric oxide synthase preventing abundant production of NO (Morelli et al., 2020).
Our results explore the beneficial effects of l-ascorbic acid in nitrate therapy. Nitrates therapy for the treatment of cardiac problems induces elevated serum levels of lipid peroxidation, hemoglobin derivatives as methemoglobin and carboxyhemoglobin, rate of hemoglobin autoxidation, the cellular levels of the pro-inflammatory cytokines marker (NF-κB) and apoptosis markers (caspase-3) in the myocardium muscles in a dose-dependent manner. Meanwhile, Nitrates reduces the enzymatic effect of SOD, GSH and CAT accompanied by a decrease in the level of mitochondrial oxidative stress marker (nrf2) in the myocardium muscles and decreases both the serum iron and TIBC as well in a dose-dependent manner in the rat model. Concomitant treatment with a moderate dose of l-ascorbic acid significantly ameliorates these changes for all examined parameters. A moderate dose (20 mg/kg body weight in the rat model) of l-ascorbic acid supplementation could emerge as an important therapeutic strategy to prevent organic nitrate oxidative stress and can reduce nitrate tolerance. It remains to try to include a moderate dose of l-ascorbic acid in nitrate therapy for patients to study the nitrate tolerance and the cardiotoxic biomarkers.
All authors declare no potential conflicts of interest, including any financial, personal or other relationships with other people or organizations within that could inappropriately influence, or be perceived to influence, this work.
Ahmed Mohsen Elsaid Hamdan; in vivo studies, paper drafting and revising the article critically for important intellectual content. Zuhair M. Mohammedsaleh; supervised and monitored all aspects of this study from conception of the idea to submission of the manuscript, Aalaa Aboelnour; contributed her statistical analysis and data interpretation, Sherif M.H. Elkhannishy; contributed byt the intellectual ability to conception the project design, biochemical studies and laboratory analysis. All authors contributed equally to the final version of the manuscript.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649378 | Byung-Jun Sung,Sung-Bin Lim,Won-Mo Yang,Jae Hyeon Kim,Rohit N. Kulkarni,Young-Bum Kim,Moon-Kyu Lee | ROCK1 regulates insulin secretion from β-cells | 29-10-2022 | ROCK1,Insulin secretion,Beta cells,Hyperglycemia,Pyruvate kinase,Diabetes | Objective The endocrine pancreatic β-cells play a pivotal role in maintaining whole-body glucose homeostasis and its dysregulation is a consistent feature in all forms of diabetes. However, knowledge of intracellular regulators that modulate β-cell function remains incomplete. We investigated the physiological role of ROCK1 in the regulation of insulin secretion and glucose homeostasis. Methods Mice lacking ROCK1 in pancreatic β-cells (RIP-Cre; ROCK1loxP/loxP, β-ROCK1−/−) were studied. Glucose and insulin tolerance tests as well as glucose-stimulated insulin secretion (GSIS) were measured. An insulin secretion response to a direct glucose or pyruvate or pyruvate kinase (PK) activator stimulation in isolated islets from β-ROCK1−/− mice or β-cell lines with knockdown of ROCK1 was also evaluated. A proximity ligation assay was performed to determine the physical interactions between PK and ROCK1. Results Mice with a deficiency of ROCK1 in pancreatic β-cells exhibited significantly increased blood glucose levels and reduced serum insulin without changes in body weight. Interestingly, β-ROCK1−/− mice displayed a progressive impairment of glucose tolerance while maintaining insulin sensitivity mostly due to impaired GSIS. Consistently, GSIS markedly decreased in ROCK1-deficient islets and ROCK1 knockdown INS-1 cells. Concurrently, ROCK1 blockade led to a significant decrease in intracellular calcium and ATP levels and oxygen consumption rates in isolated islets and INS-1 cells. Treatment of ROCK1-deficient islets or ROCK1 knockdown β-cells either with pyruvate or a PK activator rescued the impaired GSIS. Mechanistically, we observed that glucose stimulation in β-cells greatly enhanced ROCK1 binding to PK. Conclusions Our findings demonstrate that β-cell ROCK1 is essential for glucose-stimulated insulin secretion and for glucose homeostasis and that ROCK1 acts as an upstream regulator of glycolytic pyruvate kinase signaling. | ROCK1 regulates insulin secretion from β-cells
The endocrine pancreatic β-cells play a pivotal role in maintaining whole-body glucose homeostasis and its dysregulation is a consistent feature in all forms of diabetes. However, knowledge of intracellular regulators that modulate β-cell function remains incomplete. We investigated the physiological role of ROCK1 in the regulation of insulin secretion and glucose homeostasis.
Mice lacking ROCK1 in pancreatic β-cells (RIP-Cre; ROCK1loxP/loxP, β-ROCK1−/−) were studied. Glucose and insulin tolerance tests as well as glucose-stimulated insulin secretion (GSIS) were measured. An insulin secretion response to a direct glucose or pyruvate or pyruvate kinase (PK) activator stimulation in isolated islets from β-ROCK1−/− mice or β-cell lines with knockdown of ROCK1 was also evaluated. A proximity ligation assay was performed to determine the physical interactions between PK and ROCK1.
Mice with a deficiency of ROCK1 in pancreatic β-cells exhibited significantly increased blood glucose levels and reduced serum insulin without changes in body weight. Interestingly, β-ROCK1−/− mice displayed a progressive impairment of glucose tolerance while maintaining insulin sensitivity mostly due to impaired GSIS. Consistently, GSIS markedly decreased in ROCK1-deficient islets and ROCK1 knockdown INS-1 cells. Concurrently, ROCK1 blockade led to a significant decrease in intracellular calcium and ATP levels and oxygen consumption rates in isolated islets and INS-1 cells. Treatment of ROCK1-deficient islets or ROCK1 knockdown β-cells either with pyruvate or a PK activator rescued the impaired GSIS. Mechanistically, we observed that glucose stimulation in β-cells greatly enhanced ROCK1 binding to PK.
Our findings demonstrate that β-cell ROCK1 is essential for glucose-stimulated insulin secretion and for glucose homeostasis and that ROCK1 acts as an upstream regulator of glycolytic pyruvate kinase signaling.
Diabetes is a rapidly growing health problem worldwide as evidenced by increasing morbidity and mortality and the economic burden on society [1,2]. Diabetes is characterized by hyperglycemia, peripheral insulin resistance, and impaired β-cell function [3,4]. Pancreatic β-cells directly contribute to the regulation of systemic glucose balance by releasing insulin primarily in response to glucose, although other nutrients such as fatty and amino acids can also enhance insulin secretion [5,6]. Impaired glucose-stimulated insulin secretion secondary to defects in signaling pathways has been reported as a contributing factor to the development of systemic glucose intolerance and overt diabetes [7,8]. Therefore, identifying molecular mediators that underlie the precise regulation of glucose-induced insulin secretion is of great significance. Glucose is the principal stimulator of insulin secretion from β-cells acting via the glycolytic pathway [9]. Pyruvate kinase (PK), an enzyme involved in the terminal step of glycolysis, catalyzes the transfer of a phosphate group and converts phosphoenolpyruvate and adenosine diphosphate (ADP) to produce pyruvate and adenosine triphosphate (ATP) [10]. Recent studies demonstrate that activation of PK promotes insulin secretion during glucose stimulation in INS-1 β-cells, mouse, and human islets [11]. Furthermore, PK activators can enhance insulin secretion from normal, high-fat diet-fed or Zucker diabetic fatty rats and diabetic humans [12], indicating the significance of PK in regulating β-cell secretory function. ROCK1 (Rho-kinase 1; Rho-associated coiled-coil containing kinase 1) is involved in the pathogenesis of metabolic-related diseases, including hypertension, arteriosclerosis, Alzheimer’s disease, and diabetes [[13], [14], [15]]. Emerging evidence shows that peripheral ROCK1 controls insulin-mediated glucose metabolism and insulin signaling, whereas brain ROCK1 plays a dominant role in regulating feeding behavior and body-weight homeostasis [[16], [17], [18], [19], [20], [21], [22], [23], [24]]. In the liver, ROCK1 is necessary for the development of diet-induced insulin resistance and hepatic steatosis in rodents and humans [25]. Although a study reported that chemical inhibition of ROCK promotes glucose-stimulated insulin secretion in primary pancreatic β-cells [26], the precise pathways and signaling proteins that mediate the effects are virtually unexplored. Furthermore, a lack of ROCK isoform selectivity and incomplete understanding of their respective specificities makes it difficult to interpret studies with inhibitors [[27], [28], [29]]. In the current study, we investigated the role of ROCK1 in directly regulating glucose metabolism by studying mice lacking ROCK1 in pancreatic β-cells in vivo and ROCK1-deficient islets ex vivo as well as cultured β-cell lines in vitro, with particular emphasis on glucose-stimulated insulin secretion and whole-body glucose homeostasis.
All animal care and experimental procedures were conducted in accordance with the National Institute of Health’s Guide for the Care and Use of Laboratory Animals and approved by the Institutional Animal Care and Use Committee of Samsung Medical Center (Seoul, Republic of Korea) and Beth Israel Deaconess Medical Center (Boston, MA). Mice were housed at 22–24 °C on a 12 h light–dark cycle and allowed ad libitum access to standard chow (PicoLab® Rodent Diet 5053, LabDiet, St. Louis, MO) and water.
Mice bearing a loxP-flanked ROCK1 allele (ROCK1loxP/loxP) were generated and maintained as previously described [22]. Mice lacking ROCK1 in pancreatic β-cells (β-ROCK1−/−, RIP-Cre; ROCK1loxP/loxP) were generated by breeding ROCK1loxP/loxP mice with RIP-Cre transgenic mice (The Jackson lab, Stock No: 003573). ROCK1loxP/loxP mice were used as controls. All mice were maintained on a mixed genetic background (129Sv and C57BL/6).
Mice were weighed weekly from 5 weeks of age onwards. For daily food intake, 14-week-old males were individually housed for 1 week prior to the start of food intake measurements. Subsequently, food intake was measured over a 7-day period. Blood was collected via the tail from either randomly fed or overnight-fasted mice. Blood glucose was measured using a glucose meter (Roche, Basel, Switzerland), serum insulin by ELISA (Mercodia, Uppsala, Sweden), and glucagon by ELISA (R&D Systems, Minneapolis, MN, USA).
For the oral glucose tolerance test (OGTT), 8, 20, and 48-week-old males were fasted for 6 h, and blood glucose was measured before and 30, 60, 90, and 120 min after administrating glucose by gavage (2 g/kg body weight). For the insulin tolerance test (ITT), 8, 20, and 48-week-old males were fasted for 6 h, and blood glucose was measured before and 15, 30, 60, 90, and 120 min after an intraperitoneal injection of human insulin (0.75 IU/kg body weight: Humulin R, Eli Lilly). Blood glucose was measured using a glucose meter (Roche). The area under the curve for glucose was calculated using the trapezoidal rule for OGTT [30].
Pancreas sections (5 μm thick) were immunostained for insulin (guinea pig anti-insulin pig from Abcam, 1:1000, overnight at 4 °C, secondary antibody; Alexa Fluor 647-conjugated anti-guinea pig from Thermo Fisher Scientific, 1:500, 1 h at room temperature) and nuclei were stained with DAPI (MilliporeSigma) in fluorescent mounting medium (Dako). The number of insulin+ β-cells were counted in a random manner by a single observer using a microscope (Olympus Corp.). Samples were blinded to the observer. At least 100 cells were counted per mouse. Insulin levels in islets were measured by ELISA (Mercodia) according to the manufacturer’s instructions.
Pancreases were rapidly dissected after intra-ductal injection from 9 to 10-week-old male mice and islets were isolated by digesting the pancreas with collagenase P (Roche, Basel, Switzerland) and purified using a Ficoll gradient (Merck Biochrom, Billerica, MA) [31]. After isolation, the islets were cultured for 24 h at 37 °C and size-matched islets were hand-picked using an inverted microscope under sterile conditions. Ten or thirty size-matched islets per individual experiment (per genotype) were used.
The isolated islets or β-cell lines (INS-1 and MIN6 cell) were preincubated in a HEPES-KRB buffer containing 3.3 mM glucose for 30 min at 37 °C and placed in a 0.2 μm syringe filter. The filter was connected to a peristaltic pump and the flow rate was adjusted to 2 or 5 ml/min. Fractions were serially collected at 5 min intervals for 30 min, 1 min intervals for 10 min, 2 min intervals for 50 min, and 5 min intervals for 30 min. Fractions were appropriately diluted and measured for insulin by ELISA (Mercodia).
The isolated islets or β-cell lines (INS-1 and MIN6 cell) were preincubated with a KRB buffer containing 3.3 mM glucose at 37 °C for 120 min and incubated with either 3.3 mM or 16.7 mM glucose in a KRB buffer at 37 °C for 60 min in the presence or absence of sodium pyruvate (10 mM, MilliporeSigma), TEPP-46 (10 μM, MilliporeSigma), or exendin-4 (20 nM, MilliporeSigma). Insulin levels in the media were determined by ELISA (Mercodia).
The INS-1 832/13 cells (gift from C. Newgard PhD, Duke University) were cultured in RPMI-1640 (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (FBS, Thermo Fisher Scientific) and 1% penicillin-streptomycin (P/S, Thermo Fisher Scientific) and HEPES. The MIN6 mouse insulinoma cells were cultured in DMEM supplemented with 15% FBS (Thermo Fisher Scientific) and 1% P/S. The MIN6 cells (Addexbio) were cultured in AddexBio advanced medium (Addexbio) supplemented with 15% FBS, 0.05 mM 2-mercaptoethanol (Thermo Fisher Scientific), and 1% P/S. The NIT1 mouse insulinoma cells (ATCC, American Type Culture Collection) were cultured in Ham’s F12K (ATCC) supplemented with 10% FBS and 1% P/S. The HEK293 human embryonic kidney cells were cultured in DMEM supplemented with 10% FBS and 1% P/S. The Chinese hamster ovary (CHO) cells were cultured in Ham’s F-12 (Thermo Fisher Scientific) supplemented with 10% FBS and 1% P/S. All cells were cultured at 37 °C in an atmosphere of 5% CO2.
INS-1 or MIN6 cells were transiently transfected with small interfering RNA (siRNA) using Lipofectamine 2000 (Thermo Fisher Scientific) according to the manufacturer’s instructions. Cells were used for studies 48 h after transfection. The siRNA sequence for ROCK1 (Bioneer, Daejeon, South Korea) was 5′-UCCAAGUCACAAGCAGACAAGGAUU-3′ as described [20]. Scramble siRNA was used as an experimental control (Bioneer). MIN6, HEK293, or CHO cells were transiently transfected with pAV-CAG-ROCK1 (Vigene Biosciences, Rockville, MD) and pEGFP-C1-PKM2 (Addgene, Watertown, MA) using Lipofectamine 3000 (Thermo Fisher Scientific) according to the manufacturer's instructions. The cells were used for studies 48 h after transfection.
The isolated islets or INS-1 cells were preincubated in 3.3 mM glucose and subsequently incubated in 16.7 mM glucose for 60 min at 37 °C. For measuring ATP levels, islets or cells were washed with PBS and the bioluminescence reaction was initiated by adding BacTiter-Glo™ reagent (Promega, Madison, WI) and maintained for 5 min at room temperature. Bioluminescence was determined by a GloMax Multi Microplate Multi Reader (Promega). For measuring calcium levels, the freshly isolated islets or cells were preincubated with 3.3 mM glucose for 120 min and incubated with 16.7 mM glucose treated with Fluo-4 direct calcium reagent solution containing 2.5 mM probenecid (F10472, Invitrogen) for 60 min at 37 °C. Fluorescence was measured at an excitation wavelength of 494 nm and emission wavelength at 516 nm by a GloMax Multi Microplate Multi Reader (Promega) [32].
The isolated islets or INS-1 cells were preincubated with 3.3 mM glucose for 120 min and incubated with 16.7 mM glucose for 60 min at 37 °C. The OCR was determined using the Seahorse Extracellular Flux (XF-96) analyzer (Seahorse Bioscience). The OCR were calculated by normalizing the protein content for the XF-96 measurement [33].
The isolated islets or INS-1 cells were preincubated with 3.3 mM glucose for 120 min and incubated with 16.7 mM glucose for 60 min. Pyruvate levels in islets and INS-1 cells were measured by the Pyruvate Assay Kit (abcam, #ab65342, Cambridge, United Kingdom) according to the manufacturer’s instructions. PK activity in INS-1 cells was measured by the Pyruvate Kinase Assay Kit (Abcam, #ab83432) according to the manufacturer’s instructions.
INS-1, MIN6, or NIT1 cell lines were treated with or without fasudil (10 μM) for 1 h and stimulated with high (16.7 mM) glucose for 15 min. Cells were incubated with primary antibodies against ROCK1 (1:100, Cat #: sc-17794, Santa Cruz Biotechnology, Dallas, TX, USA) and PK (1:100, Cat #: ab38237, Abcam) overnight at 4 °C. PLA was performed as described previously [34]. PLA was performed using the Duolink® In Situ Detection Reagents Red with Duolink® In Situ PLA probe anti-rabbit PLUS and anti-mouse MINUS (MilliporeSigma). The nuclei of cells were stained using Duolink® In Situ Mounting Medium with DAPI (MilliporeSigma). Images were captured by a fluorescence microscope (Leica DMi8, Leica) and analyzed by ImageJ (NIH).
The cell lysates (100 μg) were subjected to immunoprecipitation with 1 μg HA (Cat #: 3724, Cell Signaling Technology) or normal rabbit IgG (Cat #: 2729, Cell Signaling Technology) coupled to Dynabead protein A/G (Thermo Fisher Scientific) overnight at 4 °C. The immunoprecipitants were washed four times with lysis buffer, and then were eluted by 1X Novex™ Tris-Glycine SDS sample buffer (Thermo Fisher Scientific) with 1X NuPAGE™ Sample Reducing Agent (Thermo Fisher Scientific) for 10 min at 100 °C.
Tissue lysates (30 μg protein) or immunoprecipitated samples were resolved by SDS–PAGE and transferred to nitrocellulose membranes (Bio-Rad Laboratories, Hercules, CA). The membranes were incubated with antibodies against ROCK1 (Cat#: sc-17794, Santa Cruz Biotechnology), ROCK2 (Cat#: sc-5561, Santa Cruz Biotechnology), HA, or β-actin (Cat#: sc-130065, Santa Cruz Biotechnology). The membranes were washed and incubated with secondary antibodies (Cat#: SC-2004 or SC-2005, Santa Cruz Biotechnology). The bands were visualized with SuperSignal Chemiluminescent Substrates (Thermo Fisher Scientific).
Pancreases from ROCK1loxP/loxP and β-ROCK1−/− mice fed high glucose were fixed in 2% glutaraldehyde for 12 h. An electron microscopic analysis of docked granules was performed on the pancreas sections and quantitated as described previously [35].
Data are presented as means ± SEM and individual data points are plotted. Unpaired Student’s ‘t’ tests were used to compare two groups. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) was performed with post-hoc tests and Fisher’s PLSD tests. Repeated measures two-way ANOVA was performed for GTT, ITT, and GSIS. When intervention or interaction (intervention-by-time) was significant by repeated measures two-way ANOVA, post hoc analyses were performed using the SPSS program (SPSS version 18.0, SPSS, Inc., Chicago, IL) for multiple comparisons. All reported p values were two-sided unless otherwise described. Differences were considered significant at P < 0.05.
We confirmed that ROCK1 expression in pancreatic islets was decreased ∼65% in β-ROCK1−/− mice compared with ROCK1loxP/loxP (control) mice (Control vs. β-ROCK1−/− mice; 100 ± 5.9 vs. 34.9 ± 2.1%, n = 3), whereas its expression in hypothalamus and peripheral tissues was not different between genotypes (Supplementary Fig. 1). Furthermore, a lack of significant expression of ROCK2 in the islets suggested a lack of compensation between isoforms (Supplementary Fig. 1). Body weight (Figure 1A) and daily food intake and accumulated food intake (7 days) were similar between groups (Figure 1B and C). However, β-ROCK1−/− mice displayed hyperglycemia compared with control mice starting at 5 weeks of age, peaked at ∼12 weeks, and remained high throughout the 42 week period of the study (Figure 1D). Serum insulin levels in β-ROCK1−/− mice significantly decreased (∼54%) compared with ROCK1loxP/loxP mice, whereas serum glucagon levels were comparable between groups (Figure 1E and F). Importantly, the islet perifusion analysis showed a marked decrease in both the 1st and 2nd phases of insulin secretion in response to glucose stimulation in β-ROCK1−/− mice (Figure 1G, Supplementary Fig. 2A). The ex vivo statically incubated islet experiments further confirmed these observations (see Figure 1H). However, ROCK1 deletion in pancreatic β-cells had no significant effects on insulin content or β-cell numbers (Figure 1I and J). The proximity of insulin granules to the cell surface of β-cells is thought to especially impact the magnitude of the 1st phase insulin release [36,37]. Electron microscopy analysis showed the number of secreted granules close to the cell membrane in β-cells of β-ROCK1−/− mice reduced by 32% compared with β-cells in control mice (Figure 1K, left and right panels), indicating that ROCK1 activation is important in the docking process of insulin granules to the β-cell membrane. Collectively, these results suggest ROCK1 activation in pancreatic β-cells is involved in the regulation of insulin secretion in vivo.
β-ROCK1−/− mice showed impaired glucose tolerance by 8 weeks of age, as revealed by an increased area under the glucose curve during OGTT (Figure 2A). While the β-ROCK1−/− mice continued to exhibit glucose intolerance as they aged, the intolerance did not worsen (Figure 2B and C). This effect is most likely due to impaired glucose-stimulated insulin secretion and is independent of changes in body weight or insulin sensitivity (Figure 2D–F). Glucose tolerance measured by ipGTT was normal among RIP-Cre, RIP-WT, and ROCK1loxP/loxP mice at 10 weeks of age (Supplementary Fig. 3A). In addition, at 25 weeks of age, oral glucose tolerance was not different between RIP-Cre and ROCK1loxP/loxP mice (Supplementary Fig. 3B). Together, these data highlight the necessity of ROCK1 in regulating β-cell function without impacting insulin sensitivity.
We measured GSIS in INS-1 and MIN6 cell lines transfected with ROCK1 siRNA to further determine whether ROCK1 directly regulates insulin secretion in cultured β-cells. We confirmed that ROCK1 siRNA significantly reduced ROCK1 mRNA levels (Supplementary Fig. 2A). Consistent with the in vivo results, inhibition of ROCK1 significantly reduced both phases of insulin secretion measured during glucose perifusion in INS-1 cells (Figure 3A, Supplementary Fig. 3B) and MIN6 cells (Supplementary Fig. 4B). GSIS markedly decreased when ROCK1 expression was suppressed (Figure 3B, Supplementary Fig. 4C), whereas insulin content was relatively normal in the cell lines (Figure 3C, Supplementary Fig. 4D). These data, combined with the results of in vivo insulin secretion, demonstrate that ROCK1 activation is necessary to regulate glucose-stimulated insulin secretion.
We measured intracellular Ca++ levels, ATP levels, and OCR during glucose stimulateion (16.7 mM), which are critical events associated with insulin secretion in INS-1 cells, to further investigate the underlying mechanism(s) by which ROCK1 regulates insulin secretion. siRNA-mediated inhibition of ROCK1 resulted in a significant decrease in glucose-stimulated intracellular Ca++ levels (Figure 3D, Supplementary Fig. 5), ATP levels (Figure 3E), as well as OCR (Figure 3F). Similar findings were observed in islets freshly isolated from β-ROCK1−/− mice (Figure 3G–I). These data demonstrate that ROCK1 impacts the regulatory machinery involved in insulin secretion in β-cells.
We further determined whether pyruvate, a key intermediate of the glycolysis pathway, is involved in insulin secretion triggered by ROCK1. Pyruvate levels greatly decreased in both INS-1 cell transfected siRNA ROCK1 and islets from β-ROCK1−/− mice (Figure 4A and B). Glucose-stimulated PK activity was reduced ∼15% in ROCK1 knockdown INS-1 cells compared with control cells, while fructose-1,6-bisphosphate (FBP)-induced PK activity was similar between groups (Figure 4C). Pyruvate treatment enhanced the insulin secretion in response to glucose stimulation of INS-1 β-cells transfected with scrambled siRNA. While the insulin secretion was blunted in response to glucose stimualtion in cells with ROCK1 siRNA, the insulin secretion in response to pyruvate was maintained (Figure 4D). Similar observations were found in freshly isolated islets from β-ROCK1−/− mice (Figure 4F). Exogenous supplementaion with pyruvate increased insulin secretion 2.9-fold in INS-1 β-cells transfected with siRNA ROCK1 and 3.3-fold in islets from β-ROCK1−/− mice but only 1.3-fold in INS-1 β-cells transfected with scrambled siRNA and 1.5-fold in islets from control mice (Figure 4E and G). Similar to the effects of exogenous pyruvate, the PK activator, TEPP-46, also significantly increased GSIS in freshly isolated islets from control mice (Figure 4H). The effects of the PK activator on enhancing GSIS was evident even in the absence of ROCK1 in isolated islets (Figure 4H). Thus, the PK activator increased insulin secretion 4.8-fold in islets from β-ROCK1−/− mice but only 1.6-fold in islets from control mice (Figure 4I). Interestingly, glucagon-like peptide-1 (GLP-1) action was not involved in the regulation of ROCK1-mediated insulin secretion (Supplementary Fig. 6A and B). Together, these data suggest the effects of pyruvate and PK are independent of ROCK1-mediated insulin secretion in β-cells and they may play a role in regulating insulin secretion as an upstream of ROCK1.
We undertook PLA, a powerful technology to detect proteins with high specificity and sensitivity, to further test the hypothesis that ROCK1 binds to PK in response to glucose [38]. PLA revealed that each red spot represents a ROCK1-PK interaction complex in INS-1 β-cells. Glucose induced a physical interaction between ROCK1 and PK in INS-1 β-cells, as evidenced by a marked increase in the number of red spots. However, this effect was significantly impaired by treatment with the ROCK inhibitor fasudil (Figure 5A). Quantitative analysis indicated that glucose stimulation increased ROCK1-PK interactions by ∼2-fold over control, and this effect was restored to control levels when ROCK inhibitor was treated (Figure 5A). These results were further confirmed in MIN6 or NIT1 β-cells (Figure 5B and C). In addition, in vitro overexpression studies indicated that ROCK1 binds to PK in MIN6, HEK293, or CHO cells (Figure 5D). Together, these data suggest that PK physically interacts with ROCK1 in β-cells. The precise dynamic alterations between ROCK1 and PK in disease states warrants further investigation.
The ability of glucose to increase insulin secretion represents a major feature of β-cell function and its dysregulation is a key pathogenic feature in all forms of diabetes [3,4]. The current study was thus designed to determine the physiological role of ROCK1 in pancreatic β-cells in homeostatic control of insulin secretion in the context of glucose metabolism. Our data clearly suggest that ROCK1 is necessary for the regulation of insulin secretion in pancreatic β-cells during glucose stimulation. This effect is likely mediated through the physical interactions between ROCK1 and PK. Thus, we identify ROCK1 as an important regulator of β-cell metabolism that may lead to new treatment options for diabetes. A major finding of this study is that deletion of ROCK1 in pancreatic β-cells significantly decreases GSIS in vivo, which ultimately leads to systemic glucose intolerance. Studies in freshly isolated islets ex vivo and β-cells in vitro confirmed these data. Importantly, these effects are observed when insulin content or β-cell numbers are normal, suggesting that the marked reduction in insulin secretion due to ROCK1 deletion is directly linked to defects in the insulin secretory machinery rather than insulin synthesis and production. This is supported by the reduced number of insulin granules docked at the plasma membrane in β-cells from isolated islets from β-ROCK1−/− mice, which is associated with a decrease in the first phase of insulin release during glucose stimulation. Consistent with this view, previous reports link several proteins such as TRB3 [37], ABCA12 [39], and LKB1 [40] with modulating insulin granules and plasma membrane docking dynamics in the regulation of GSIS. The mechanism’s underlying insulin granules’ mobilization could involve in F-actin remodeling in response to glucose stimulation [41]. Given that ROCK regulates actin cytoskeleton reorganization [42,43], it is conceivable that ROCK1 deletion may inhibit F-actin remodeling to limit the access of insulin granules to the plasma membrane in β-cells. The glycolytic pathway in pancreatic β-cells involves a cascade of events that break down glucose into pyruvate, producing ATP and nicotinamide adenine dinucleotide (NADH) [44]. Activation of the glycolytic pathway could lead to a significant increase in insulin secretion from β-cells. As expected, exogenous supplementation of β-cells with pyruvate greatly promotes insulin release on a background of glucose stimulation. The fact that pyruvate’s ability to increase insulin secretion during glucose stimulation is enhanced in the absence of ROCK1 suggests that the ROCK1-mediated insulin secretory mechanism is linked to the actions of pyruvate. Given that pyruvate levels in islets of β-ROCK1−/− mice reduced, it is likely that PK, which converts phosphoenolpyruvate and ADP into pyruvate and ATP, is involved in this regulation. The significance of PK activation for the induction of insulin secretion has been recently documented [11,12]. For example, small molecule activators of PK potently amplify GSIS by switching mitochondria from oxidative phosphorylation to anaplerotic phosphoenolpyruvate biosynthesis [11]. Moreover, PK activation ameliorates GSIS in islets obtained from animals and humans, manifesting insulin resistance and type 2 diabetes [12]. In this context, we observed that treatment of ROCK1-deficient islets with the PK activator increased glucose-stimulated insulin secretion. Our data point to hypersensitization of β-cells to glucose by pyruvate or PK activator treatment in the absence of ROCK1, which leads to enhanced insulin release. Although the precise mechanism for this phenomenon remains to be elucidated, it is likely that ROCK1-deficient islets are pyruvate-sensitive. Taken together, these novel findings demonstrate that glycolytic PK signaling is linked to ROCK1 action in regulating insulin secretion. It is useful to note that primary mouse β-cells lack the pyruvate carrier, monocarboxylate transporters (MCTs) [45]. The rescue of impaired insulin secretion by pyruvate supplementation in ROCK1 deficient b-cells may not occur directly through β-cells and could be mediated via MCTs in α-cells [46] or yet un-identified pyruvate transporters in β-cells. Further investigation is warranted to clarify the pathways that mediate pyruvate-induced insulin section on β-cells and whether MCTs are involved in this event. Since phosphoenolpyruvate (PEP), an upstream metabolite of pyruvate, can be generated from pyruvate via PEP cycle in the mitochondria [47,48], it is likely that pyruvate regulates PK in the context of GSIS. Thus, pyruvate supplementation is not expected to fully rescue GSIS when ROCK1-dependent PK activation is impaired. In contrast to this expectation, we observed that pyruvate treatment completely restored impaired GSIS despite impaired PK activity secondary to ROCK1 inhibition pointing to a PK-independent mechanism. A plausible explanation for this observation is the involvement of a pyruvate–malate cycle that is functionally linked to insulin secretion [49]. Malate exported from the mitochondria to the cytosol is regenerated to pyruvate by cytosolic malic enzyme for cycling back to the mitochondria [48,49]. Thus, in our studies, a pyruvate–malate cycle plays a potentially dominant role over the traditional PEP cycle in the insulin secretory regulation when pyruvate is provided in β-cells lacking ROCK1. Our Our previous work demonstrated that global ROCK1-/mice have elevated serum insulin in the fasted and fed states owing to systemic insulin resistance. Increased insulin levels in global ROCK1−/− mice were also found during the glucose excursion of a glucose tolerance test previous work demonstrated that global ROCK1−/− mice exhibit elevated serum insulin owing to systemic insulin resistance [18]. It is likely the increased serum insulin in global ROCK1−/− mice is due to β-cell compensation for ambient insulin resistance. On the other hand, β-ROCK1−/− mice have lower serum insulin levels, which is due to impaired insulin secretion from β-cells. Thus, the metabolic action of ROCK1 is thought to be site- and context-dependent nature of regulation [23]. We propose that ROCK1 is a positive regulator of insulin secretion and developing small molecule for its activation may offer unexplored approaches to treating diabetes.
The study was designed by B.J.S., Y.B.K., and M.K.L. B.J.S., S.B.L., W.M.Y., and J.H.K. performed in vivo, ex vivo, and in vitro experiments and analyzed data. R.N.K. provided conceptual advice and contributed to the editing of the manuscript. M.K.L. and Y.B.K. wrote the manuscript with input from all other authors. M.K.L. and Y.B.K. are the senior and corresponding authors. |
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PMC9649379 | Jiyuan Chen,Yujie Wang,Lu Han,Rong Wang,Chunai Gong,Gang Yang,Zhe Li,Shen Gao,Yongfang Yuan | A ferroptosis-inducing biomimetic nanocomposite for the treatment of drug-resistant prostate cancer | 03-11-2022 | Enzalutamide-resistant prostate cancer,Polyunsaturated fatty acids,Ferroptosis,Dihomo-γ-linolenic acid,DECR1 siRNA1 | Second-generation androgen receptor (AR) inhibitors such as enzalutamide are the first-line treatments for castration-resistant prostate cancer (CRPC). Resistance to enzalutamide will greatly increase the difficulty of prostate cancer treatment and reduce the survival time of patients. However, drug-resistant cancer cells seem to be more sensitive to ferroptosis. Therefore, we constructed a biomimetic tumor-targeting magnetic lipid nanoparticle (t-ML) to codeliver dihomo-γ-linolenic acid (DGLA) and 2,4-dienoyl-CoA reductase 1 (DECR1) siRNA (t-ML@DGLA/siDECR1). DGLA is a dietary polyunsaturated fatty acid (PUFA), while DECR1 is overexpressed in prostate cancer and can inhibit the generation of PUFAs. The combination of DGLA and siDECR1 can efficiently induce ferroptosis by peroxidation of PUFAs, which has been verified both in vitro and in vivo. With the assistance of an external magnet, t-ML showed good tumor targeting ability and biocompatibility, and t-ML@DGLA/siDECR1 exhibited significant ferroptosis induction and tumor suppression capabilities. Moreover, in a nude mouse model of prostate cancer fed on a high-fat diet (HFD), there was no distant organ metastasis when the tumor-bearing mice were treated with t-ML@DGLA/siDECR1 and an external magnet, with upregulated PUFAs and downregulated monounsaturated fatty acids (MUFAs). Hence, this study has broadened the way of treating drug-resistant prostate cancer based on ferroptosis induction. | A ferroptosis-inducing biomimetic nanocomposite for the treatment of drug-resistant prostate cancer
Second-generation androgen receptor (AR) inhibitors such as enzalutamide are the first-line treatments for castration-resistant prostate cancer (CRPC). Resistance to enzalutamide will greatly increase the difficulty of prostate cancer treatment and reduce the survival time of patients. However, drug-resistant cancer cells seem to be more sensitive to ferroptosis. Therefore, we constructed a biomimetic tumor-targeting magnetic lipid nanoparticle (t-ML) to codeliver dihomo-γ-linolenic acid (DGLA) and 2,4-dienoyl-CoA reductase 1 (DECR1) siRNA (t-ML@DGLA/siDECR1). DGLA is a dietary polyunsaturated fatty acid (PUFA), while DECR1 is overexpressed in prostate cancer and can inhibit the generation of PUFAs. The combination of DGLA and siDECR1 can efficiently induce ferroptosis by peroxidation of PUFAs, which has been verified both in vitro and in vivo. With the assistance of an external magnet, t-ML showed good tumor targeting ability and biocompatibility, and t-ML@DGLA/siDECR1 exhibited significant ferroptosis induction and tumor suppression capabilities. Moreover, in a nude mouse model of prostate cancer fed on a high-fat diet (HFD), there was no distant organ metastasis when the tumor-bearing mice were treated with t-ML@DGLA/siDECR1 and an external magnet, with upregulated PUFAs and downregulated monounsaturated fatty acids (MUFAs). Hence, this study has broadened the way of treating drug-resistant prostate cancer based on ferroptosis induction.
Prostate cancer is the second most prevalent malignant tumor among men worldwide [1]. In the USA, the 5-year relative survival rate for all stages of prostate cancer is 98% due to the development of treatments and diagnosis methods. However, approximately 6–7% of patients will metastasize distantly and progress to metastatic castration-resistant prostate cancer (CRPC), with a sharp decline in 5-year relative survival rate (from 98% to 30%) [2]. To date, first-line drugs for metastatic CRPC, such as second-generation androgen receptor (AR) inhibitors enzalutamide (Enz) and abiraterone acetate have shown considerable effects. However, drug resistance and low responsiveness have limited the application of these drugs [3]. Recently, ferroptosis, a newly witnessed form of programmed cell death, is characterized by the accumulation of iron-dependent lethal lipid peroxides [4]. Fe2+/Fe3+ can react with peroxides and generate reactive oxygen species (ROS), which was called Fenton reaction. The accumulation of ROS leads to ferroptosis in cells. Cancer cells usually have higher iron demands and susceptibility to ferroptosis, and AR inhibitor-resistant prostate cancer cells are even more susceptible to ferroptosis [5]. AR inhibitors can reprogram the metabolic state of prostate cancer, leading to an accumulation of lipids to supply bioenergetic processes and cell proliferation. These increased lipids, especially polyunsaturated fatty acids (PUFAs), can enhance cell membrane fluidity and lipid peroxidation. Thus, AR inhibitor-resistant CRPC cells are highly sensitive to ferroptosis, suggesting that ferroptosis may have significant advantages for AR inhibitor-resistant CRPC therapy [6]. However, ferroptosis is a double-edged sword. Excessive ferroptosis may also lead to damage to body functions, such as exacerbating inflammatory bowel disease and cardiovascular disease [7,8]. Therefore, we need to precisely induce ferroptosis in CRPC tumor sites to avoid the toxic side effects of ferroptosis. It is reported that ingestion of dihomo-γ-linolenic acid (DGLA), a dietary PUFA, can induce ferroptosis in cancer cells [9]. In addition, the mitochondrial enzyme 2,4-dienoyl-CoA reductase 1 (DECR1) is overexpressed in CRPC,which is involved in the degradation of PUFAs. Knockdown or inhibition of DECR1 can increase PUFAs and decrease monounsaturated fatty acids (MUFAs) in prostate cancer cells and induce ferroptosis in prostate cancer by inhibiting GPX4, thereby suppressing the growth of cancer [10]. Herein, we report a biomimetic nanocomposite that codeliver DGLA and DECR1 siRNA (siDECR1) for the treatment of enzalutamide-resistant prostate cancer based on the regulation of PUFAs and ferroptosis (Scheme 1). To codeliver DGLA and siDECR1, magnetic lipid nanoparticles (ML) was prepared as a drug-loaded core (ML@DGLA/siDECR1). Lipid nanoparticles, composed of lipidoids, helper lipids, cholesterol and positive lipids, are usually used to deliver gene drugs such as siRNA and mRNA [11]. And obviously, lipid drugs such as DGLA can be easily loaded onto lipid nanoparticles due to the similarity solution principle. Moreover, Fe3O4 superparamagnetic magnetosomes could induce ferroptosis by increasing iron levels in cancer cells, with excellent magnetic targeting ability [12]. Here, the ML was composed of ethylenediamine-capped polyethyleneimine (en-PEI), 1,2-dioctadecanoyl-sn-glycero-3-phosphocholine (DSPC), cholesterol, 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy (polyethylene glycol)-2000] (DSPE-mPEG2k), and oleic acid-coated Fe3O4 nanoparticles (OA@Fe3O4), with a molar ratio of 52:10:32:2:4. However, if without a specific modification, lipid nanoparticles are cytotoxic and easily accumulate in the liver [13]. Cell membrane coating technology is an emerging bionic technology, and nanoparticles camouflaged with cancer cell membranes have a unique tumor targeting ability owing to inherent homotypic binding with good biocompatibility [14]. Therefore, to improve the active tumor targeting ability and safety of the nanoparticles, the prostate cancer cell membrane was extracted as a shell to camouflage the ML (t-ML). We demonstrated that the drug loaded biomimetic nanocomposite (t-ML@DGLA/siDECR1) can accurately target the tumor site, thereby inducing ferroptosis in drug-resistant prostate cancer by Fenton reaction and increased PUFAs. Moreover, administration of t-ML@DGLA/siDECR1 with an external magnet could significantly reduce distant organ metastasis in a mouse model of drug-resistant CRPC fed a high fat diet (HFD), showing considerable safety and significantly inhibited tumor growth.
The establishment of drug-resistant cell lines is a continuous and laborious process that usually takes at least 3 months [15], and the established enzalutamide-resistant C4–2B cell line (C4-2BEnz) was characterized after 6 months of coincubation with 10 μM enzalutamide. As shown in Fig. S1A, compared with enzalutamide-sensitive C4–2B cells, C4-2BEnz cells had fewer pseudopodia bulges, smaller cell volumes and denser organelles. C4-2BEnz cells presented a densely adherent growth mode and had a significantly faster growth rate than C4–2B cells. The CCK-8 experiment (Fig. S1B) showed that C4-2BEnz cells exhibited strong drug resistance with an IC50 value of 284.5 μg/mL, which was 178 times that of the C4–2B group. Moreover, the cell clone formation experiment further proved that the proliferation and cloning ability of drug-resistant cells was significantly enhanced (Fig. S1C). The established enzalutamide-resistant C4-2BEnz cell line was applied in subsequent experiments.
As shown in Fig. 1A and B and Fig. S2A, ML was loaded with OA@Fe3O4 and had a particle size of 92.49 ± 0.24 nm, with a near neutral zeta potential (−0.76 ± 0.46 mV). After camouflaging the prostate cancer cell membrane, the size of t-ML (96.37 ± 2.38 nm) was slightly higher, while the zeta potential of t-ML was −9.90 ± 0.80 mV, which was due to the negative potential of the C4–2B (−19.93 ± 0.50 mV) or C4-2BEnz cell membrane (−24.13 ± 1.12 mV). The morphology of OA@Fe3O4, ML, and t-ML could be observed more intuitively through transmission electron microscopy (TEM) images (Fig. 1C). OA@Fe3O4 nanoparticles were ∼10 nm spheroids or cubes. ML was an ∼100 nm spherical particle, with OA@Fe3O4 nanoparticles scattered in it. Relevently, t-ML was an ∼100 nm irregular spherical particle, and the inner OA@Fe3O4 nanoparticles and the surface prostate cancer cell membrane could be clearly witnessed. Meanwhile, t-ML fully inherited the membrane proteins of prostate cancer cell membranes (Fig. 1D). In addition, the stability of t-ML in PBS buffer, ultrapure water and fetal bovine serum (FBS) was evaluated at 4 °C. As shown in Fig. 1E and Fig. S3, t-ML had good stability, it was stable for 42 days in PBS or ultrapure water and 5 days in FBS. All these results indicated that t-ML was an irregular spherical nanoparticle with negative zeta potential, which was easily taken up by cells (∼100 nm) and had good biocompatibility (∼−10 mV) [16,17].
Owing to the rule of “like dissolves like” [18], t-ML had a good entrapment capacity for lipid drugs like DGLA, and the entrapment efficiency (EE) rate and drug loading (DL) rate of DGLA in t-ML were 87.14% ± 14.94% and 22.73% ± 3.90%, respectively (Fig. S2B). To evaluate the gene loading capacity of t-ML, a plasmid of enhanced green fluorescence protein (pEGFP) was used as a model drug. As shown in Fig. 1F, when the N/P ratio was ≥ 10, pEGFP could be completely compressed in t-ML, showing a strong gene binding ability. As shown in Fig. 1G and H, when the N/P ratio was 40, the gene transfection ability of t-ML was better than that of traditional cationic materials Lipofectamine 2000 (Lipo 2000) and en-PEI (P < 0.001). Therefore, in subsequent evaluation experiments, the N/P ratio of t-ML and gene drugs was set at 50. Moreover, the drug release of siRNA is acid-dependent, and its drug release rate was much faster in pH 5.5 (in endolysosomes) than in pH 7.4 (in blood), which may due to the proton sponge effect (Fig. S4) [19]. Additionally, after coating a film of prostate cancer cell membrane, the cytotoxicity of ML to HEK-293T cells was reduced, and the cell viability of the t-ML group was maintained at more than 70% at 600 μg/mL, which was much higher than that of the Lipo 2000 group and en-PEI group (P < 0.05) (Fig. 1I). Moreover, t-ML also had no toxicity to C4–2B or C4-2BEnz cells, and the cell survival rate was greater than 75% at a concentration of 1200 μg/mL for 24 h (Fig. S5). All these results indicated that t-ML had good safety and biocompatibility.
To obtain active tumor targeting ability, t-ML was endowed with superparamagnetic OA@Fe3O4 nanoparticles and homologous cancer cell membranes. Our previous study has demonstrated the homologous targeting capability of the prostate cancer cell membrane [20]. To investigate the tumor targeting ability of t-ML on prostate cancer cells, the fluorescent substance – Nile red (Nile) was used as a model drug. Moreover, 1.5 T external magnets were applied to evaluate the in vitro magnetic targeting ability of t-ML. As shown in Fig. 2A and Fig. S6, after camouflaging the cancer cell membrane with or without external magnets, the rate of positive cells in the t-ML@Nile groups was 2.2 times higher than that of the ML@Nile groups (P < 0.01). Moreover, after applying external magnets to the bottom of the cell plate for 2 h, the rate of positive cells in the t-ML group increased by 10.7% (P < 0.001). Moreover, the intracellular colocalization test was used to verify that t-ML could deliver drugs to the effect site (Fig. 2B). The fluorescence of Nile and siFAM in the t-ML@Nile/siFAM group overlapped around the nucleus. The fluorescence intensity of the t-ML@Nile/siFAM group was much stronger than that of the other groups, following the order of t-ML@Nile/siFAM > ML@Nile/siFAM > Nile/siFAM, which was in line with the results of cellular uptake study. Besides, due to the sensitivity and degradability of naked siRNA to ribozymes [21], there was no obvious FAM fluorescence in the Nile/siFAM group. To assess whether t-ML requires energy to enter cells and whether t-ML can deliver drugs into mitochondria for mitochondrial regulatiory effects, t-ML@Nile was coincubated with C4-2BEnz cells for 1–4 h, and the mitochondria were labeled with MitoTracker Green. After 1 h of coincubation, a small amount of Nile entered the mitochondria; while at 4 h, a large amount of Nile had entered the mitochondria, indicating that the entry of t-ML into cells was an active transport process (Fig. 2C). Moreover, endolysosome escape ability is critical for gene drug delivery systems to reduce the degradation of gene drugs in endolysosomes [22]. Therefore, lysosomes were labeled with LysoTracker Red to observe the intracellular biodistribution of t-ML@coumarin-6. As shown in Fig. 2D, after 1 h of coincubation, coumarin-6 had entered the cytoplasm, and its green fluorescence were overlapped well with the red fluorescence of lysosomes, emitting strong orange fluorescence around the nucleus in the merged image. However, after 4 h of administration, the green fluorescence of coumarin-6 was separated from the red fluorescence of lysosomes, suggesting an emancipation of coumarin-6 from lysosomes. All these results demonstrated the in vitro acitive tumor targeting ability and high drug delivery efficiency of t-ML, which could actively target prostate cancer and escape phagocytic degradation by the endolysosome system. In addition, t-ML could be localized in mitochondria for drug release, providing evidence for the regulation of mitochondrial lipid metabolism and ferroptosis.
As shown in Fig. 3A and C and Fig. S7A, for the DGLA groups, either in C4–2B or C4-2BEnz cells, the antiproliferation ability sequence from high to low was as follows: t-ML@DGLA/siDECR1, ML@DGLA/siDECR1, ML@DGLA, and DGLA. The half maximal inhibitory concentration (IC50) values of the DGLA group in C4–2B or C4-2BEnz cells were 27.36 μg/mL and 22.76 μg/mL, respectively. After loading in ML, the drug efficiency of DGLA was enhanced, and the IC50 values of ML@DGLA in C4–2B or C4-2BEnz cells were 1.52-fold or 1.45-fold lower than that in the DGLA group, respectively. Moreover, after synergy with siDECR1, the IC50 values were further reduced, and the IC50 values of the t-ML@DGLA/siDECR1 group on C4–2B and C4-2BEnz cells were 3.08-fold and 3.21-fold lower than those of the DGLA group, respectively. Correspondingly, for the siRNA groups, due to the instability of naked siRNA, the free NC siRNA and siDECR1 had almost no cytotoxicity (Fig. 3B,D and Fig. S7B). The IC50 values of the ML@siDECR1 group in C4–2B and C4-2BEnz cells were 9.158 nM and 7.279 nM, respectively. The IC50 values of the ML@DGLA/siDECR1 and t-ML@DGLA/siDECR1 groups were 3.2-fold and 5.1-fold lower than those of the ML@siDECR group, respectively. All these results showed a synergistic effect of DGLA and siDECR1. While DGLA and siDECR1 were coloaded in t-ML, the antiproliferation effect was significantly enhanced, which might be due to the tumor homotypic targeting ability of t-ML. In addition, it is worth noting that enzalutamide-resistant C4-2BEnz cells were more sensitive to DGLA and siDECR1 treatment than C4–2B cells. Thus, with increased iron demands, drug-resistant cancer cells are more sensitive to ferroptosis, and ferroptosis-inducing agents can enhance the effect of AR inhibitors in CRPC [[23], [24], [25], [26]]. The results of the antimigration and anti-invasion studies were basically in line with those of the antiproliferation studies. DGLA and siDECR1 showed a synergistic effect in t-ML@DGLA/siDECR1 and exhibited the strongest antimigration and anti-invasion abilities among all the groups (Fig. 3E–G). Consistent with a previous study [27], siDECR1 in vectors (ML, t-ML) showed significant antimigration and anti-invasion abilities (P < 0.0001). When C4-2BEnz cells were treated with DGLA alone, it had no antimigration effect but had a certain degree of anti-invasion ability. This might be due to the rapid growth and migration of drug-resistant cells, free DGLA did not have enough time to be taken up by the cells to exert its antimigration effect. After loading in ML, ML@DGLA exhibited considerable antimigration ability with increased cellular uptake of DGLA (P < 0.0001). Indeed, ML also had some antimetastatic effect, as the inner OA@Fe3O4 nanoparticles induced ferroptosis in the presence of increased iron supplements. Based on these factors, there were almost no migrating or invading cells in the t-ML@DGLA/siDECR1 group, indicating its strong antimigration and anti-invasion abilities.
Ferroptosis is characterized by the accumulation of ROS and lipid peroxidation [23]. Fe3O4 nanoparticle-based naniplatforms can release Fe2+/Fe3+ ions in the acidic tumor microenvironment to catalyze the Fenton reaction, leading to an increased level of ROS [28,29]. After 8 h of coincubation with C4-2BEnz cells, the generation of ROS was detected with the green fluorescence probe 2,7-dichlorofluorescin diacetate (DCFH-DA). In line with the antiproliferation study, there was almost no signal in the naked NC siRNA and siDECR1 groups. The free DGLA group exhibited considerable green fluorescence, and the fluorescence intensity was enhanced upon loading of DGLA in ML. Correspondingly, ML@siDECR1 induced a certain amount of ROS under the protection of ML. The fluorescence intensity of ML@DGLA/siDECR1 group was further enhanced, and the fluorescence intensity of t-ML@DGLA/siDECR1 group was the strongest (P < 0.05) (Fig. 4A, Fig. S8A). Moreover, mitochondria play a vital role in ferroptosis, and mitochondrial membrane potential (MMP) hyperpolarization is highly associated with ferroptosis [30]. Rhodamine 123 (Rho 123) is a probe for MMP, and its fluorescence intensity decreases with decreasing MMP, indicating damage to the mitochondrial membrane [31,32]. Compared with the NC siRNA group, the fluorescence intensity of ML@DGLA/siDECR1 and t-ML@DGLA/siDECR1 groups decreased sharply (P < 0.0001), indicating boosted MMP loss and ferroptosis in prostate cancer cells (Fig. 4B, Fig. S8B). Moreover, the intracellular concentration of Fe2+/Fe3+ ions was detected by an intracellular iron colorimetric assay kit. As shown in Fig. 4C,D and Fig. S9, in both C4–2B cells and C4-2BEnz cells, compared with the naked NC siRNA and siDECR1 groups, the Fe content in the t-ML and DGLA groups was slightly increased. The Fe content of the ML@DGLA and ML@siDECR1 groups was further increased due to the OA@Fe3O4 encapsulated by ML itself and its tumor-targeting ability, and the Fe content of the t-ML@DGLA/siDECR1 group was the highest (P < 0.01). Besides, a malondialdehyede (MDA) test showed that lipid peroxidation explosively increased about 3-fold in cells after administration of t-ML@DGLA/siDECR1 compared to control (Fig. 4E and F). Additionally, the mRNA and protein expression levels of DECR1 and GPX4 were significantly attenuated in cells treated with t-ML@DGLA/siDECR1 compared to control (Fig. 4G and H). All these results suggested that ferroptosis was strongly induced in prostate cancer cells, especially in enzalutamide-resistant prostate cancer cells.
In vivo animal experiments were performed according to Fig. 5A. To investigate the biodistribution behaviors of t-ML, the deep red fluorescent dye 1,1-dioctadecyl-3,3,3,3-tetramethylindotricarbocyaine iodide (DiR) was used as a model drug. As shown in Fig. 5B–D, comparing with the ML@DiR group, prostate cancer membrane camouflaged biomimetic lipid nanoparticles t-ML@DiR showed significant tumor targeting ability (P < 0.01), and less drug accumulation in liver (P < 0.001) and kidney (P < 0.05) sites. Moreover, with the help of an external magnetic field, the t-ML@DiR + M group showed strong tumor targeting ability. The drug was obviously accumulated in the tumor site with 4 h of in vivo injection, and the fluorescence intensity did not decline for 24 h. In contrast, the fluorescence intensity in the tumor site of the t-ML@DiR group was significantly reduced at 24 h. Therefore, the t-ML@DiR + M group exhibited a long-circulating effect, and the tumor targeting ability was significantly better than that of the t-ML@DiR group (P < 0.0001). It is worth mentioning that compared with the t-ML@DiR group, the accumulation of the t-ML@DiR + M group in the liver and kidney also increased correspondingly. This may be due to the fact that when the t-ML@DiR was guided by an external magnetic field to the tumor site, the nanoparticles passed through the liver and kidney, the flux of nanoparticles increased, and the amount of nanoparticles retained by the liver and kidney was relatively increased at the same time. In the pharmacodynamics study, all mice were fed an HFD. Tumors in HFD-fed mice grew rapidly, reaching 100 mm3 in 3–5 days. During the 16-day observation period, comparing with the rapidly increasing tumor volume in the saline group, the tumor growth rate in the other groups was relatively slow (Fig. 5E-L, S10). The slowdown of tumor growth in the t-ML group may be due to the fact that OA@Fe3O4 contained in t-ML has a certain ferroptosis-inducing effect based on the catalytic effect of the Fenton reaction. Although the free drug group (DGLA + siDECR1) showed a significant effect compared with the saline group (P < 0.05), its tumor volume continued to grow, reaching 1632.9 ± 421.5 mm3 on day 16. Although there was no statistical difference between the last three groups, the tumor volume in the t-ML@DGLA/siDECR1 + M group (228.0 ± 23.8 mm3) was 1.4-fold and 1.9-fold smaller than that in the t-ML@DGLA/siDECR1 (311.9 ± 93.9 mm3) and ML@DGLA/siDECR1 (436.9 ± 133.9 mm3) groups on day 16, respectively. Moreover, in the survival observation experiment, the survival time of the t-ML@DGLA/siDECR1 + M group was significantly longer than that of the saline group (P < 0.0001), and the median survival time was extended from 18 days to 30 days (Fig. 5M).
One of the main mechanism of ferroptosis is the peroxidation of unsaturated fatty acids, and interestingly, different types of unsaturated fatty acids have different effects on ferroptosis [23]. PUFAs can be peroxidized by the Fe2+/Fe3+ ion-catalyzed Fenton reaction. Correspondingly, endogenous MUFAs can inhibit ferroptosis through compensatory action, thereby maintaining a balance of PUFAs/MUFAs in the body [4]. However, in tumor tissue, the balance of PUFAs/MUFAs is broken, it has higher iron and lipid demands [4,23]. Therefore, PUFA supplementation and/or MUFA inhibition can inhibit tumor growth and metastasis by inducing ferroptosis. In the targeted lipidomics study, all the targeted lipids were analyzed by UPLC–MS/MS. 423 lipids were quantified in positive ion mode, and 251 lipids were quantified in negative ion mode. As shown in Fig. 6A and B, Fig. S11 and Supplementary Material 2,3, compared with the normal saline group, in the significant difference with |FC| > 1.5 and P < 0.05 as the screening criteria, the upregulated lipids in t-ML@DGLA/siDECR1 + M group were basically PUFAs. In addition, although there was no significant difference among the tested lipids, PUFAs in the tumor tissues of the t-ML@DGLA/siDECR1 + M group showed an upregulated trend, while MUFAs showed a downregulated trend, which was conducive to the process of ferroptosis (Fig. 6C and D and Supplementary Material 4). In line with the in vitro study, the mRNA and protein levels of DECR1 and GPX4 in tumor tissues of t-ML@DGLA/siDECR1+M group were significantly downregulated, suggesting that t-ML@DGLA/siDECR1 with an external magnetic field has efficient DECR1 knockdown and ferroptosis-inducing abilities (Fig. 6E–F).
As shown in Fig. 7A, tumors from HFD-fed mice exhibited a high degree of malignancy and metastatic capacity. All groups except t-ML@DGLA/siDECR1 + M had lung metastases. Meanwhile, DGLA alone and ML showed a certain degree of toxicity and inflammatory damage in liver and kidney. After camouflaging with prostate cancer membrane, no inflammatory damage was witnessed in major organs in both t-ML@DGLA/siDECR1 and t-ML@DGLA/siDECR1 + M groups. Correspondingly, from histological observation, the damage of tumor sites in each group was consistent with the results of the drug efficacy experiments. Except for the normal saline control group, the tumor sites in the other groups had a certain degree of ablation. The t-ML@DGLA/siDECR1 + M group had the strongest drug effect, most of the tumor cells were ablated, and there was almost no complete nuclear morphology. Drug treatment had no effect on weight gain of mice in each group, and the weight of mice in each group maintained a steady increase (P > 0.05) (Fig. 7B). Additionally, the free drug group, DGLA + siDECR1, showed an increase in all blood biochemical indiexes, and alanine aminotransferase (ALT) and creatinine (CR) were significantly higher than those in the saline group (P < 0.0001), suggesting its toxicity to the liver and kidneys. Accordingly, there was no significant difference between the saline and t-ML@DGLA/siDECR1 + M groups (Fig. 7C–F). These results indicated that t-ML@DGLA/siDECR1 with external magnets had good biocompatibility and safety, and can significantly inhibit tumor growth and distant organ metastasis.
Drug resistance and metastasis of prostate cancer present great obstacles to its treatment, and the malignant progression of prostate cancer is characterized by high levels of lipogenesis [33]. Aberrant lipogenesis has become a metabolic hallmark of prostate cancer, and lipid accumulation favors carbon and energy storage in prostate cancer, promoting its progression and metastasis [[34], [35], [36]]. Moreover, it has been reported that a high-fat diet fuels prostate cancer progression by reprogramming the metabolome, which may explain why the prevalence of prostate cancer in Western countries is much higher than that in Eastern countries [37,38]. More importantly, lipid metabolism is closely related to ferroptosis, as it was characterized by the accumulation of iron-dependent lethal lipid peroxides. It is worth noting that lipid free radicals generated by the iron-mediated Fenton reaction promote lipid peroxidation of cell membranes, thereby damaging cell membrannes and inducing ferroptosis [4]. Moreover, not all cell membrane lipids are susceptible to peroxidation. It can be devided into 3 types: saturated fatty acids, MUFAs, and PUFAs. Only PUFAs, especially those in phospholipids, appear to be susceptible to peroxidation. However, peroxidation of PUFAs by lipoxygenase is compenstad by MUFAs to protect cancer cells from ferroptosis [4,39]. Peroxidation of lipids is regulated by the glutathione peroxidase system, which is directly or indirectly regulated by different ferroptosis-related genes [40]. GPX4 is a vital factor in lipid homeostasis and ferroptosis, converts lipid hydroperoxides to lipid alcohols, and prevents the formation and accumulation of toxic lipid ROS in cancer cells [41]. However, the function of GPX4 may be disrupted by up-regulated PUFA levels, which lead to significant ferroptosis. Dietaty intake of DGLA increases levels of PUFAs and lipid peroxidation in prostate cancer and antagonizes the effects of ferroptosis inhibitors such as GPX4, Vitamin E, and ferrostatin. Instead, dietary and endogenous MUFAs act as a compensatory mechanism to suppress ferroptosis [9]. Moreover, deleption of DECR1, a mitochondrial enzyme involved in the degradation of PUFAs, increases PUFA levels and increases the sensitivity of prostate cancer cells to ferroptosis [10]. In addition, knockdown of DECR1 with siRNA can significantly reduce prostate cancer cell migration and invasion [27]. In the current study, we developed a biomimetic nanocomposite to codeliver DGLA and siDECR1 for the treatment of enzalutamide-resistant prostate cancer by regulating PUFAs and ferroptosis in prostate cancer. The biomimetic nanocomposite t-ML@DGLA/siDECR1 was developed and characterized, showing good biocompatibility, stability, dispersibility and excellent gene transfection ability (Fig. 1). After camouflaging the prostate cancer cell membrane, t-ML can be efficiently taken up by prostate cancer cells with the help of an external magnetic field, avoiding the phagocytosis and degradation of lysosomes due to endolysosomal escape, and the drugs can be localized and released in the cytoplasm and mitochondria, which is beneficial to DGLA and siDECR1 induces ferroptosis in prostate cancer cells (Fig. 2). Due to the superparamagnetic and homologous targeting effect of t-ML, t-ML exhibited excellent active tumor targeting under 1.5 T external magnetic field, which can reduce drug accumulation in liver, kidney, and lung regions (Fig. 2, Fig. 5). Moreover, both in vivo and in vitro studies demonstrated that t-ML@DGLA/siDECR1 + M had favorable antitumor effects, provided PUFAs and induced ferroptosis in drug-resistant prostate cancer by knocking down DECR1 and supplementing DGLA. With the upregulation of PUFAs and the downregulation of MUFAs, the sensitivity of prostate cancer to ferroptosis was enhanced, which can inhibit the growth and distant organ metastasis of prostate cancer and prolong the survival time. In addition, it was biocompatible and had no obvious damage to major organs (Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7).
In conclusion, the established biomimetic nanocomposite t-ML was stable and biocompatible with active tumor targeting and mild ferroptosis-inducing abilities. The drug-loaded t-ML@DGLA/siDECR1 with external magnets showed significant antitumor efficacy. Based on the regulation of PUFAs/MUFAs in HFD-fed drug-resistant prostate cancer-bearing nude mice, t-ML@DGLA/siDECR1 + M had a significant ferroptosis-inducing effect. In this mouse model of rapid tumor growth and metastasis, the tumor gowth of mice in the t-ML@DGLA/siDECR1 + M group was remarkably inhibited without distant organ metastasis. Therefore, this biomimetic nanocomposite provides a ferroptosis-based treatment for drug-resistant prostate cancer.
All the materials and methods are provided in Supplementary Material 1.
Jiyuan Chen: Conception and Design, Investigation, Methodology, Visualization, Writing-Reviewing and Editing; Yujie Wang: Conception and Design, Investigation, Writing-Reviewing and Editing; Lu Han: Conception and Design, Investigation, Methodology, Writing-Reviewing and Editing; Rong Wang: Conception and Design, Investigation, Methodology, Writing-Reviewing and Editing; Chunai Gong: Data Analysis, Supervision; Gang Yang: Data Analysis, Supervision; Ze Li: Data Analysis, Supervision; Shen Gao: Supervision; Yongfang Yuan: Conception and Design, Supervision, Writing-Reviewing and Editing. The authors declare that all data supporting the findings of this study are available within the article and its supplementary material files, or from the corresponding author on reasonable request.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649380 | Jie Tao,Shunyao Zhu,Xueyuan Liao,Yu Wang,Nazi Zhou,Zhan Li,Haoyuan Wan,Yaping Tang,Sen Yang,Ting Du,Yang Yang,Jinlin Song,Rui Liu | DLP-based bioprinting of void-forming hydrogels for enhanced stem-cell-mediated bone regeneration | 05-11-2022 | 3D bioprinting,Void-forming,Macroporous hydrogel,Bone marrow stem cells,Bone regeneration | The integration of 3D bioprinting and stem cells is of great promise in facilitating the reconstruction of cranial defects. However, the effectiveness of the scaffolds has been hampered by the limited cell behavior and functions. Herein, a therapeutic cell-laden hydrogel for bone regeneration is therefore developed through the design of a void-forming hydrogel. This hydrogel is prepared by digital light processing (DLP)-based bioprinting of the bone marrow stem cells (BMSCs) mixed with gelatin methacrylate (GelMA)/dextran emulsion. The 3D-bioprinted hydrogel can not only promote the proliferation, migration, and spreading of the encapsulated BMSCs, but also stimulate the YAP signal pathway, thus leading to the enhanced osteogenic differentiation of BMSCs. In addition, the in vivo therapeutic assessments reveal that the void-forming hydrogel shows great potential for BMSCs delivery and can significantly promote bone regeneration. These findings suggest that the unique 3D-bioprinted void-forming hydrogels are promising candidates for applications in bone regeneration. | DLP-based bioprinting of void-forming hydrogels for enhanced stem-cell-mediated bone regeneration
The integration of 3D bioprinting and stem cells is of great promise in facilitating the reconstruction of cranial defects. However, the effectiveness of the scaffolds has been hampered by the limited cell behavior and functions. Herein, a therapeutic cell-laden hydrogel for bone regeneration is therefore developed through the design of a void-forming hydrogel. This hydrogel is prepared by digital light processing (DLP)-based bioprinting of the bone marrow stem cells (BMSCs) mixed with gelatin methacrylate (GelMA)/dextran emulsion. The 3D-bioprinted hydrogel can not only promote the proliferation, migration, and spreading of the encapsulated BMSCs, but also stimulate the YAP signal pathway, thus leading to the enhanced osteogenic differentiation of BMSCs. In addition, the in vivo therapeutic assessments reveal that the void-forming hydrogel shows great potential for BMSCs delivery and can significantly promote bone regeneration. These findings suggest that the unique 3D-bioprinted void-forming hydrogels are promising candidates for applications in bone regeneration.
Bone defects, causing more than 1.5 million new grafts in the USA annually, remain the most pressing challenge in regenerative medicine [1]. Despite the capacity of the bone to rejuvenate itself, the regeneration potential is interrupted in the case of critical-sized defects and a tissue-engineered graft is generally required for bone tissue remolding. By far, the traditional treatment still cannot satisfy the increasing clinical demand for effective bone grafts due to the limited availability (in the case of autograft), risk of pathogen transmission (in the case of allograft), and reduced healing potential (in the case of biomaterial-based scaffolds) [2], which impel us to search for better alternatives. In the bone healing process, complex biological issues occur including inflammation response, new blood vessel formation, recruitment of osteogenic cells etc [3,4]. Research has demonstrated that a well-designed microenvironment that can regulate these issues is essential in bone regeneration [5,6]. In light of this, there is a great need to develop functional and bioactive tissue-engineered scaffolds that might bring new prospects to regulate the interaction between the implants and host tissues for the regeneration of bone defects. Bone marrow stem cells (BMSCs) that localize in the stromal compartment of the bone marrow are characterized by the abilities of self-renewal and differentiating into specialized cells, opening a promising avenue in regenerative medicine [7]. Over the past decades, a number of tissue engineering approaches combined with BMSCs have been used in clinical practice for the treatment of bone defects [8]. The installed stem cells can assist bone tissue regeneration through differentiation into osteoblasts, recruitment of other therapeutic cells, or building a favorable microenvironment via the release of paracrine factors [9,10]. However, direct delivery of cell suspension to the sites of bone fracture generally brings a disappointed outcome because of the low survival rate, short retention, and restricted functions. Recently, there increased an attention to the creation of hydrogel constructs containing stem cells for the treatment of bone defects, including silk fibroin, alginate, and GelMA [11]. GelMA, which is functionalized with photosensitive groups on gelatin backbone, not only shows favorable abilities in facilitating the adhesion and proliferation of BMSCs [12], but also owns tunable mechanical properties by photopolymerization. Meanwhile, the microenvironment constructed by GelMA hydrogel favors the osteogenesis of encapsulated BMSCs [13,14], showing potential for bone regeneration. However, GelMA hydrogels that retain structurally stable generally have a high crosslinked degree and dense polymer network, leading to insufficient nutrient diffusion and restricted cell migration/proliferation [15]. As reported by the previous studies [16,17], hydrogels with relaxation properties could promote the viability and functions of the encapsulated stem cells, leading to enhanced efficiency. However, the stress relaxation relied on the breakage of the hydrogel network, leading to disappointing structural integrity and mechanical properties, which is not an ideal biophysical element in bone regeneration. 3D bioprinting technologies have attracted much attention in fabricating high-performance bone tissue constructs [18,19]. They enable a high level of spatiotemporal modulation of the interactions between cell-cell and cell-extracellular matrix (ECM) to produce structurally sophisticated and functionally relevant tissue constructs. To date, extrusion-based bioprinting technology has been the most widely used for preparing bone tissue constructs owing to its advantages of widespread availability, cost-effectiveness, simplicity, and facile processing [20]. However, due to the interfacial artifacts between the printing lines and the serial writing fashion, the structural integrity and fabrication speed of the bone tissue constructs are compromised by the extrusion-based bioprinting technology, restricting their clinical applications. By comparison, constructs fabricated by digital light processing (DLP)-based 3D bioprinting platform are characterized by superior speed, resolution, and structural integration [21]. Zhang et al. developed a haversian bone-mimicking construct to foster the formation of new bone and blood vessels by DLP-based printing technology [22], demonstrating the potential of using this advanced 3D printing technology to build bone tissue constructs. Here, we investigate the potential of a void-forming hydrogel to regulate the behavior and functions of the encapsulated BMSCs for bone tissue regeneration via a DLP-based 3D bioprinting platform (Fig. 1). We hypothesize that bioprinted void-forming hydrogels can not only offer porous structure for cell spreading, but also enable a cell-inspired microenvironment for preserving and enhancing the functions of encapsulated stem cells. The efficacy of the construct was examined to repair a 6 mm cranial defect. This construct could promote the formation of new bone tissues, contributing to clinical applications.
Gelatin and methacrylic anhydride were purchased from Sigma Aldrich. Dextran (average Mw = 500, 000) was purchased from J&K Scientific. Cell Counting Kit-8 (CCK-8) was purchased from MCE. Live/Dead assay kit was purchased from KeyGEN BioTECH. Alexa Fluor™ 488 Phalloidin and 4’,6-diamidino-2-phenylindole (DAPI) were purchased from Thermo Fisher. Gelatin methacryloyl (GelMA) and lithium phenyl-2,4,6-trimethyl-benzoylphosphinate (LAP) were synthesized according to previous study [23]. Primary antibodies (anti-OCN and anti-COL-1) were bought from Affinity Biosciences and anti-YAP were bought from Cell Signaling Technology.
Bone mesenchymal stem cells (BMSCs) used for fabricating cellular hydrogels were isolated from adult rat. The method for the isolation of BMSCs was according to the previous study. The isolated BMSCs were cultured in DMEM/F12 (Gibco) supplemented with 10% (v/v) fetal bovine serum (FBS, Gibco) and 1% (v/v) penicillin/streptomycin (BI). All the cells were cultured into a humidified incubator with 5% CO2 atmosphere at 37 °C.
The void-forming hydrogels were prepared by DLP-based 3D printing of aqueous emulsion composed of the mixture of dextran solution and GelMA solution with a volume ratio of 1:2 according to our previous study [24]. Briefly, the dextran and GelMA polymers were dissolved into PBS solution to form 10% (w/v) dextran solution (average Mw = 500, 000; without modification) and 15% (w/v) GelMA solution at room temperature, separately. Then, 0.5% (w/v) of photo-initiator (LAP) was respectively added the solution before construction by DLP-based 3D printing using 405 nm visible light (60 mW/cm2). Pure GelMA hydrogel was settled as the control group. To observe the pore size and distribution within 3D-printed hydrogels in their hydrated state, rhodamine-labeled GelMA was used. 15% (w/v) of rhodamine labeled GelMA solution was gently mixed with 10% (w/v) of dextran solution. And the hydrogel preparation procedure was the same as previous. The hydrated morphology of the hydrogels was assessed by laser confocal microscopy (Leica SP8). The micromorphology of the 3D-printed hydrogels was observed by a scanning electron microscopy (SEM, SU8020, Hitachi). The printed hydrogels were treated with gradual dehydration in ethanol (30%, 45%, 60%, 75%, 90%, 100%), critical point drying, and Pt/C-shadowing before we visualized the hydrogels. Compression properties was measured using mechanical testing machine at room temperature (ElectroForce, TA). The permeability of the printed hydrogels was evaluated as following. A cylinder-shaped hydrogel containing FITC-labeled BSA was prepared using DLP-based 3D printer and incubated into culture medium. At predesigned timepoint, the hydrogels were imaged by florescent microscopy. To evaluate the degradation properties of the printed hydrogel, the obtained samples were washed with PBS for 24 h (37 °C) to remove the photoinitiator and uncrosslinked polymers. The degradation of the printed hydrogels was imaged followed immersed in collagenase solution. The degradation of the hydrogel was calculated as a ratio of the loss area to the primary area.
The collected BMSCs were resuspended into GelMA solution (15%, w/v) and mixed with dextran solution (10%, w/v) at a volume ratio of 2:1. The prepared bioink was constructed into designer hydrogels via DLP-based 3D printing technology (Movie S1). The bioprinter was settled in the clean bench and was sterilized using UV light for 30 min before bioprinting. The printing parameters were used as follows: printing speed, 0.1 mm/s; UV light intensity, 60 mW/cm2. The entire hydrogels measured around 6 mm in diameter and 1 mm in thickness. After washed with PBS for three times, the 3D-printed cellular hydrogels were cultured into a humidified incubator with 5% CO2 atmosphere at 37 °C. Supplementary data related to this article can be found at https://doi.org/10.1016/j.mtbio.2022.100487. The following is the supplementary data related to this article:Multimedia component 1Multimedia component 1 The viability of the encapsulated BMSCs was measured by live/dead assay and CCK-8 assay according to the manufacturer's instructions. To observe the spreading of the encapsulated BMSCs within the void-forming hydrogels, the cultured hydrogels were washed with PBS for several times and fixed by 4% paraformaldehyde. The cytoskeleton and nucleus were stained with Alexa 488-phalloidin and DAPI, separately. The cell spreading was visualized using laser confocal microscopy. Immunofluorescent staining was performed on encapsulated BMSCs with the 3D-bioprinted hydrogels after 7 days of culture. The primary antibody against YAP (1:100) was utilized. To assess the effect of porous structure on osteogenic differentiation, the printed hydrogels were osteoin-duced in osteogenic medium (Cyagen, China). After 7 d and 14 d of osteoinduction, quantitative real-time PCR (qRT-PCR) was employed to evaluate the mRNA expression of the encapsulated BMSCs. Total cellular RNA was extracted of the cells using Trizol and reverse-transcribed into cDNA using All-In-One 5X RT MasterMix (Applied Biological Materials). Assays were performed using BlasTaqTM 2X qPCR Master Mix (Bio-Rad, Thermo scientific). The primers are listed in Table 1.
The migration performance of encapsulated BMSCs was tested by Transwell assays. Transwell assays were evaluated in 12-well plate Transwell with a diameter of 12 mm and a pore size of 8 μm. The cellular hydrogels were placed in the upper chambers. After 5 and 10 days, the migrated cells were fixed with 4% paraformaldehyde for 10 min, and the cells remaining on the top of the Transwell membranes were removed. The migrated cells were stained with crystal violet and imaged with inverted microscope. The alkaline phosphatase (ALP) activity of the encapsulated BMSCs within the 3D-printed hydrogels was measured after incubation for 7 and 14 days. The osteogenic differentiation medium with osteoinductive factors was refreshed every two days. At prescribed time, the 3D-printed cellular hydrogels were washed with PBS and lysed with collagenase and RIPA lysis buffer. The ALP activity was measured using Alkaline Phosphatase Assay Kit at the wavelength of 405 nm. The total protein level was determined by BCA Protein Assay Kit. The study was performed in triplicate. Meanwhile, the qRT-PCR was employed to analysis the osteogenic expression. The primers are listed in Table 1.
All animals were purchased from the laboratory Animal Center of Army Medical University. Rat were acclimatized to the environment of the animal facility for at least seven days prior to the experiments. The animal protocol used in this study was carried out in accordance with the guidelines for the care and use of laboratory animals published by the ministry of health of the People's Republic of China and was approved by the Institutional Animal Care and Use Committee of the Army Medical University (AMUWEC20211834). To evaluate the efficacy of cellular hydrogels, full-thickness craniotomy defects (6 mm diameter) were created in the parietal bone of rats (200–220 g, male). The rats were randomly divided into three groups: Void-forming group (n = 6), Standard group (n = 6) and Control group (n = 6). Each of group of rats was anesthetized with intraperitoneal injection of chloral hydrate solution (0.3 mL/100 g). For rats in the control group, the created defects had no treatment. The cranial defect treated with 3D-printed BMSCs-laden void-forming hydrogels or 3D-printed BMSCs-laden standard hydrogels was regarded as void-forming group or standard group. After 8 weeks, the rats were sacrificed, and the harvested samples were fixed with 4% paraformaldehyde for over 24 h.
The fixed samples were scanned with micro-CT (PerkinElmer). Three-dimensional reconstruction was performed using the Sky Scan CtAn softeware and the newly formed bone area was measured by Image J software.
After micro-CT scanning, the samples were decalcified for further histological evaluations. The decalcified samples were dehydrated through an ascending graded series of ethanol solutions and cleared with xylene. Then the samples were embedded in paraffin and sectioned at a thickness of 5 μm for histological evaluations. Hematoxylin & eosin (H&E), Masson trichrome and immunohistochemistry (COL-1, 1:100 and OCN, 1:100) staining were carried out via standard protocols and imaged using an optical microscope.
All statistical analysis was conducted using Graphpad Prim 9 (Graphpad Software Inc.). The significance was measured using one-way ANOVA or two-way ANOVA. Results were displayed as means with standard deviation (SD) and p < 0.05 was considered as statistically significant. For all the tests, data from at least three independent samples or experiment repeated thrice were used.
To address the limitations of current BMSCs-based biomaterials in the treatment of critical bone defects, the present study, therefore, developed a void-forming hydrogel by 3D-bioprinting of a mixture of an emulsion solution and BMSCs. The aqueous emulsion was consisted of two immiscible phases (GelMA and dextran), in which dextran microdroplets dispersed within GelMA solution (Fig. S1, Supporting Information). The emulsion was polymerized and constructed using a DLP-based bioprinter (Fig. 2A). A visible light (405 nm) was used to induce the polymerization of the emulsion. The printing process was realized by continuously projecting the digital images and lifting the polymerized hydrogels. After fabrication, the dextran phase could be removed via dissolving into water or incubation medium, leaving pores within the GelMA hydrogels. The 3D-printed hydrogels were visualized with laser confocal microscope. The pure GelMA solution used for preparing the standard hydrogel was treated as the control group. Rhodamine B-labeled GelMA and FITC-labeled dextran polymers were used to image the hydrate morphology of the hydrogels. As shown in Fig. 2B–i, the hydrogel emitted red fluorescence represented GelMA polymer networks, while dark areas indicated the formed pores, implying the successful preparation of 3D-printed porous hydrogels. The void-forming hydrogels and standard hydrogels were further critical-point drying and characterized with scanning electron microscope (SEM, Fig. 2B–ii). The standard hydrogels had a smooth surface, and no pores were found on the hydrogels. As we hypothesized, porous structure displayed across all the void-forming hydrogels in consistent with fluorescent images. The above results indicated that the dextran phases could be removed to leave pores within the GelMA hydrogels. The effect of the formed pores on the mechanical properties was using a mechanical analyzer in the unconfined compression mode. The porous structure led to the decreased strain and stress of the hydrogels, compared to the standard hydrogels (Fig. 2C; Fig. S2). Meanwhile, the void-forming hydrogel degraded faster than standard GelMA hydrogel in the presence of collagenase (Fig. S3). Next, the release test of the void-forming hydrogels was evaluated by mixing the FITC-labeled BSA with the emulsion or pure GelMA solution. As presented in Fig. 2D, comparing to the standard hydrogels, the FITC-labeled BSA could diffuse faster from the void-forming hydrogels, indicating that these hydrogels promoted the exchange of substance between the encapsulated cells and extracellular microenvironment. As high cell viability is a premise for successful bioprinting of constructs in bone regeneration, live/dead assay and CCK-8 are performed to investigate the effects of pore formation on encapsulated cell viability and distribution within the void-forming hydrogels. A live/dead fluorescent staining post-printing revealed that both 3D-printed hydrogels had no toxicity on the encapsulated cells within 5 days (Fig. 3A). Meanwhile, it revealed that aggregated cells were rarely observed in both groups, demonstrating the good biodistribution of the encapsulated BMSCs in the printed hydrogels. Higher OD value indicates more cells. As shown in Fig. 3B, the proliferation of BMSCs was promoted within the void-forming hydrogels, while the standard hydrogels restricted the proliferation of encapsulated BMSCs and there was a slight decrease in cell number. To further demonstrate the advantages of the void-forming hydrogels on encapsulated stem cells, a migration assay was employed by gelling the cell-laden emulsion in the upper chamber of a transwell. As shown in Fig. 3C (Fig. 3D), the generated pores within the printed hydrogels caused more cells to migrate through the insert membrane, comparing to the standard hydrogels. These observations suggested that the void-forming hydrogels had the potential as the matrix for encapsulating BMSCs in bone tissue engineering. Furthermore, there was considerable variation in spreading morphology for encapsulated BMSCs (Fig. 4A). Specifically, the area of spreading BMSCs was higher in void-forming hydrogels than that in standard hydrogels (Fig. S4, Supporting Information). BMSCs within the void-forming hydrogels exhibited extended spreading morphology. In contrast, the BMSCs remained rounded in the standard hydrogels. To investigate how cells respond to the void-forming hydrogel, nuclear localization of YAP transcriptional regulator was studied. The YAP transcriptional regulator was thought to play a vital role in controlling the cell fate in response to mechanical or geometric cues [25,26]. Nuclear localization of YAP was previously found to promote the cytoskeletal organization, paracrine regulation, and osteogenic differentiation of MSCs cultured on PLCL-nHA composite porous scaffolds [27]. The results presented in Fig. 4B displayed that the void-forming hydrogels could significantly promote YAP expression than that in standard hydrogels. In addition, there was more YAP expressed in nuclear in void-forming hydrogels, leading to the up-regulation of the YAP targeted genes CTGF, CYR61, and CDH2 (Fig. 4C), while YAP mainly remained in the cytoplasm in standard hydrogels. These results indicated that hydrogels in situ forming pores had an impact on transcriptional factor activity. Based on the finding of promoted migration, proliferation, and cell spreading of encapsulated BMSCs within void-forming hydrogels, we next investigated the effect of pores on the osteogenic differentiation of encapsulated BMSCs. Alkaline phosphatase (ALP) plays a vital role during the early stage of osteogenesis. As shown in Fig. 4D, the expression of ALP was increased within 14 days in all groups and void-forming further enhanced ALP activity, which was significantly higher than that measured in standard hydrogels. We also analyzed the gene expression of osteogenic markers (OSX and RUNX2). For OSX, there presented a significant increase within the space of two weeks (Fig. 4E). At day 7, we found the OSX expression had no significant difference between the two groups. At day 14, the average OSX expression level was approximately 4-fold higher for void-forming hydrogels compared with the standard hydrogels. RUNX2 is the earliest and most specific marker for bone formation. Compared with standard hydrogels, the encapsulated BMSCs within void-forming hydrogels exhibited approximately 3-fold higher and 1.5-fold higher expression of RUNX2 at day 7 and 14, respectively (Fig. 4F). Our results demonstrated that directly 3D-printing of BMSCs-laden void-forming hydrogels had the potential in boosting bone regeneration. To evaluate the efficacy of our void-forming hydrogels, we used these hydrogels to repair a rat cranial defect in vivo. Sprague–Dawley rats with cranial defects were randomly divided into three groups: void-forming, standard and control group. After 8 weeks post-implantation, the samples were harvested and fixed by 4% paraformaldehyde for radiological and histological evaluation. Fig. 5A displayed the micro-CT scanning images of the regenerated bone tissues. We found that newly generated bone tissues in the original cylindrical defects were observed. The generated bone tissues almost filled the defects in the void-forming group, while only a small amount of new bone tissues was observed in other two groups. In the quantitative micro-CT analysis (Fig. 5B), the defects treated with void-forming hydrogels and standard hydrogels were covered by newly formed bone at 67% and 45%, respectively, while the defects left empty showed a minimal healing (26%, control group). Moreover, the value of newly formed bone area in void-forming group was significantly higher than that in the standard group, demonstrating that void-forming hydrogels were more efficient at facilitating new bone tissue regeneration. Moreover, the newly formed bone tissues were performed with histological staining (Hematoxylin &eosin and Masson's trichrome) to support the radiographic findings. As observed by H&E staining (Fig. 5C), the defect treated with void-forming hydrogel was occupied with newly formed bone, and thick tissue and bone-like tissue bridged the gaps. Meanwhile, void-forming group presented more newly formed bone tissues in the defects than standard group. In contrast, the defects in the control group were connected with fibrous inflammatory tissue. Masson trichrome staining images (red indicates calcified bone) displayed that the defect in the void-forming group was composed with blue and red stained osteoid islands (Fig. 5D), suggesting that the newly formed bone tissue gradually calcified and matured. In the standard group, the defect only filled with fibrous soft tissue with minimal bone formation. In addition, the formed pores within 3D-printed hydrogels that affected the newly formed bone tissues were also identified with immunohistochemical staining for osteogenic markers: collagen 1 (COL-1) and osteocalcin (OCN). According to the immunohistochemical results (Fig. 6), more osteogenic markers (COL-1 and OCN) were found in the void-forming group than other groups. Taken together, these results indicated that the directly 3D printing of porous hydrogels could facilitate BMSCs in repairing cranial defects.
Bone tissue engineering has merged as a potent approach for the treatment of cranial defects to overcome the limitations of autografts and allografts, such as multiple surgeries, high risk of contamination, and lack of available donor sites [28]. This approach exploits a combination of cells, biomaterials, and growth factors to build biochemical and biophysical cues for rebuilding the lost bone tissues [29,30]. Meanwhile, inspired by the off-the-shelf availability, non-immunogenicity, and stability after in vitro expansion, BMSCs are currently the most promising cellular source to combine with other biomaterials for enhanced therapeutic index [31]. The implanted BMSCs can contribute to the fracture healing process via both cellular and paracrine effects, while biomaterials offer an optimized 3D space to preserve the functionalities of the contained BMSCs and provide physiological regulatory capacities for enhanced therapeutic efficacy. Nonetheless, traditional fabrication techniques generally can offer bone tissue constructs with simple architectures and hardly replicate microscale units of the natural bone tissues. And cells are commonly loaded after the establishment of constructs, leading to limited control over distribution. Recently, 3D printing of hydrogels for BMSCs delivery has received much attention in bone regeneration, in which 3D printing technology displays feasibility in patterning cells and biomaterials at high resolution for the creation of customized structure similar to bone tissues [32,33]. However, the therapeutic efficacy of the encapsulated stem cells can be dramatically inhibited by the inefficient substance exchange with external microenvironment and mechanical compression by the dense chemical-crosslinking polymers when directly 3D bioprinting of cell-laden hydrogels. Thus, a cell-inspired platform for enhancing the BMSCs behaviors and functions was appealing for bone regeneration. 3D printing of porous hydrogels containing BMSCs would be an ideal route of administration to the target sites for speeding up the bone regeneration. The contact between the structured porous hydrogels and surrounding tissues can significantly promote tissue ingrowth, angiogenesis, and interface fusion [24,34]. Although a number of porous hydrogels have been reported via salt-leaching [35], organic phases that served as porogens [36], and cryogelation [37], which are not ideal to be applied in 3D direct bioprinting of cell-laden porous hydrogels, as it was not possible to encapsulate living cells within polymer solution during the biofabrication process. In addition, cell seeding that was applied after the porous hydrogel establishment would lead to nonuniform distribution. Recently, the group of Zhang reported a cryobioprinting strategy to fabricate cell-laden porous hydrogel constructs [38], which used a freezing plate with controlled temperature. During the biofabrication process, the ice crystals were formed to serve as the porogens. And DMSO and melezitose must be added into the bioink to maintain the cell viability. To address these limitations, we developed a void-forming hydrogel using a feasible and effective approach for direct preparation of cell-laden porous hydrogels via a DLP-based bioprinting platform. The prepared bone tissue constructs with enhanced cell viability can be realized by rapidly projecting digital images with blue light. After establishment of the cellular hydrogels, the dextran polymer will be dissolved into the incubation media, leading to pore formation within the hydrogel constructs. The formed porous structure endowed the hydrogels with a favorable microenvironment for promoting the proliferation, migration, and spreading of the printed BMSCs. To promote bone tissue regeneration, a cell-inspired microenvironment is needed for the regulation of encapsulated stem cells. Previous studies reported that YAP, a mechanosensitive transcriptional activator, shows a crucial role in regulating angiogenesis [39], immunomodulation [40], and tissue regeneration [41]. A study revealed that fast relaxing hydrogel with decreased mechanical properties was found to promote hMSCs spreading and YAP nuclear localization [16]. Recently, Lian et al. demonstrated that YAP staining appeared mainly in the nuclear portion when the BMSCs were seeded on the porous hydroxyapatite nanoparticles functionalized PLCL scaffolds [27]. In contrast, they observed significantly weakened YAP expression in BMSCs cultured on dense scaffolds. However, Ehlinger et al. highlighted the insensitivity of YAP translocation when dental pulp stem cells (DPSCs) were cultured on different substrate rigidity (from 1.5 KPa to 2.5 MPa) [42]. While the pore formation decreased strain and stress of the hydrogels in this study. Therefore, we hypothesized that whether our fabricated porous structure within the hydrogel had an effect on YAP signal pathway. In vitro testing revealed that YAP expression was significantly higher in encapsulated BMSCs within void-forming hydrogels than that in the standard hydrogels. Meanwhile, the void-forming hydrogels displayed capacities in facilitating YAP nuclear translocation. Although some research investigated that the porous hydrogels could facilitate cell spreading and proliferation of the directly encapsulated BMSCs, few disclosed the underlying molecular mechanisms [43]. Our studies provided an alternative approach to design 3D-bioprinted hydrogels for bone tissue engineering. On top of that, the up-regulation of YAP nuclear translocation enhanced ALP activity and gene expression of osteogenic markers when BMSCs cultured in osteogenic differentiation media, showing great potential in bone regeneration. However, the matrix elasticity has a significant regulation for bone regeneration. Nathaniel et al. reported that porous alginate hydrogel with an intermediate elastic modulus presented optimal bone regeneration [43]. Therefore, the elastic modulus of our void-forming hydrogel should be further optimized to match the mechanical environment. The efficacy of the prepared hydrogels was evaluated in repairing the full-thickness craniotomy defects (6 mm in diameter). Our results displayed that, under the present experimental conditions, the skull was successfully reconstructed after treatment with BMSCs loaded void-forming hydrogels. After 2 months, histological analysis and immunofluorescence staining were performed to compare bone regeneration through the void-forming hydrogels with that supported by the standard hydrogels. After injury to the craniofacial bone, micro-CT contributes significant information in the evaluation of bone regeneration. The results indicated that the BMSCs loaded void-forming hydrogels realized a higher bone cover areas in cranial defect model of rats. Similarly, a large amount of newborn bone lacunas was observed to completely occupy the defects area and integrate tightly with the host bone tissues in the histoligical evulations. Meanwhile, higher osteo-related protein expression (OCN and COL-1) was confirmed in the void-forming group at 2 months. To conclude, this study presented a feasible and effective platform for 3D bioprinting of void-forming hydrogels in promoting skull reconstruction, paving the way for the next generation of BMSCs functionalized hydrogels.
A void-forming hydrogel was prepared by 3D bioprinting of the BMSCs mixed with an aqueous emulsion. The 3D-bioprinted porous hydrogel could significantly enhance the cell spreading, migration, and proliferation of the encapsulated BMSCs. More importantly, the niche created by the porous structure forced the YAP nuclear localization and facilitated the up-regulation of YAP targeted genes. In vivo testing revealed that the generated pores significantly promoted the 3D-bioprinted hydrogels in skull repair. The proposed strategy might represent a potential clinical alternative for skull regeneration, which also be expected to inspire the 3D bioprinting of functional biomaterials for tissue repair.
J.T., J.S., and R.L. designed the experiments. J.T., S.Z., Y.Y., N.Z., and Y.T. performed the experiments. J.T., Y.W., Z.L., X.L., H.W., and S.Y. helped to analyze the results. J.T. wrote the manuscript. R.L. and J.S. supervised the project.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649383 | Annette Brandt,Anja Baumann,Angélica Hernández-Arriaga,Finn Jung,Anika Nier,Raphaela Staltner,Dragana Rajcic,Christian Schmeer,Otto W. Witte,Barbara Wessner,Bernhard Franzke,Karl-Heinz Wagner,Amélia Camarinha-Silva,Ina Bergheim | Impairments of intestinal arginine and NO metabolisms trigger aging-associated intestinal barrier dysfunction and ‘inflammaging' | 31-10-2022 | Aging,Endotoxin,Nitric oxide,Intestinal permeability,Microbiota | Aging is considered a state of low grade inflammation, occurring in the absence of any overt infection often referred to as ‘inflammaging'. Maintaining intestinal homeostasis may be a target to extend a healthier status in older adults. Here, we report that even in healthy older men low grade bacterial endotoxemia is prevalent. In addition, employing multiple mouse models, we also show that while intestinal microbiota composition changes significantly during aging, fecal microbiota transplantation to old mice does not protect against aging-associated intestinal barrier dysfunction in small intestine. Rather, intestinal NO homeostasis and arginine metabolism mediated through arginase and NO synthesis is altered in small intestine of aging mice. Treatment with the arginase inhibitor norNOHA prevented aging-associated intestinal barrier dysfunction, low grade endotoxemia and delayed the onset of senescence in peripheral tissue e.g., liver. Intestinal arginine and NO metabolisms could be a target in the prevention of aging-associated intestinal barrier dysfunction and subsequently decline and ‘inflammaging'. | Impairments of intestinal arginine and NO metabolisms trigger aging-associated intestinal barrier dysfunction and ‘inflammaging'
Aging is considered a state of low grade inflammation, occurring in the absence of any overt infection often referred to as ‘inflammaging'. Maintaining intestinal homeostasis may be a target to extend a healthier status in older adults. Here, we report that even in healthy older men low grade bacterial endotoxemia is prevalent. In addition, employing multiple mouse models, we also show that while intestinal microbiota composition changes significantly during aging, fecal microbiota transplantation to old mice does not protect against aging-associated intestinal barrier dysfunction in small intestine. Rather, intestinal NO homeostasis and arginine metabolism mediated through arginase and NO synthesis is altered in small intestine of aging mice. Treatment with the arginase inhibitor norNOHA prevented aging-associated intestinal barrier dysfunction, low grade endotoxemia and delayed the onset of senescence in peripheral tissue e.g., liver. Intestinal arginine and NO metabolisms could be a target in the prevention of aging-associated intestinal barrier dysfunction and subsequently decline and ‘inflammaging'.
3-NT 3-nitrotyrosine AB antibiotics Arg arginase Bmi1 B lymphoma Mo-MLV insertion region 1 homolog CD14 cluster of differentiation 14 DFMO difluoromethylornithine EDTA ethylenediaminetetraacetic acid FMT fecal microbiome transfer iNOS inducible nitric oxide synthase KRH Krebs-Henseleit-bicarbonate LBP lipopolysaccharide binding protein Lrig1 leucine-rich repeats and immunoglobulin-like domains protein 1 mo months MOPS 3-(N-morpholino) propanesulfonic acid Muc2 mucin 2 MyD88 myeloid differentiation primary response 88 NO nitric oxide Lgr5 leucine-rich repeat-containing G-protein coupled receptor 5 norNOHA N(ω)-hydroxy-nor- l -arginine OTU operative taxonomic unit PAI-1 plasminogen activator inhibitor-1 PERMANOVA Permutational analysis of variance SDS sodium dodecyl sulfate SIMPER Similarity percentages analysis Tert telomerase reverse transcriptase TLR toll-like receptor TNFα tumor necrosis factor alpha ZO-1 zonula occludens-1
Results of the Global Burden of Disease Study suggest that world-wide life expectancy at birth rose by 6.3 years from 67.2 years in 2000 to 73.5 years in 2019 and that this trend will sustain [1]. Studies also suggest that the expected healthy life span will not increase at the same rate but rather that time spend unhealthy will be expanded [1]. Despite intense research efforts throughout the last decades, mechanisms underlying aging-associated decline and also their diversity are not yet fully understood. In recent years, evidences from animal and human studies accumulated suggesting that intestinal homeostasis, and especially, mucosal integrity may be major factors for health and well-being (for overview see Refs. [2,3]). Indeed, together with a stable microbiota the mucus layer acts as first line of defense against external injuries (for overview see Refs. [3,4]), regulates entry and digestion of food-derived nutrients and forms and shapes the development of the immune system (for overview see Refs. [5,6]). While it has been suggested that the gross architecture of the intestinal epithelium in the small and large intestine is not markedly affected by aging [7,8], studies in rodents and non-human primates suggest that the number of goblet cells increases while expression of α-defensines, lysozyme and F4/80 mRNA expression, as well as NOx levels and protein concentration of tight junction protein decreased with increasing age [[9], [10], [11], [12]]. These alterations were found to go along with an increase in intestinal permeability [10,13,14] and bacterial endotoxin levels [12,14,15]. Results of several studies further suggest that these aging-associated alterations are linked to changes of intestinal microbiota in older animals and humans [9,11,12,[16], [17], [18]]. It has been suggested that aging-associated intestinal microbiota dysbiosis and the increase of systemic tumor necrosis factor alpha (TNFα) levels found in old mice may be critical in aging-related intestinal barrier dysfunction [19]. However, whether and how intestinal microbiota or other alterations at the level of the gut affect intestinal barrier function in aging is not yet fully understood. Starting from this background, we aimed to determine (1) whether changes in markers of intestinal integrity are also found in healthy older individuals and (2) molecular mechanisms possibly involved in the aging-associated changes in intestinal integrity.
Fasting plasma samples from 16 young, healthy, male adults (ages 23–33 years) and 16 healthy male older subjects (≥75 years of age) collected in the due course of screening visits for nutritional intervention studies in our department were analyzed. All studies were approved by the respective local ethics committees of the University of Vienna, Austria and were performed in accordance with the ethical standards laid down in the Helsinki Declaration of 1975 as revised in 1983. Studies are registered at http://www.clinicaltrials.gov (NCT01775111, NCT03482284, NCT04847193 and NCT04341818). Written informed consent was obtained from all subjects before the study. None of the subjects included in this analysis suffered from any of the following diseases: metabolic diseases e.g., cardiovascular diseases, type 2 diabetes, and non-alcoholic fatty liver disease, chronic inflammatory diseases, malignant diseases or took medication to control any of these diseases or were considered obese (BMI >20.0 to <30.0 kg/m2).
All procedures in mice were approved by local authorities and animals were handled in accordance with the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes (Thüringer Landesamt, Bad Langensalza, Germany and BMBWF-66.006/0014-V/3b/2019, BMBWF-66.006/0013-V/3b/2018, Vienna, Austria). Trial 1: To determine markers of intestinal permeability and to analyze intestinal microbiota, tissue and blood from portal vein was obtained from 3 and 24 months old C57BL/6J male mice anesthetized with nitrous oxide housed in a pathogen-free animal facility (Jena University Hospital) under standardized conditions. Tissues were either snap-frozen immediately or were fixed in neutral-buffered formalin for the histological staining. Blood was spun and plasma was obtained and frozen until further use. Trial 2: To determine the effects of fecal microbiome transfer (FMT) on aging-associated intestinal barrier decline, feces were collected from 3 months old male C57BL/6J mice and 17 months old male C57BL/6J mice housed in groups in a specific pathogen-free animal facility at the University of Vienna under standardized conditions. The latter mice showed signs of intestinal barrier dysfunction e.g., significantly higher bacterial endotoxin levels in peripheral blood obtained from the vena fascialis and increased markers of senescence e.g., higher p16 protein levels in blood (see Fig. 3). Prior to the FMT treatment, naïve 17 months old mice were treated with an antibiotic mixture (polymyxin B (92 mg/kg BW) and neomycin (216 mg/kg BW)) in drinking water for three days. Mice were then randomly assigned to two groups treated with fecal microbiota from 1) young (o + yFMT) or 2) from old mice (o + oFMT), by oral gavage three times weekly for the following six weeks. Feces used for FMT were collected and stored as previously described [20] and microbiota composition was analyzed as detailed below. Trial 3: To determine the role of NO synthesis and arginase activity in aging-associated decline of intestinal barrier function, old male C57BL/6J mice (age 17 months) showing signs of intestinal barrier dysfunction and old age e.g., elevated bacterial endotoxin levels and markers of senescence in blood were either treated with the arginase inhibitor N(ω)-hydroxy-nor-l-arginine (norNOHA) (Bachem, Switzerland, 10 mg/kg BW, i.p.) or vehicle (NaCl, i.p.) three times weekly for six weeks. Mice were kept in groups in a specific pathogen-free animal facility at the University of Vienna under standardized conditions. At the end of trials 2 and 3 animals were anesthetized with ketamine (100 mg/kg BW) and xylazine (16 mg/kg BW), and after blood was drawn from the portal vein, mice were sacrificed, and tissue was collected.
Everted gut sacs were built of rinsed small intestinal tissue as previously described in detail [21]. In brief, after everting and filling the tissue sacs with 1x Krebs-Henseleit-bicarbonate buffer with 0.2% BSA (KRH), tissue sacs were incubated in gassed KRH buffer (95% O2/5% CO2) at 37 °C supplemented with xylose (0.1% (w/v) for either 5 min to determine intestinal permeability or supplemented for 55 min with 1 μM of arginase inhibitor norNOHA or 20 μM ROCK-inhibitor Y-27632 or 1 mM difluoromethylornithine (DFMO). The latter experiments were followed by a 5 min incubation with additional xylose (0.1% (w/v)) to measure xylose permeation. Xylose concentration was measured using a modified protocol for measuring xylose in urine and serum samples based on phloroglucinol as previously published [21,22], and expressed as μmol/cm. Concentrations of Y-27632, norNOHA and DFMO were determined in pilot studies. The remaining tissue was snap-frozen.
Limulus amebocyte lysate assay (Charles River, France) was used for detecting endotoxin concentration in portal and peripheral plasma of mice and in plasma of patients, as described previously [23]. In addition, commercially available SEAP reporter HEK293 cells assays (Invivogen, USA) were used to determine total TLR2 and TLR4 ligands in human as well as mouse plasma as detailed previously [24].
Lipopolysaccharide binding protein (LBP) and plasminogen activator inhibitor-1 (PAI-1) protein levels were measured in peripheral plasma of humans and plasma from portal vein of mice using commercially available ELISAs (Abnova, Taiwan and LOXO, Germany), respectively, as detailed by the manufacturer. Arginase activity in proximal small intestine was measured, as previously described [21].
Paraffin-embedded intestinal tissue sections (4 μm) from proximal and distal small intestine and colon were stained with Alcian Blue and periodic acid-Schiff staining, as previously described by others [25]. Number of cells/100 μm villus was assessed in proximal and distal small intestine as well as colon using a microscope with an integrated camera (DFC 450 C Leica, Germany).
Paraffin-embedded intestinal tissue sections (4 μm) were stained for 3-NT, occludin and ZO-1 as well as Arg-1 and -2 as previously described [21,26,27]. In brief, sections were incubated with specific primary antibodies (3-NT: Santa Cruz Biotechnology, USA; occludin and ZO-1: Invitrogen, USA; arginase-1 and arginase-2: Cell Signaling, USA) following peroxidase-linked secondary antibody and diamino-benzidine to determine specific binding. The extent of occludin and ZO-1 staining was defined as the percentage of microscopic field within the default color range capturing eight pictures per sample using a microscope with an integrated camera (Leica DM6B, Leica DMC4500, Leica, Germany). 3-NT-positive cells were counted per mm villus in eight randomly selected microscopic fields per sample.
Total RNA was extracted from snap-frozen liver, proximal and distal small intestinal as well as colonic tissue using peqGOLD TriFast (Peqlab, Germany) and reverse transcribed as described previously [28]. mRNA expression of genes listed in Supplemental Table 1 were determined by real-time RT-PCR as described previously [29].
To detect Arg-1, Arg-2, pROCK2, ROCK2, p16 (CDKN2A) and cluster of differentiation 14 (CD14) by Western blot, protein was either isolated from snap-frozen small intestine using RIPA buffer (20 mM 3-(N-morpholino) propanesulfonic acid (MOPS), 150 mM NaCl, 1 mM ethylenediaminetetraacetic acid (EDTA), 1% Nonidet P-40 and 0.1% sodium dodecyl sulfate (SDS)) containing protease and phosphatase inhibitor cocktails (Sigma-Aldrich, Germany) or plasma samples were used which were diluted with loading buffer (0.3 M Tris, 10% SDS, 50% glycerol, 0.05% bromphenol blue, 20% β-mercaptoethanol). Samples (10–30 μg protein/lane) were separated in a SDS-polyacrylamide gel and transferred to an Immun-Blot®-polyvinylidene difluoride membrane (Bio-Rad, USA). Resulting membranes were incubated with specific primary antibodies (Arg-1, Arg-2: Cell Signaling, USA; ROCK2, pROCK2: GeneTex, USA; CD14: Santa Cruz, USA; p16 (CDKN2A): biorbyt, UK) and corresponding secondary antibodies. Protein bands were detected using a luminol-based enhanced chemiluminiscence HRP substrate (Super SignalWest Dura kit, Thermo Fisher Scientific, USA) and analyzed densitometrically (ChemiDoc MP System, Bio-Rad, USA). Band intensities of proteins determined in tissue were normalized to β-actin protein bands while those determined in plasma samples were normalized to ponceau staining as detailed before [26].
Nitrite levels in scraped mucosa and small intestinal tissue were determined using a commercially available Griess reagent kit according to the instructions of the manufacturer (Promega, USA) and as detailed before [21]. In brief, tissue samples were homogenized in PBS, centrifuged and Griess reagent was added to the resulting supernatant.
Total DNA was extracted from small intestinal content of mice using the FastDNA™ SPIN Kit for soil from MP Biomedicals (Solon, OH, USA), following the manufacturer's instructions. Illumina library preparation was performed according to Hernandez-Arriaga et al. [30] targeting the V1-2 region of the 16S rRNA gene. Samples were sequenced using 250 bp paired-end sequencing chemistry on an Illumina MiSeq platform. Raw reads were quality filtered, assembled and aligned using Mothur pipeline [31]. A total of 24,890 ± 13,365 raw reads were obtained per sample. UCHIME was used to find possible chimeras and reads were clustered at 97% identity. Only operative taxonomic units (OTUs) present on average abundance higher than 0.0001% and with a sequence length higher than 250 bp were considered for further analysis. The closest representative was manually identified with seqmatch from RDP as described previously [32]. Sequences were submitted to European Nucleotide Archive under the accession number PRJEB52291.
All values are shown as means ± sem. Outliers were identified using Grubb's test. Differences between two groups were determined using an unpaired two-tailed students t-test, after testing for normal distribution, while for three groups a one-way ANOVA was performed (Graph Pad Prism Version 7.0, USA). Differences were considered statistically significant when p < 0.05. Regarding Illumina amplicon sequencing data, a sample similarity matrix was created using Bray-Curtis coefficient [33] and further explored with Principal Coordinates Analysis (PCoA) [34]. Permutational analysis of variance (PERMANOVA) was used to evaluate statistical differences between different groups. Similarity percentages analysis (SIMPER) was used to identify the OTUs that contribute to the differences between groups (PRIMER-E, version 7.0.9, Plymouth Marine Laboratory, Plymouth, UK) [35]. Differences in the abundance of OTUs of interest between age groups were evaluated using the non-parametric Wilcoxon test [36]. Visualizations were created using R version 4.1.0 with R Studio and Calypso [37,38].
While not showing any signs of frailty or suffering from any overt diseases and only being overweight but not obese (Supplemental Table 2), bacterial endotoxin and TLR2 ligand levels in the peripheral blood of male older adults were all significantly higher than in young male adults (Fig. 1). LBP and soluble CD14 protein levels in plasma of older men were also higher than in young men.
In line with the findings in humans and despite being fed the same diet, male old mice were significantly heavier and markers of senescence [[39], [40], [41]], e.g. PAI-1 protein levels in plasma and p16 mRNA expression of hepatic tissue (Table 1) were significantly higher than in young animals. Portal bacterial endotoxin concentration was significantly higher in 24 months old mice than in young animals (+2.2-fold, Fig. 2) and was associated with an increased xylose permeation in the proximal small intestine but not in colon. While morphology and number of goblet cells were similar in proximal and distal small intestine as well as in colon in old and young mice (Supplemental Fig. 1, Supplemental Table 3), protein and mRNA expression levels of the tight junction proteins occludin and ZO-1 were significantly lower in proximal small intestine of old mice than in young animals (Fig. 2, Supplemental Fig. 1). Similar differences were not found in distal small intestine and colon (Fig. 2, Supplemental Fig. 1). Since the most prominent changes in tight junction protein levels were found in the proximal small intestine, all further studies were focused on this part of the small intestine.
The loss of tight junction proteins in the proximal small intestine was associated with significantly lower mRNA expression of the antimicrobial peptides Lysozyme and Cramp in old mice when compared to young animals. (Lysozyme and Cramp: p < 0.05; Table 1). Expressions of stem cell markers Leucine-rich repeat-containing G-protein coupled receptor 5 (Lgr5) and telomerase reverse transcriptase (Tert) were significantly lower in proximal small intestine of old mice compared to younger animals, while Leucine-rich repeats and immunoglobulin-like domains protein 1 (Lrig1) and B lymphoma Mo-MLV insertion region 1 homolog (Bmi1) mRNA expressions were not different between groups (Table 1). The total microbial community of the small intestine was statistically different between young and old mice (p = 0.001, Fig. 2F), where 28.6% of the OTU were unique for young mice and 9% for old mice. The average dissimilarity between groups was 60%. Analysis of the community structure showed that Firmicutes were more abundant in old mice than in young mice (p = 0.07). The abundances of the genera Allobaculum and Bifidobacteria were higher in old mice when compared to young mice (p < 0.05 for Allobaculum and Bifidobacteria abundance). At genus level, unclassified members of Porphyromonadaceae and Lachnospiraceae were significantly more abundant in small intestine of young than in old animals (p < 0.05) (Fig. 2, Supplemental Table 4).
Alterations of NO-homeostasis have been discussed to be critical in the development of intestinal barrier dysfunction (for overview see Ref. [42]). Expression of inducible nitric oxide synthase (inos) mRNA, concentration of 3-NT protein adducts and NO2− in small intestinal tissue were significantly lower in 24 months old mice than in 3 months old animals (Fig. 2, Supplemental Fig. 1). While protein levels of arginase-2, the isoform of arginase predominately found in the tip of the villi in small intestine, were similar between young and old mice, the activity of total arginase was significantly higher in small intestinal tissue of old mice when compared to young animals (Fig. 2).
Microbiota composition in fecal samples used for the FMT differed markedly between young and old donors (Supplemental Fig. 2); however, the six week long FMT had no effects on gross signs of aging e.g., body weight, activity or general behavior of 17 months old mice showing signs of beginning senescence and impaired barrier function before the treatment. Expression of p16 mRNA in liver tissue and PAI-1 concentration in plasma were similar between groups after the FMT. Also, neither intestinal permeability in small intestine nor expression of tight junction proteins or bacterial endotoxin and sCD14 protein levels in plasma differed between treatment groups (Fig. 3, Supplemental Table 5). Furthermore, Tlr4 mRNA expression in liver tissue was similar between groups, while in o + oFMT-treated mice, Myeloid differentiation primary response 88 (Myd88) mRNA expression was significantly lower than in o + yFMT-treated animals (Fig. 3). Expression of Mucin 2 (Muc2), Lysozyme and stem cell factors Lgr5 and Tert mRNA in small intestinal tissue was also similar between groups regardless of microbiota transplanted (Supplemental Table 5). However, the microbiota composition of o + oFMT- and o + yFMT-treated mice was significantly different (Supplemental Fig. 2 and Supplemental Table 6).
In 17 months old mice, the treatment with the arginase inhibitor norNOHA for 6 weeks resulted in significantly lower p16 mRNA expression in hepatic tissue, lower intestinal permeability, higher mRNA expression of tight junction proteins occludin and Zo1 in proximal small intestine, lower bacterial endotoxin in plasma and lower Cd14 as well as Myd88 mRNA expression in liver tissue when compared to age matched vehicle treated mice (Fig. 4). Tlr4 mRNA expression in liver tissue and CD14 and PAI-1 concentration in plasma were similar between groups. Also, levels of NO2− were higher in small intestinal tissue of norNOHA-treated mice compared to vehicle controls (Fig. 4). Furthermore, mRNA expression of stem cell markers Lgr5 and Tert were significantly higher in small intestinal tissue of norNOHA-treated mice compared to vehicle-treated mice (Table 2). No differences were found between groups when assessing ZO-1 protein staining and mRNA expression of Lysozyme, Muc2 as well as the total bacterial microbiota composition in small intestine (Table 2, Supplemental Fig. 3 and Supplemental Table 7).
To determine if an altered ROCK2 activity might be critical in the above described alterations, we determined phosphorylation of ROCK2 in small intestinal tissue of 3 and 24 months old mice. In small intestinal tissue of old mice, pROCK2 showed a trend towards higher levels than in young mice (p = 0.06, Fig. 5). Treatment of small intestinal everted sacs of old mice with the ROCK-inhibitor Y-27632 and norNOHA, respectively, decreased both permeability and arginase activity to a similar extend. These changes were associated with an increase in NO2− levels in tissue (Fig. 5). Moreover, inhibiting ornithine decarboxylase with difluoromethylornithine (DFMO) resulted in a decreased permeability, too, while NO2− levels were not affected.
Results of recent studies suggest that aging is related to changes in intestinal microbiota composition and barrier function (for overview see Ref. [43]). It has also been discussed that changes in intestinal microbiota composition and/or the increased permeation of bacterial (endo)-toxin may enhance aging-associated decline. Here, employing studies in healthy older men and mice, we found that even in the absence of any overt health impairments, old age is associated with increased levels of bacterial (endo)-toxins and significant changes in intestinal microbiota composition, a loss of tight junction proteins and increased permeability in small intestine in old mice. Furthermore, expressions of antimicrobial peptides and several stem cell factors were also lower in small intestinal tissue while Tlr4 mRNA expression was higher in the liver of old mice, further supporting the hypothesis that old age in mice is associated with impairments of intestinal barrier function in small intestine and an increased translocation of bacterial toxins. Interestingly, neither tight junction protein levels nor intestinal permeability were altered in large intestine of animals. Old age has been reported before to be associated with marked changes in diversity and the prevalence of specific bacterial strains in fecal and small intestinal microbiota, as well as elevated levels of bacterial endotoxin in a variety of species [9,12,[14], [15], [16], [17],44]. In baboons, these alterations have been linked to a loss of tight junction proteins in the large intestine [10], while in rodents, being in line with our finding, tight junctions’ protein levels were lower in small intestinal tissue [11]. However, data regarding changes of intestinal permeability in humans is still contradictory. Indeed, in studies employing sugar-based functional tests e.g., lactulose/mannitol or sucrose excretion, no differences or even lower intestinal permeability were reported in older adults compared to young healthy individuals [[45], [46], [47]]. It has been speculated that this apparent discrepancy between studies may be related to impairments of kidney function or a concomitant intake of drugs and prevalence of diseases in study subjects as well as their specific age (e.g. >75 year or 65–75 years of age) and their gender [45,46] and permeability tests used (e.g. aggregates of bacterial endotoxin with sizes up to 1,000 kDa [48,49] vs. markedly smaller molecular sizes of sugars employed in permeability tests (for overview see Ref. [50])). Taken together, further studies are needed to delineate changes in intestinal barrier function in aging humans. Still, results of the present study suggest that regardless of the species, old age is associated with elevated bacterial (endo)-toxin levels, and that in mice, these increases are related to changes of intestinal microbiota composition and a loss of tight junctions in the small intestinal tissue. These results by no means preclude that other aging-related alterations like an impaired clearance of bacterial endotoxin or elevated bioavailability in the gut might have been critical herein, too. Also, further studies are needed to assess if alterations alike are also found in women and female mice as in the present study only male subjects and mice were enrolled. Indeed, results of studies in drosophila suggest sex difference in the development of aging-associated intestinal degeneration [51].
Previous studies by others (Thevaranjan et al., [19]) have suggested that the systemic increase in proinflammatory cytokines and aging-related impairments of intestinal barrier function are related to a diminished diversity of microbial community. In the same study it was also shown that co-housing old and young germ-free mice with old SPF-mice did result in similarly increased paracellular intestinal permeability. In contrast, young germ-free mice co-housed with old SPF-mice showed an increased intestinal permeability compared to young germ-free mice co-housed with young SPF-mice [19]. Furthermore, Thevaranjan et al. also reported, that germ-free mice showed lower intestinal permeability and mortality up to the age of 22 months. This was also associated with lower levels of plasma cytokines and subsequently with reduced ‘inflammaging' compared to age-matched old wild-type mice [19] further suggesting that intestinal microbiota and/or bacterial (endo-) toxins may be critical in the development of aging-associated decline. Fransen et al. showed that the transfer of bacteria from old mice to young germ-free mice promoted inflammation in the small intestine and increased intestinal permeability [52]. These data are partially in contrast with the findings of the present study where neither the transfer of fecal microbiota obtained from young to old mice nor form old to old mice affected intestinal permeability, markers of senescence, tight junction, or stem cell factor mRNA expression in the small intestine. Interestingly, microbiota composition in small intestine was significantly different between old mice receiving young FMT and old mice receiving old FMT. Differences between our studies and those of others might have resulted from marked differences in experimental design (germ-free mice vs. SPF animals, application of microbiota). Taken together, these data suggest that aging-associated intestinal barrier function may not only depend on changes of intestinal microbiota associated with aging but that other ‘independent' factors may be critical herein. However, these findings by no means preclude that intestinal microbiota may be critical in healthy aging but rather suggest that other cellular alterations - independent of intestinal microbiota - may impact aging-associated intestinal barrier dysfunction (see below).
Intestinal barrier dysfunction has also been discussed to be related to an imbalance of iNOS and arginase activity [42,53,54]. Indeed, we have shown that the loss of intestinal barrier function in diet-induced non-alcoholic fatty liver disease is associated with an increased formation of NOx and lower activity of arginase [21,53]. In humans and rodents with inflammatory bowel disease, contrasting the findings in settings of metabolic disease, arginase-1 expression in intestinal tissue was reported to be induced and a genetic deletion of arginase-1 in immune cells was reported to arbitrate a significant protection against intestinal inflammation [55]. In the present study, old age in mice was associated with lower NOx levels in small intestinal tissue, while arginase activity was higher. Furthermore, the treatment with the arginase inhibitor norNOHA improved intestinal barrier function going along with lower bacterial endotoxin levels in portal plasma and a decreased expression of markers of senescence in liver while NOx levels in small intestinal tissue were higher. If treating mice with a NO donor to compensate the imbalance between NO synthesis and arginase activity during aging-associated impaired intestinal barrier exerts similar beneficial effects, remains to be determined. Also, if an oral supplementation of the amino acid arginine, shown to be a substrate for arginase but also allosteric regulator [53,56], affects aging-associated intestinal barrier dysfunction remains also to be determined in further experiments. Somewhat in line with our findings, Xiong et al. suggest that a genetic deletion of arginase-2 attenuates the onset of senescence and extends life-span in mice [57]. In the present study, arginase-2 was the predominant isoform of arginase detected in the villi of the small intestine. Interestingly, while the expression of antimicrobial peptides and stem cell factors were higher in old mice treated with norNOHA, intestinal microbiota composition was similar between groups. These results suggest that while having been associated with the development of several aging-associated dysfunctions, maintaining intestinal barrier function in aging may not (solely) depend on intestinal microbiota. Rather alterations of cellular metabolism and herein especially of the NO homeostasis in small intestinal tissue might be critical in maintaining intestinal barrier function, too. Interestingly, results employing ex vivo models further suggest that the induction of arginase activity is related to changes in the phosphorylation of ROCK2 and the downstream signaling cascade of arginase. The RhoA/ROCK signaling cascade has been indicated to regulate both, arginase-2 and myosin light chain phosphorylation [58,59]. Also an activation of the RhoA/ROCK signaling cascade has been suggested depending upon interleukin-6 and TNFα, two cytokines found to be increased in blood of older human and rodents [19,[59], [60], [61], [62]]. And while others have demonstrated that old TNFα knockout mice are protected from intestinal barrier dysfunction [19] it remains to be determined if the dysregulation of arginase activity and NO homeostasis found in the present study is related to a TNFα-dependent regulation of the RhoA/ROCK/arginase signaling cascade.
Taken together, our data further bolster the hypothesis that even healthy aging is associated with changes in intestinal microbiota composition and intestinal barrier function. Our data also suggest that the impairments at the level of intestinal barrier may not primarily result from shifts in intestinal microbiota composition. Rather, alterations in intestinal NO homeostasis, and herein specifically an increased activity of arginase and decreased bioavailability of NO in intestinal mucosa, may trigger the loss of intestinal tight junction proteins subsequently leading to an increased translocation of bacterial (endo)-toxins (see Graphical Abstract). However, further studies are needed to determine whether alterations alike are also found in aging humans and if a modulation of the intestinal NO homeostasis has a persisted protective effect on aging associated intestinal barrier dysfunction.
Annette Brandt: Formal analysis, Investigation, Writing – original draft, Writing - review & editing. Anja Baumann: Investigation. Angélica Hernández-Arriaga: Formal analysis, Investigation. Finn Jung: Investigation. Anika Nier: Investigation. Raphaela Staltner: Investigation. Dragana Rajcic: Investigation. Christian Schmeer: Methodology, Resources. Otto W. Witte: Methodology, Resources. Barbara Wessner: Methodology, Resources. Bernhard Franzke: Resources, Investigation. Karl-Heinz Wagner: Methodology, Resources. Amélia Camarinha-Silva: Formal analysis, Investigation. Ina Bergheim: Conceptualization, Methodology, Writing – original draft, Writing - review & editing, Supervision, Funding acquisition.
The present work was funded by grants from German Research Foundation within the Priority Program SPP1656 (FKZ: BE2376/8-1 to IB, CA1708/1-1 to ACS), the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 859890 (SmartAge), the Herzfelder Family Foundation/ Austrian Science Fund FWF (P35271 to IB) and the Austrian Science Fund FWF (I04844 to IB).
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
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PMC9649432 | 36280732 | Gardar Sveinbjornsson,Magnus O. Ulfarsson,Rosa B. Thorolfsdottir,Benedikt A. Jonsson,Eythor Einarsson,Gylfi Gunnlaugsson,Solvi Rognvaldsson,David O. Arnar,Magnus Baldvinsson,Ragnar G. Bjarnason,,Thjodbjorg Eiriksdottir,Christian Erikstrup,Egil Ferkingstad,Gisli H. Halldorsson,Hannes Helgason,Anna Helgadottir,Lotte Hindhede,Grimur Hjorleifsson,David Jones,Kirk U. Knowlton,Sigrun H. Lund,Pall Melsted,Kristjan Norland,Isleifur Olafsson,Sigurdur Olafsson,Gudjon R. Oskarsson,Sisse Rye Ostrowski,Ole Birger Pedersen,Auðunn S. Snaebjarnarson,Emil Sigurdsson,Valgerdur Steinthorsdottir,Michael Schwinn,Gudmundur Thorgeirsson,Gudmar Thorleifsson,Ingileif Jonsdottir,Henning Bundgaard,Lincoln Nadauld,Einar S. Bjornsson,Ingrid C. Rulifson,Thorunn Rafnar,Gudmundur L. Norddahl,Unnur Thorsteinsdottir,Patrick Sulem,Daniel F. Gudbjartsson,Hilma Holm,Kari Stefansson | Multiomics study of nonalcoholic fatty liver disease | 24-10-2022 | Genome-wide association studies,Liver diseases | Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options. | Multiomics study of nonalcoholic fatty liver disease
Nonalcoholic fatty liver (NAFL) and its sequelae are growing health problems. We performed a genome-wide association study of NAFL, cirrhosis and hepatocellular carcinoma, and integrated the findings with expression and proteomic data. For NAFL, we utilized 9,491 clinical cases and proton density fat fraction extracted from 36,116 liver magnetic resonance images. We identified 18 sequence variants associated with NAFL and 4 with cirrhosis, and found rare, protective, predicted loss-of-function variants in MTARC1 and GPAM, underscoring them as potential drug targets. We leveraged messenger RNA expression, splicing and predicted coding effects to identify 16 putative causal genes, of which many are implicated in lipid metabolism. We analyzed levels of 4,907 plasma proteins in 35,559 Icelanders and 1,459 proteins in 47,151 UK Biobank participants, identifying multiple proteins involved in disease pathogenesis. We show that proteomics can discriminate between NAFL and cirrhosis. The present study provides insights into the development of noninvasive evaluation of NAFL and new therapeutic options.
NAFL disease (NAFLD), when >5% of the liver is fat with no identifiable secondary cause, is estimated to affect 25% of the world’s population. NAFL, the first stage of NAFLD, can progress to nonalcoholic steatohepatitis (NASH). Furthermore, some patients with NASH develop liver cirrhosis and hepatocellular carcinoma (HCC). Obesity, metabolic syndrome, diabetes and hypertension are recognized risk factors for NAFL and NASH, and NASH-related cirrhosis is the second most common indication for liver transplantation in the United States of America. Several sequence variants have been associated with liver enzymes, cirrhosis and NAFLD, including missense variants in PNPLA3 (ref. ), TM6SF2 (ref. ), GCKR (ref. ) and MTARC1 (ref. ). Furthermore, a protein-truncating variant in HSD17B13 has been associated with NASH and fibrosis but not with steatosis. It is challenging to both diagnose and stage NAFLD. Although liver enzymes are commonly elevated, they are nonspecific and poor predictors of progression. Magnetic resonance imaging (MRI)-derived proton density fat fraction (PDFF) provides accurate liver fat quantification, but liver biopsy is essential for NASH diagnosis and staging. However, biopsy involves sampling variability and a risk of complications. Currently, no pharmacological therapy has been approved for NASH. The identification of potential drug targets and biomarkers to monitor disease progression and treatment response is therefore paramount. We performed genome-wide association studies (GWASs) of PDFF, NAFL, cirrhosis and HCC, and a combined GWAS of PDFF and NAFL. To further characterize the risk variants, we tested them for association with clinical traits, mRNA expression in various tissues and circulating protein levels in large datasets from Iceland and the UK Biobank (UKB). We further investigated whether plasma proteins discriminate between disease stages.
We extracted liver PDFF from raw abdominal MR images of 36,116 Britons of European ancestry from the UKB (Fig. 1, Table 1 and Methods). The MR images were generated using two acquisition techniques, that is, 8,448 images using gradient multiecho (GRE) and 27,668 using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL). The results of the two methods were similar, with 20.0% of IDEAL images and 23.6% of GRE images having a PDFF > 5%. Of the 270 (IDEAL) and 97 (GRE) individuals diagnosed with NAFL, 179 (66.3%) and 76 (78.4%) had a PDFF > 5.0% (Supplementary Fig. 1). Our PDFF estimates correlate well (r2 = 0.96) with 3,869 PDFF measurements calculated by others using LiverMultiScan (Supplementary Fig. 2). We performed a GWAS on the PDFF estimates (n = 36,116) using each individual’s first available MRI measurement. We also meta-analyzed GWASs on clinically diagnosed NAFL (International Classification of Disease, 10th revision (ICD-10), code K76.0), including 785 cases from Iceland (deCODE genetics), 5,921 from the UK (UKB), 2,134 from the USA (Intermountain INSPIRE and HerediGene registries) and 651 from Finland (FinnGen), for a total of 9,491 NAFL cases and 876,210 controls. We combined the summary-level GWAS PDFF data and the NAFL ICD-10 code meta-analysis data to maximize power to detect associations using multitrait analysis. We identified 18 independent sequence variants at 17 loci in the combined GWAS (Table 2 and Supplementary Table 1), of which 4 have not been reported in an NAFL GWAS (in/near PNPLA2, TOR1B, APOH and GUSB). The variants associated with the combined PDFF and NAFL at genome-wide significance (GWS) were nominally significant for both phenotypes (P < 0.05). Furthermore, their effects on the two phenotypes were comparable (Fig. 2), suggesting that variants that increase PDFF also increase risk of NAFL and vice versa. We therefore refer to the combined phenotype as NAFL. The strongest association was with missense variant p.Ile148Met in PNPLA3 (P = 3.0 × 10−217, effect = 0.28 s.d. for PDFF and P = 9.7 × 10−116, odds ratio (OR) = 1.47 for NAFL). This variant has been reported as being associated with NAFLD. Two of the eighteen variants had greater effect on PDFF in men than in women, p.Ile43Val in GPAM (effect in men = 0.09 s.d., effect in women = 0.04 s.d., Pmales versus females = 0.00099) and p.Glu167Lys in TM6SF2 (effect in men = 0.40 s.d., effect in women = 0.28 s.d., Pmales versus females = 7.3 × 10−5). The NAFL variants include a low-frequency (minor allele frequency (MAF) = 2.96%) missense variant, p.Cys325Gly, in APOH (encoding β2-glycoprotein 1) (P = 4.0 × 10−9, effect = 0.13 s.d. for PDFF and P = 0.02, OR = 1.11 for NAFL). APOH is highly expressed in the liver and p.Cys325Gly has been associated with liver enzymes. A low-frequency (MAF = 1.32%) missense variant, p.Asn252Lys, in PNPLA2, the closest homolog of PNPLA3 (ref. ), is also associated with NAFL (P = 4.9 × 10−12, effect = 0.22 s.d. for PDFF and P = 0.0013, OR = 1.16 for NAFL). Homozygous mutations in PNPLA2 have been associated with neutral lipid storage disease and fatty liver is among its features. PNPLA2 p.Asn252Lys has been associated with increased waist:hip ratio (WHR) and high-density lipoprotein-cholesterol levels but not with NAFL. Adjusting for the WHR does not affect the association (P = 2.4 × 10−10, effect = 0.20 s.d. for PDFF). A common NAFL-associated variant, rs6955582[A], intronic in GUSB (P = 4.7 × 10−7, effect = −0.038 s.d. for PDFF and P = 0.00019, OR = 0.95 for NAFL) correlates with a missense variant in GUSB, p.Leu649Pro (r2 = 0.87 in the UK and 0.99 in Iceland, MAF = 44.9%). An intronic variant (MAF = 9.23%) in TOR1B, rs7029757[A] (P = 9.2 × 10−10, effect = −0.078 s.d. for PDFF and P = 0.00028, OR = 0.92 for NAFL) is also associated with NAFL. The rs7029757[A] has been reported as associating with alanine aminotransferase (ALT) levels and cirrhosis but not NAFL. We also performed meta-GWASs on 4,809 all-cause cirrhosis (cirrhosis for simplification; Methods) cases (ncases UK = 2,301, ncases Iceland = 691, ncases USA = 392, ncases Finland = 1,425, ncontrols = 967,898) and 861 HCC cases (ncases UK = 374, ncases Iceland = 406, ncases USA = 81, ncontrols = 819,551) using ICD-10 codes. Four variants were associated with cirrhosis (Table 3), two of which associated with PDFF (Supplementary Table 2). The two that did not associate with PDFF (P > 0.19) are a splice-region variant in HSD17B13, rs72613567[TA], which has been reported as being associated with NASH, and a missense variant p.Glu366Lys in SERPINA1, which causes α1-antitrypsin deficiency. We compared the PDFF and cirrhosis effects of the 18 NAFL variants in the UKB data. The cirrhosis effects were proportional to the PDFF effects (Fig. 2) except for p.His48Arg in ADH1B (alcohol dehydrogenase 1b) and p.Cys282Tyr in HFE (homeostatic iron regulator). These two variants are associated with cirrhosis through alcohol consumption (ADH1B) and hemochromatosis (HFE), rather than solely through hepatic fat. Similarly, the effects of the 18 variants on HCC were proportional to the PDFF effects (Fig. 2).
To prioritize plausible causal genes at the associated loci, we evaluated the lead affected amino acid sequence, mRNA expression (expression quantitative trait loci (eQTLs)) or splicing QTLs (sQTLs). For this analysis, we used annotation of 46.5 million sequence variants tested in the GWAS and measured mRNA levels using inhouse RNA-sequencing (RNA-seq) data from whole blood (n = 17,846) and adipose tissue (n = 770), and publicly available data in the Genotype-Tissue Expression project (GTEx, v.v8). Fourteen lead associations were with missense variants or variants in high linkage disequilibrium (LD; r2 > 0.8) with a missense variant: in PNPLA3, APOE, GCKR, GPAM, PNPLA2, TMC4, MTARC1, APOH, ADH1B, HFE, ERLIN1, GUSB and two independent variants in TM6SF2. The missense variants in GUSB, TM6SF2, PNPLA3 and TMC4 are associated with expression levels of the corresponding gene (cis-eQTLs) in various tissues, and the missense variant in TMC4 is also associated with liver expression of MBOAT7. Loss of MBOAT7 has been associated with NAFLD. These variants are top eQTLs, that is, they are either the strongest association at their loci with expression levels of these genes or highly correlated to it (r2 > 0.8). The variants in TOR1B, HSD17B13 and GUSB associate with splicing in whole blood as top sQTLs (Supplementary Table 3). The intronic variant rs7029757[A], located 50-bp downstream of exon 2 in TOR1B (torsin 1b), is associated with cryptic splicing (P = 1.0 × 10−1493; Supplementary Table 3). This variant elongates exon 2 by 50 bp, leading to a frameshift introducing a premature stop codon in exon 3 (Supplementary Fig. 3).
We tested the 20 NAFL and cirrhosis variants for association with 51 phenotypes related to liver function or NAFLD, using data from Iceland, the UK, Denmark (CHB-CVDC/DBDS) and available meta-analyses (Supplementary Table 4). The risk alleles of all variants were associated with increased levels of at least one liver enzyme (Figs. 2 and 3). Most variants were associated with cholesterol and sex hormone-binding globulin measures (SHBG), but with inconsistent direction of effects with regard to the NAFL risk allele (Fig. 3). The missense variants p.Thr165Ala in MTARC1 and p.Ile43Val in GPAM had the greatest similarity in their associations with the tested traits (Figs. 3 and 4). The missense variant p.Cys325Gly in APOH was also associated with increased risk of atrial fibrillation (P = 3.5 × 10−9, OR = 1.12, ncases = 96,018), heart failure (P = 2.4 × 10−9, OR = 1.12, ncases = 99,214) and higher levels of lipoprotein (a) (Lp(a); P = 2.2 × 10−56, effect = 0.10 s.d.) (Supplementary Table 4). The p.His48Arg in ADH1B was associated with reduced risk of alcohol dependence (P = 2.6 × 10−64, OR = 0.31, ncases = 60,800). We compared variant associations with alcoholic liver disease (ALD) diagnoses and NAFL diagnoses (Supplementary Fig. 4). The p.His48Arg was the only variant with a significantly greater effect on the risk of ALD (ncases = 3,818) than of NAFL (OR = 0.33 and OR = 0.85, respectively, P = 6.6 × 10−10).
Among the 20 variants with GWS associations with NAFL and cirrhosis, 14 are common or low-frequency missense variants (MAF < 3%). Although these variants implicate probable relevant genes, it is not clear whether loss or gain of function of the encoded proteins reduces or increases disease risk. To investigate this, we looked for associations with rare predicted loss-of-function (pLOF) variants in candidate causal genes at these loci, using data from Iceland and UK. We tested 47 pLOF variants for associations with the same traits that we detected with the lead variant at the locus and found two rare pLOF variant associations in Iceland: p.Arg305Ter in MTARC1 (MAF = 0.34%) and p.Thr189GlyfsTer5 in GPAM (MAF = 0.11%) (Supplementary Table 5). Both genes encode mitochondrial enzymes. The rare pLOF variant p.Arg305Ter in MTARC1 was associated with lower total cholesterol levels (P = 3.2 × 10−5, effect = −0.18 s.d.) (Fig. 4 and Supplementary Table 5), consistent with the protective allele of the common missense variant p.Thr165Ala in MTARC1. As p.Arg305Ter is predicted to reduce the function of MTARC1, this suggests that p.Thr165Ala also lowers cholesterol levels through reduced MTARC1 function. Therefore, its association with a reduced risk of NAFL is most probably driven by reduced MTARC1 function. Our findings align with reports of another protein-truncating variant in MTARC1, p.Arg200Ter, that is associated nominally with reduced cirrhosis risk. The minor allele of the common missense variant in GPAM, p.Ile43Val, is associated with increased risk of NAFL and higher total cholesterol levels, whereas the rare pLOF variant, p.Thr189GlyfsTer5, is associated with lower total cholesterol levels (P = 3.5 × 10−8, effect = −0.41 s.d.), suggesting that p.Thr189GlyfsTer5 decreases NAFL risk through decreased GPAM function. Although neither of these pLOFs is associated with NAFL or cirrhosis, they give information on whether previously reported associations are gain or loss of function. MTARC1’s p.Arg305Ter is located in the gene’s last exon, and we found no evidence of nonsense-mediated decay in our RNA data (Supplementary Fig. 3). We also found a pLOF variant in GCKR, p.Arg540Ter (MAF = 0.60%), which is associated with increased triglyceride levels (P = 6.9 × 10−7, effect = 0.16 s.d.), similar to the NAFL risk-increasing allele of the GCKR common missense variant p.Leu446Pro.
rs72613567[TA] in HSD17B13 and p.Ile148Met in PNPLA3 interact in their effects on liver enzymes. We replicated the interaction between rs72613567[TA] in HSD17B13 and p.Ile148Met in PNPLA3 (P = 9.0 × 10−15 and 2.9 × 10−12 on aspartate transaminase (AST) and ALT, respectively) (Supplementary Fig. 5). The interaction of these two variants on the diagnoses of NAFL and HCC was also significant (P = 0.00073 and P = 0.0020, respectively). PDFF and body mass index (BMI) were correlated (r2 = 0.32), with only 2% of individuals having high PDFF (>5%) and low BMI (<25 kg m−2) compared with 19% with high PDFF and high BMI (≥25 kg m−2) (Table 1 and Fig. 1). Among the 20 NAFL and cirrhosis variants, only p.Cys130Arg in APOE and p.His48Arg in ADH1B were associated with BMI (Supplementary Table 4, nBMI measures = 486,305). Missense variants in PNPLA3, TM6SF2, APOE and GUSB interacted with BMI to affect PDFF (P < 0.05/20; Supplementary Table 6 and Supplementary Fig. 6). None of the variants interacted with age to affect PDFF (Supplementary Fig. 7). Among participants in the UKB, 2,795 had 2 liver MR images taken 2–3 years apart. Consecutive PDFF measurement were correlated (r2 = 0.90), as were BMI measurements (r2 = 0.60) (Supplementary Figs. 8 and 9). Changes in PDFF and BMI associated strongly (P = 2.8 × 10−104), with direction of change in the two measures that on average were the same (Supplementary Fig. 10). We found a nominally significant interaction between p.Ile148Met in PNPLA3 and changes in BMI (P = 0.030; Supplementary Fig. 11). Furthermore, the variations in the PDFF changes were greater among p.Ile148Met carriers than noncarriers (P = 0.0055; Supplementary Fig. 12) and were not fully explained by the interaction between the genotype and change in BMI (P adjusted for change in BMI: Padj = 0.032).
To gain insight into proteins affecting the pathogenesis of NAFL or cirrhosis and search for relevant biomarkers, we analyzed protein levels measured with 4,907 aptamers by SomaScan v.4 in 35,559 Icelanders and 1,459 immunoassays using the Olink Explore 1536 in 47,151 European-ancestry participants from the UKB. The levels of 2,741 proteins associated (after Bonferroni’s adjustment) with NAFL (nIceland = 234 cases, nUK = 572 cases) and 948 with cirrhosis (nIceland = 111 cases, nUK = 303 cases) in either Iceland or UKB. We looked for associations between the 20 NAFL and cirrhosis variants and protein levels. Sixteen variants were associated with the level of one or more proteins in trans, using either Iceland (SomaScan) or UKB (Olink), as top protein QTLs (pQTLs) (Supplementary Table 7). The trans pQTLs in GCKR and SERPINA1 are nonspecific in that they are associated with 396 and 172 proteins, respectively. Focusing on the variants associated with fewer than 100 proteins, we identified 273 proteins that associated with the variants in trans (that is, variants at a different chromosomal location to the gene encoding the targeted protein), of which 26 were associated with both SomaScan and Olink measurements. Levels of ten proteins were associated with five or more variants (SMPDL3A and NAAA proteins with eight variants; SMPD1 and GUSB protein with seven variants; KRT18, HEXB, GSTA1, ENTPD5, CTSO and ACY1 proteins with five variants) (Supplementary Fig. 15) and eight out of these ten proteins are associated with NAFL or cirrhosis (Supplementary Tables 8–10). The missense variant p.Ile148Met in PNPLA3 is a top trans pQTL for 86 proteins, with aldo–keto reductase family 7 member 3 protein (AKR7A3) showing the most significant association in Iceland (SomaScan) (P = 3.1 × 10−12, effect = 0.07 s.d.) and keratin 18 (KRT18) in UKB (Olink) (P = 8.4 × 10−38, effect = 0.10 s.d.). The rs72613567[TA] in HSD17B13 was associated most significantly with levels of the DnaJ B member homolog subfamily 9 protein (DNAJB9) in Iceland (SomaScan) and carboxylesterase 3 (CES3) in UKB (Olink). Missense variants in MTARC1 (p.Thr165Ala) and GPAM (p.Ile43Val) were both associated with levels of group XIIB secretory phospholipase A2-like protein (PLA2GXIIB) as measured in Iceland (SomaScan), and inactive Cα-formylglycine-generating enzyme (SUMF2), hypoxia-upregulated protein 1 (HYOU1) and acid sphingomyelinase-like phosphodiesterase 3a (SMPDL3A), as measured in the UKB (Olink). PLA2GXIIB is highly expressed in the liver and the NAFL protective allele of these variants was associated with lower protein levels (P = 9.3 × 10−12, effect = −0.062 s.d. and P = 2.4 × 10−14, effect = −0.072 s.d., for MTARC1 and GPAM variants, respectively). Moreover, the rare pLOF variants in Iceland in GPAM and MTARC1 were both associated with lower PLA2GXIIB protein levels (P = 7.4 × 10−4, effect = −0.44 s.d. and P = 0.013, effect = −0.19 s.d., respectively). We note that other NAFL variants were associated nominally with PLA2GXIIB, some with an opposite direction of effects to the LOF variants in GPAM and MTARC1 (Supplementary Fig. 13). Therefore, to explore whether any protein measured in plasma may have a causal role in disease, we performed Mendelian randomization using all-trans pQTLs for each protein. The effects of pQTLs of the transferrin receptor protein 1 (TFRC) were proportional to their effect on PDFF (Pivw-olink = 5.6 × 10−10, Pivw-somascan = 2.0 × 10−4; Supplementary Fig. 13 and Supplementary Table 11), suggesting that TFRC may have a causal role in NAFL. Apart from TFRC, the analysis suggests that the alterations in protein levels in plasma are not causal but rather a consequence of disease because, for many proteins, the effects of the set of NAFL variants on PDFF were proportional to their effects on protein level (Supplementary Table 12). We performed a pathway analysis using the PANTHER v.16.0 tool to seek understanding of the variant–liver disease associations. The 273 proteins associating with NAFL variants were enriched for multiple metabolic and catabolic processes, including the metabolism of hormones, lipids, alcohol, vitamins, steroids and monocarboxylic acid among other pathways and biological processes (Supplementary Table 13). Last, we compared the plasma proteome of those diagnosed with cirrhosis with those with NAFL (Supplementary Table 10 and Supplementary Fig. 14). In Iceland (SomaScan), the most significant difference between NAFL and cirrhosis was, first, for calsyntenin 2 (CSTN2, P = 3.6 × 10−17, effect = 0.92 s.d.) and, second, for insulin-like growth factor-binding protein (IGFBP) 2 (P = 5.9 × 10−17, effect = 0.92 s.d.). IGFBP2 levels were elevated in individuals with cirrhosis but reduced in NAFL compared with population controls. Using UKB (Olink), the most significant difference between NAFL and cirrhosis was, first, for thrombospondin 2 (THBS2, P = 6.6 × 10−114, effect = 1.59 s.d.) and, second, for IGFBP7 (P = 1.3 × 10−89, effect = 1.35 s.d.). Levels of THBS2, IGFBP7 and IGFPB2 are measured with both SomaScan and Olink and are associated in both datasets. For NAFL compared with the population, aminoacylase-1 (ACY1) showed the strongest association in both Iceland and UKB (Supplementary Tables 8–10). To investigate whether plasma proteins can effectively discriminate between having an NAFL and cirrhosis, we trained penalized logistic regression models using liver enzymes, age, sex and BMI as a baseline, as well as using the plasma proteome and genetic risk scores (GRSs). In both Iceland (SomaScan) and UKB (Olink), the models trained with the plasma proteome outperformed other models in discriminating between NAFL and cirrhosis, NAFL and the population, and cirrhosis and the population (Fig. 5 and Supplementary Tables 14–17).
We identified 18 sequence variants that are associated with NAFL and two additional variants that are associated with cirrhosis and not PDFF. The NAFL variants affect the risk of cirrhosis and HCC proportional to their effects on PDFF, supporting a causal relationship between hepatic fat accumulation and these diseases. The NAFLD variants are associated with other phenotypes, with variable patterns of association. One interpretation of this is that perturbations of more than one biochemical pathway lead to NAFLD. The strongest NAFL-associated variant, p.Ile148Met, in PNPLA3 interacts with rs72613567[TA] in HSD17B13 and BMI but not with age. Longitudinal PDFF measures suggest that p.Ile148Met carriers are more susceptible to change in BMI than noncarriers, in line with previous studies suggesting that the p.Ile148Met genotype increases sensitivity to the beneficial effects of dietary interventions and bariatric surgery. Sixteen NAFL-associated variants were coding, five were eQTLs and three sQTLs, and many implicated genes were involved in lipid metabolism, reinforcing the notion of its importance as a primary process in NAFL pathogenesis. For example, variants at TM6SF2 exert their effects on hepatic lipid concentration by reducing TM6SF2 function, causing reduced secretion of triglyceride-rich lipoproteins. Several variants affect NAFL and blood lipids in the same direction, including p.Leu446Pro in GCKR, which also decreases glycated hemoglobin and type 2 diabetes risk. This is consistent with evidence that the variant increases hepatic glucose metabolism and de novo lipogenesis. The p.His48Arg in ADH1B is associated with less risk of having an NAFL diagnosis. The variant associates with less alcohol consumption and reduces the risk of ALD substantially more than the risk of NAFL. The amount of consumed alcohol (2 and 3 units of alcohol for women and men, respectively) used to distinguish between NAFL and ALD is quite arbitrary. Therefore, it is likely that the association of the variant with NAFL diagnosis is driven by its effect on alcohol consumption below the ALD diagnosis threshold. None of the other NAFL variants was associated with a significantly greater effect on ALD than NAFL. The NAFL associations include missense variants in GPAM and MTARC1, which both encode mitochondrial enzymes and are highly expressed in liver and adipose tissue. GPAM encodes glycerol-3-phosphate acyltransferase 1, which catalyzes the first step of triglyceride synthesis. Gpam knockout mice have lower hepatic triglyceride content and overexpression has the opposite effect. The results for MTARC1 are in line with previous reports of the effects of missense and LOF variants in MTARC1, but the mechanism explaining the associations was less clear. The similarity in their associations with other traits indicates that mutual or similar pathways explain the MTARC1 and GPAM associations. Furthermore, both missense variants affect plasma levels of PLA2GXIIB protein. We identified rare pLOF variants in both genes affecting the same traits and reducing liver enzymes and total cholesterol levels. This suggests that GPAM and MTARC1 have an etiological role and inhibition of GPAM and MTARC1 could be therapeutic for NAFL or NASH, with a favorable effect on the metabolic profile. The lack of associations with increased risk of a large set of diseases is reassuring with regard to treatment safety. Among associations with NAFL are missense variants in APOH and GUSB. APOH is highly and almost exclusively expressed in the liver. The other trait associations of p.Cys325Gly in APOH strongly suggest a role in lipid metabolism. Furthermore, the variant has been reported in GWASs of coronary artery disease and Lp(a) levels. We replicated the Lp(a) association and observed associations with an increased risk of atrial fibrillation and heart failure. Our results indicate a role for GUSB in the etiology of NAFL. The missense variant p.Leu649Pro in GUSB associates with both NAFL and RNA expression levels of GUSB. Furthermore, seven NAFL variants associate with GUSB plasma protein levels. GUSB encodes β-glucuronidase, a lysosomal enzyme involved in the breakdown of glycosaminoglycans. Plasma proteome analysis revealed that missense and pLOF variants in MTARC1 and GPAM are associated with levels of PLA2GXIIB in plasma. PLA2GXIIB is highly expressed in the liver and knockout mice have severe hepatosteatosis. However, our proteomic analysis does not support an etiological role for PLA2GXIIB because NAFL variants affect plasma PLA2GXIIB levels with effect directions inconsistent with their NAFL effects. Evidence suggests that PLA2GXIIB is a mediator of hepatic lipoprotein secretion and its inhibition results in decreased levels of plasma lipids. Thus, PLA2GXIIB may mediate variant effects on circulating lipids without directly affecting hepatic fat and thus could serve as a biomarker of drug target engagement. Diagnosis and monitoring of complications in patients with NAFLD are challenging. We designed models including plasma proteins that outperformed a model trained on liver enzymes and GRSs in discriminating between NAFL and cirrhosis diagnoses. Thus, levels of plasma proteins have the potential to serve as a noninvasive tool for use in the diagnosis and monitoring of disease, whereas GRSs are associated with a lifetime risk of disease. THBS2 was elevated in individuals with cirrhosis compared with NAFL and the population, and ACY1 in individuals with NAFL compared with the general population. Intrahepatic THBS2 expression levels have previously been shown to correlate with fibrosis in patients with NAFLD. The association of IGFBP2 and IGFBP7 with cirrhosis and NAFL is consistent with previous studies of NASH progression. Both proteins bind insulin-like growth factors (IGFs) and modulate their availability. IGFs are mainly produced in the liver, and IGFBP2 and IGFBP7 elevation may reflect imbalances in the IGF system due to liver damage. However, an etiological role has been suggested for IGFBP7, which may contribute to hepatic fibrogenesis and act as a tumor suppressor in HCC. Levels of SHBG are also associated with cirrhosis compared with NAFL, consistent with previous reports of a positive correlation with advanced fibrosis in NASH. There are conflicting epidemiological studies about whether NAFLD is associated with increased or decreased levels of SHBG. In line with this, many NAFL variants are associated with SHBG plasma levels with inconsistent directions of effect compared with their effect on hepatic fat content. A limitation of the present study is the lack of data enabling a more detailed phenotype stratification, in particular histological data for disease staging. Furthermore, information on other causes of liver disease, such as alcohol consumption, is limited. Our approach is, however, in line with the recent opinion to not base the disease diagnosis on a criterion of exclusion of other diseases, such as ALD. In conclusion, we used multiomics data to shed light on the genetic basis of NAFLD. Analysis of pLOF variants, blood RNA expression and plasma proteomics data pointed to causative genes and whether changes in their functions contribute to the pathogenesis. We demonstrated the diverse effects of NAFL risk alleles on other diseases and traits, including blood lipids and proteins, and showed that plasma proteomics has the potential to stage NAFLD.
The UKB project is a large prospective cohort study of ~500,000 individuals from across the United Kingdom, aged between 40 and 69 years at recruitment. The present study has collected extensive phenotypic and genotypic information on participants, including ICD-10-coded diagnoses from inpatient and outpatient hospital episodes and abdominal MRI through its imaging study. Genotype imputation data were available for 487,409 participants (May 2017 release), of which 408,658 were included because they self-reported as white. The UKB resource was used under application no. 56270. All phenotype and genotype data were collected following an informed consent obtained from all participants. The North West Research Ethics Committee reviewed and approved UKB’s scientific protocol and operational procedures (REC reference no.: 06/MRE08/65). The Icelandic deCODE genetics study is based on WGS data from 49,708 Icelanders participating in various research projects at deCODE genetics. Variants identified through WGS were imputed into 155,250 chip-genotyped Icelanders using long-range phasing and their untyped close relatives based on genealogy. Sequencing was done using GAIIx, HiSeq, HiSeqX and NovaSeq Illumina technology. SNPs and insertions/deletions (indels) were identified, and their genotypes were called using joint calling with Graphtyper. All participants who donated blood signed an informed consent. The personal identities of the participants and biological samples were encrypted by a third-party system. The study was approved by the Icelandic Data Protection Authority and the National Bioethics Committee of Iceland (no. VSN-20-182). FinnGen summary statistics, including fatty liver disease and cirrhosis, were imported in December 2020 from a source available to researchers (v.4: https://www.finngen.fi/en/access_results) and methods have been documented (https://finngen.gitbook.io/documentation). The FinnGen database consists of samples collected from the Finnish biobanks and phenotype data collected at the national health registers. The Coordinating Ethics Committee of the Helsinki and Uusimaa Hospital District evaluated and approved the FinnGen research project. The project complies with existing legislation (in particular, the Biobank Law and the Personal Data Act). The official data controller of the present study is the University of Helsinki. The Copenhagen Hospital Biobank Cardiovascular Study (CHB-CVDC) was used to acquire secondary cardiovascular phenotypes. CHB-CVDC involves a targeted selection of patients with cardiovascular disease from the CHB, a biobank based on patient blood samples drawn in Danish hospitals. The CHB-CVDC has been approved by the National Committee on Health Research Ethics (1708829) and the Danish Data Protection Agency (P-2019-93). For binary phenotypes, the control group included blood donors from the Danish Blood Donor Study (DBDS) (n = 99,000), approved by the Danish Data Protection Agency (P-2019-99) and the Scientific Ethical Committee system (NVC 1700407). Chip typing and genotype imputation of CHB-CVDC and DBDS were performed at deCODE genetics using a north European sequencing panel of 15,576 individuals (including 8,429 Danes). The samples from the USA (Intermountain dataset) were whole-genome studied using NovaSeq Illumina technology (n = 8,288) and genotyped using Illumina GSA chips (n = 28,279). Samples were filtered on a 98% variant yield. Over 245 million high-quality sequence variants and indels, to a mean depth of at least 20×, were identified using Graphtyper. Quality-controlled chip genotype data were phased using Shapeit 4 (ref. ). A phased haplotype reference panel was prepared from the sequence variants using the long-range phased chip genotype data. The haplotype reference panel variants were then imputed into the chip-genotyped samples using inhouse tools and methods described previously. In the US association analysis, samples assigned <93% CEU ancestry (northern European from Utah) were excluded. We adjusted for sex, age and the first 20 principal components. Phenotypic data were based on ICD-10 code diagnoses of individuals. The Intermountain Healthcare Institutional Review Board approved the study and all participants provided written informed consent before enrollment.
The MR images used for calculating PDFF for 36,116 individuals were collected as a part of the UKB abdominal protocol, which, in turn, is part of the UKB imaging enhancement. Two acquisitions were used, a single-slice GRE sequence and a single-slice IDEAL sequence. The slice was captured through the center of the liver superior to the porta hepatis. The GRE sequence was captured using the following settings: repetition time (TR) = 27 ms, time to echo (TE) = [2.38, 4.76, 7.15, 9.53, 11.91, 14.29, 16.67, 19.06, 21.44, 23.82] ms, bandwidth = 710 Hz, flip angle (FA) = 20%, voxel size = 2.5 × 2.5 × 6.9 mm3 and a 160 × 160 acquisition matrix. The IDEAL sequence used TR = 14 ms, TE = [1.2, 3.2, 5.2, 7.2, 9.2, 11.2] ms, bandwidth = 1,565 Hz, FA = 5%, voxel size = 1.719 × 1.719 × 10.0 mm3 and a 256 × 232 acquisition matrix.
We used two different approaches for calculating the PDFF from the liver MR images depending on whether the acquisition was GRE (n = 8,448) or IDEAL (n = 27,668). We implemented the PDFF estimation methods using a tailored Python code. For the GRE acquisition, we used a three-point Dixon method to compute a PDFF map using the second, fourth and sixth echoes. Eight 25-voxel rectangular regions of interest (ROIs) were defined within the liver and we computed the mean and s.d. of the PDFF maps over those ROIs. The reported PDFF was the ROI with the lowest s.d. By choosing the lowest s.d., we avoided ROIs with water–fat swaps. For the IDEAL acquisition, we assumed the following signal model for each voxel:where ρw and ρf are the water and fat components, respectively, Δf is the chemical shift of fat with respect to water, φ quantifies B0 field inhomogeneity, is an MRI relaxation constant and ti the ith echo time. The parameters of interest, ρw, ρf and , are estimated from the signal model using an iterative weighted least-squares algorithm. The PDFF map was finally constructed using at each voxel. The reported PDFF was calculated in the same way as for the GRE case. Iron concentration (Supplementary Fig. 1) can be estimated by using the coefficient: Iron concentration = 0.202 + 0.0254 . To evaluate the correspondence between the PDFF scores for the IDEAL and the GRE acquisition, we investigated 1,222 PDFF scores computed for both. We also fit a linear model to quantify this relationship yielding in units of percentage:and R2 = 0.92. There are 3,869 PDFF scores for the GRE acquisition that we computed and that are available in the UKB data showcase. Supplementary Fig. 2 shows a scatterplot of those two sets of scores demonstrating good agreement. The relationship is given in units of percentage:and R2 = 0.96.
The NAFL sample consisted of 9,491 individuals from deCODE genetics, UKB, Intermountain and FinnGen. Case status was based on the ICD-10 code K76.0 (nonalcoholic fatty liver disease) from electronic health records. To analyze NAFL complications, we defined an all-cause cirrhosis phenotype based on cirrhosis- and fibrosis-related ICD-10 codes (K70.2, K70.3, K70.4, K74.0, K74.1, K74.2, K74.6, K76.6 and KI85) and used ICD-10 code C22.0 for HCC. Analyzing this cirrhosis/fibrosis phenotype has previously been shown to increase power to detect associations compared with subtypes of nonalcoholic and alcoholic cirrhosis.
NAFL-associated variants were tested for association with other phenotypes from deCODE genetics, UKB, FinnGen, CHB-CVDC/DBDS or other publicly available data sources, which contain extensive medical information on various traits. A total of 51 phenotypes was chosen for the analysis (Supplementary Table 4) based on known associations with NAFLD, liver function or the identified variants. These included other liver diseases and known blood markers of liver function, blood lipids, cardiovascular diseases, diabetes, anthropometric traits, hematological traits and hormone levels.
We used logistic regression to test for association between sequence variants and binary phenotypes assuming an additive genetic model. For deCODE genetics, the model included the following covariates: sex, county of birth, current age or age at death (first- and second-order terms included), blood sample availability for the individual and an indicator function for the overlap of the lifetime of the individual with the time span of the phenotype collection. In CHB-CVDC/DBDS, the covariates were sex, age and 20 principal components to adjust for population stratification and blood sample availability. In the UKB study, 40 principal components were used to adjust for population stratification, and age and sex were included as covariates in the logistic regression model. When analyzing PDFF, BMI was included as a covariate in the analysis to increase power of associations. We used a linear mixed model implemented in BOLT-LMM to test for association between sequence variants and quantitative traits. The measurements used were adjusted for sex, year of birth and age at measurement (when available), and these were subsequently standardized to have a normal distribution. For the meta-analysis of summary-level statistics from different populations, we used a fixed-effects inverse variance method based on effect estimates and s.e.m. With the aim of studying NAFL, we combined summary-level data from the GWAS of PDFF and the meta-analysis of GWAS using ICD-10-code-based NAFL with multitrait analysis of genome-wide association summary statistics (MTAG). To have two nonoverlapping sets, we excluded individuals from the NAFL ICD-10 code analysis who had a PDFF measurement in the UK. To account for inflation in test statistics due to cryptic relatedness and stratification, we applied the method of LD score regression. For the GWS, we accounted for multiple testing with a weighted Bonferroni’s adjustment, using as weights the enrichment of variant classes with predicted functional impact among association signals estimated from the Icelandic data. This yielded significance thresholds of 1.8 × 10−7 for variants with high impact (including stop-gained and loss, frameshift, splice acceptor or donor and initiator codon variants), 3.5 × 10−8 for variants with moderate impact (missense, splice-region variants and in-frame indels), 3.2 × 10−9 for low-impact variants (including synonymous, 3′- and 5′-UTRs and upstream and downstream variants), 1.6 × 10−9 for other variants in DNase I hypersensitivity sites and 5.3 × 10−10 for all other variants.
To test whether a specific primary variant is affecting the mean effect of another secondary variant in a given quantitative trait (that is, to test if they are interacting), we split the population into three groups based on the genotype of the primary variant, denoted by . We then estimated the mean effect, β0, β1 and β2 of the secondary variant in each group separately, where the quantitative trait was standardized to a normal distribution. We estimated the interaction between the primary and secondary variants by considering the following model:where b and γ are the unknown parameters to be estimated. To assess the significance of the interaction parameter γ, we applied a likelihood ratio test, comparing our model with the null model: β0 = β1 = β2 (or equivalently γ = 0).
RNA-seq analysis was performed on whole blood (n = 17,846) and subcutaneous adipose tissue (n = 750). We isolated RNA using Chemagic Total RNA Kit special (PerkinElmer) in whole blood and RNAzol RT in adipose tissue, according to the manufacturer’s protocol (Molecular Research Center, RN 190). The concentration and quality of the RNA were determined using an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA was prepared and sequenced on the Illumina HiSeq 2500 and Illumina Novaseq systems according to the manufacturer’s recommendation. RNA-seq reads were aligned to personalized genomes using the STAR software package v.2.5.3 with Ensembl v.87 gene annotations. Gene expression was computed based on personalized transcript abundances estimated using kallisto. Association between sequence variants and gene expression was tested using BOLT-LMM, assuming an additive genetic model and quantile-normalized gene-expression estimates, adjusting for measurements of sequencing artefacts and demography variables. The strongest association within 1 Mb of each gene with P < 1 × 10−7 was called a top cis-eQTL. Quantification of alternative RNA splicing in whole blood was done using LeafCutter. The cis association between sequence variants and quantified splicing (cis-sQTL) was tested using linear regression assuming an additive genetic model and quantile-normalized percentage-spliced-in values (PSI) of each splice junction, adjusting for measurements of sequencing artefacts, demography variables, and 15 leave-one-chromosome-out principal components of the quantile-normalized PSI matrix. All variants with MAF > 0.2% within 30 kb of each LeafCutter cluster were tested, and the strongest association for each splice junction with P < 1 × 10−8 was called a top cis-sQTL.
Plasma samples were collected from Icelanders through two main projects: the Icelandic Cancer Project (52% of participants; samples collected from 2001 to 2005) and various genetic programs at deCODE genetics, Reykjavík, Iceland (48%). The samples collected at deCODE genetics were mainly collected through the population-based deCODE Health study. The average participant age was 55 years (s.d. = 17 years) and 57% were women. In the case of repeated samples for an individual, we selected one of them at random. This left measurements for 39,155 individuals. Of these, 35,559 Icelanders were used in the protein GWASs, because they also had genotype information. All plasma samples were measured with the SomaScan v.4 assay (SomaLogic, Inc.) containing 4,907 aptamers, providing a measurement of relative binding of the plasma sample to each of the aptamers in relative fluorescence units (r.f.u.). When testing for association between protein levels and disease, logistic regression was used with age and sex as covariates. The date of diagnosis was not available and the analysis was therefore not adjusted for the time from diagnosis. A pQTL association is considered to be in cis if the associated variant is located no more than 1 Mb from the transcription start site of the gene that encodes the measured protein, and in trans otherwise. A pQTL was considered to be significant if identified in the previous study (P < 1.8 × 10−9). A top pQTL is the top (most significantly associated) variant per megabase bin.
For a subset of 54,265 individuals in the UKB study (47,151 of British and Irish ancestry), the levels of 1,472 proteins were measured with the Olink Explore 1536 platform as a part of the UKB–Pharma Proteomics Project (UKB application no. 65851). A large majority of the samples were randomly selected across the UKB. The UKB plasma samples were measured using the Olink Explore 1536 platform (https://www.olink.com/products-services/explore) at Olink’s facilities in Uppsala, Sweden. Measurements with the Olink Explore platform use the NPX values recommended by the manufacturer, which include normalization. When testing for associations with sequence variants, the protein levels were standardized to a normal distribution.
We performed Mendelian randomization analyses with the MendelianRandomization R package, using both inverse variance, weighted linear regression and Egger regression, with default settings.
The trans pQTLs that were associated with the identified PDFF variants were grouped together and tested for enrichment in Reactome pathways, Gene ontology terms (biological process, molecular function and cellular component) and PANTHER protein classes. The analysis was performed with the PANTHER v.16.0 tool using Fisher’s exact test and false discovery rate (FDR) correction. The associated pQTLs were tested against a reference list that included all measured SomaAmers (n = 5,286). Pathway analysis was performed for all variants associating with more than two proteins.
To examine how effective circulating protein levels (SomaScan and Olink panels) are at discriminating liver disease stages, we trained a logistic regression model with an elastic net penalty on protein levels to classify between individuals diagnosed with cirrhosis and NAFL. To test how this model compares with one trained on liver enzymes, an additional logistic regression model was exclusively trained on age, sex, BMI, ALT, AST and γ-glutamyl transferase. Genetic risk scores for NAFL and cirrhosis from the identified genetic variants were constructed to compare with the protein-based prediction. We created NAFL and cirrhosis GRSs by calculating the sum-of-effect alleles of the NAFLD variants weighted by their cirrhosis and NAFL GWAS effects. In addition, logistic regression models combining the protein scores, liver enzymes and GRSs were trained. In that case, the protein scores were calculated using out-of-fold predictions (with tenfold). The SomaScan analysis was performed on 181 individuals with NAFL and 73 with cirrhosis, and the Olink analysis was performed on 610 individuals with NAFL and 262 with cirrhosis. Models trained to discriminate between the presence of disease diagnosis and population were trained, respectively, on an additional set of 20,619 individuals (SomaScan) and 38,018 individuals (Olink) without cirrhosis and NAFL. The SomaScan measurements were log(transformed) to reduce the effect of outliers. However, we found that this was not necessary for the Olink data. Boruta was used to select relevant features. The selected proteins were preprocessed with Yeo–Johnson power transforms and then scaled to zero mean and unit variance (estimated from the training set) before being fed to the logistic regression model. The elastic net λ and α parameters were tuned using grid search to minimize tenfold crossvalidated average precision. Model performance was evaluated by considering the mean and s.e.m. of the receiver operating characteristic (ROC) area under the curve (AUC) of 1,000 repeated, tenfold-stratified crossvalidation runs.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Any methods, additional references, Nature Research reporting summaries, source data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01199-5.
Supplementary InformationSupplementary Figs. 1–15 and Supplementary Note. Reporting Summary Peer Review File Supplementary Table 1Supplementary Tables 1–17. |
PMC9649437 | 36280734 | Daniel J. Weiner,Emi Ling,Serkan Erdin,Derek J. C. Tai,Rachita Yadav,Jakob Grove,Jack M. Fu,Ajay Nadig,Caitlin E. Carey,Nikolas Baya,Jonas Bybjerg-Grauholm,Anders Rosengren,Varun Warrier,Ziarih Hawi,Sabina Berretta,Evan Z. Macosko,Jonathan Sebat,Luke J. O’Connor,David M. Hougaard,Anders D. Børglum,Michael E. Talkowski,Steven A. McCarroll,Elise B. Robinson | Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p | 24-10-2022 | Autism spectrum disorders,Gene expression,Genome-wide association studies,Epigenomics | The canonical paradigm for converting genetic association to mechanism involves iteratively mapping individual associations to the proximal genes through which they act. In contrast, in the present study we demonstrate the feasibility of extracting biological insights from a very large region of the genome and leverage this strategy to study the genetic influences on autism. Using a new statistical approach, we identified the 33-Mb p-arm of chromosome 16 (16p) as harboring the greatest excess of autism’s common polygenic influences. The region also includes the mechanistically cryptic and autism-associated 16p11.2 copy number variant. Analysis of RNA-sequencing data revealed that both the common polygenic influences within 16p and the 16p11.2 deletion were associated with decreased average gene expression across 16p. The transcriptional effects of the rare deletion and diffuse common variation were correlated at the level of individual genes and analysis of Hi-C data revealed patterns of chromatin contact that may explain this transcriptional convergence. These results reflect a new approach for extracting biological insight from genetic association data and suggest convergence of common and rare genetic influences on autism at 16p. | Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p
The canonical paradigm for converting genetic association to mechanism involves iteratively mapping individual associations to the proximal genes through which they act. In contrast, in the present study we demonstrate the feasibility of extracting biological insights from a very large region of the genome and leverage this strategy to study the genetic influences on autism. Using a new statistical approach, we identified the 33-Mb p-arm of chromosome 16 (16p) as harboring the greatest excess of autism’s common polygenic influences. The region also includes the mechanistically cryptic and autism-associated 16p11.2 copy number variant. Analysis of RNA-sequencing data revealed that both the common polygenic influences within 16p and the 16p11.2 deletion were associated with decreased average gene expression across 16p. The transcriptional effects of the rare deletion and diffuse common variation were correlated at the level of individual genes and analysis of Hi-C data revealed patterns of chromatin contact that may explain this transcriptional convergence. These results reflect a new approach for extracting biological insight from genetic association data and suggest convergence of common and rare genetic influences on autism at 16p.
Genome-wide association studies (GWAS) have productively identified robust statistical associations between thousands of common genetic variants and traits. However, most associations are noncoding, complicating efforts to identify the genes that mediate these associations. A dominant approach is to fine-map associations to individual variants and then to their nearby target genes. Although there are numerous examples of success, functional interpretation of individual genetic variants remains a critical bottleneck. Moreover, most complex trait heritability often does not reside within these individually significant associations, but is rather scattered across thousands of individually nonsignificant loci across the genome. Autism spectrum disorder (ASD, autism) provides a compelling example for the need to jointly interpret many classes of genetic variation. Although common polygenic variation is the largest genetic influence on autism at a population level, extracting biological insight from this predominantly noncoding signal is challenging. Similarly, de novo recurrent copy number variants (CNVs), which are strongly associated with autism, often encompass many genes with generally undefined downstream mechanisms. For example, although deletion of the 0.7-Mb, 31-gene locus at chromosome 16p11.2 is one of the most common and largest single genetic influences on autism, exactly how the deletion increases the likelihood of autism diagnosis has remained undetermined despite considerable inquiry. Thus, a critical open question is whether regional polygenic signals colocalize with recurrent large CNVs and whether this colocalization can highlight uncommonly relevant areas of the genome for autism diagnosis. In particular, given that both regions of common polygenic influence and recurrent large CNVs span many genes and influence chromatin structure and gene regulatory landscapes, large chromatin landscapes have the potential to unify analysis of regional polygenic and rare variations. To examine polygenic influences arising from regions of the genome, including regions harboring autism-associated CNVs, we developed the stratified polygenic transmission disequilibrium test (S-pTDT), which extends the trio-based polygenic transmission disequilibrium test (pTDT) to genomic annotations. Using S-pTDT and 9,383 European ancestry autism trios, we performed an unbiased genome-wide search for excess over-transmission of autism’s polygenic influences. Unexpectedly, the greatest excess is localized to the 33-Mb p-arm of chromosome 16 (16p), the region that includes the recurrent, autism-associated and mechanistically cryptic proximal 16p11.2 CNV. Further linking the 16p11.2 CNV with the broader p-arm of the chromosome, in vitro deletion of the 16p11.2 locus was associated with decreased average expression of neuronally expressed genes on chromosome 16p. Similarly, an increased autism polygenic score (PGS) constructed exclusively with 16p variants was associated with decreased average expression of cortically expressed genes on 16p across multiple cohorts. These transcriptional effects of the 16p11.2 deletion and 16p autism PGS were correlated at the level of individual genes on 16p, suggesting mechanistic convergence of common and rare variant influences on autism in the region. We observed chromatin contact patterns that we hypothesize explain this transcriptional convergence: uncommonly high within-16p chromatin contact in two independent Hi-C datasets and increased contact between the 16p11.2 locus and a distal region on 16p (Mb: 0–5.2) with convergent gene expression changes. Our results motivate a model of convergent common and rare genetic influences on autism at 16p and more broadly suggest that chromatin contact may facilitate coordinated genetic and transcriptional effects within very large regions of the genome.
Individuals with autism inherit more common polygenic influences on autism from their parents than expected by chance (‘over-transmission’). Using pTDT, we observe mean over-transmission of autism PGS within European ancestry trios from three different autism trio cohorts: the Psychiatric Genomics Consortium (PGC) (n = 4,335 trios, 0.20 s.d. over-transmission, P = 1.5 × 10−37), the Simons Simplex Collection (SSC) (n = 1,851 trios, 0.19 s.d. over-transmission, P = 1.3 × 10−17) and Simons Foundation Powering Autism Research (SPARK) (n = 3,197 trios, 0.17 s.d. over-transmission, P = 6.4 × 10−21), all using a PGS generated from an external GWAS of the Danish iPSYCH consortium (19,870 individuals with autism and 39,078 controls; Supplementary Figs. 1 and 2). As biological insights from autism’s common variant influences have been limited, we aimed to leverage the statistical power of pTDT to identify regions of the genome with excess common variant relevance in autism. We therefore developed S-pTDT, which estimates transmission in parent–child trios of PGS constructed from small sets of SNPs (Fig. 1a). Similar to pTDT, S-pTDT’s within-family design prevents spurious association due to population stratification and many types of ascertainment bias. We first asked whether S-pTDT could identify any regions of the genome with transmission of autism polygenic influence significantly over or under genome-wide expectation. To do this, we constructed stratified PGSs from adjacent blocks of SNPs, yielding 2,006 (often overlapping) partitions collectively covering the whole genome (median SNPs per partition: 3,000, median partition length: 11.7 Mb; Supplementary Fig. 3 and Methods). We then performed S-pTDT on each partition, first estimating transmission in the 5,048 trios from SSC + SPARK and then in the 4,335 trios from PGC. As expected given the robust over-transmission of the genome-wide autism PGS, most of the stratified partitions have a point estimate of over-transmission, and the degree of over-transmission increases with number of SNPs in the partition and size of the partition (Supplementary Fig. 4). To estimate the extent to which transmission of each region differed from expectation, we constructed a linear model regressing S-pTDT transmission on the number of SNPs in the partition and the length of the partition (Methods). This model yields a residual z-score for each partition, which estimates, in s.d., how much more or less transmission there is than there is expected relative to genome-wide patterns. Transmission of regional polygenic influences on autism is correlated between SSC + SPARK and PGC trios (r = 0.21, P < 1 × 10−10; Fig. 1b and Supplementary Fig. 5), which indicates stability in the S-pTDT rankings despite each partition including on average only 0.3% of all PGS variants, the vast majority of partitions containing no autism GWAS loci, and phenotypic and genetic heterogeneity among the iPSYCH autism GWAS and autism trio cohorts. Partitions that include autism GWAS loci are enriched among positive (z-score >0) S-pTDT scores in both SSC + SPARK and PGC trios: 29 of 46 (63%) partitions including an ASD GWAS locus are located in the top right quadrant, compared with expectation of 12 partitions (P = 1.7 × 10−8) (Fig. 1b, red points). This observation is consistent with the expectation that individuals with autism on average over-inherit alleles that increase the probability of autism diagnosis. Unexpectedly, partitions with large S-pTDT z-scores cluster on 16p (approximately 0–33 Mb; Fig. 1b, blue points): of the 12 partitions with the largest average S-pTDT z-score across SSC + SPARK and PGC, 5 are on 16p, whereas the other 7 localize to autism GWAS loci. The three partitions that are nominally S-pTDT enriched in both datasets (S-pTDT z-score >1.96) collectively cover the entirety of 16p (Fig. 1c). Given that the highly over-transmitted regions span 16p, we constructed a single new 33-Mb partition spanning the p-arm and compared it with the 73 other, nonoverlapping, 33-Mb regions found in the human genome (Fig. 1d, inset). The excess over-transmission at 16p becomes even more apparent in this framing, with an S-pTDT z-score in SSC + SPARK of 3.37 (Fig. 1d). In contrast, the same common variants at 16p are not over-transmitted to 1,509 unaffected siblings in SSC (S-pTDT z-score = −0.06, P = 0.95; Supplementary Fig. 6). Although 16p does not contain a genome-wide significant locus for autism (Supplementary Fig. 7), we nevertheless sought to determine whether the S-pTDT signal at 16p could be explained by one or a small number of common variant associations. We partitioned the 16p region into 25 adjacent blocks with low between-block linkage disequilibrium (median length: 1.3 Mb) and assessed the S-pTDT signal for each. Consistent with the absence of a single driving locus in the region, the association signal was diffuse (Supplementary Fig. 8) and decayed gradually with successive removal of the most over-transmitted blocks (Fig. 1e and Methods). Restated, the S-pTDT association at 16p does not appear to be driven by a single coding or regulatory locus in the region, but exists more diffusely across the 33-Mb segment of genome. We performed a number of additional analyses to further interrogate the S-pTDT finding at 16p. First, individuals with autism with a neurodevelopmental disorder-associated CNV on 16p (1.0% of individuals with autism in SSC + SPARK) did not drive the signal (Methods and Supplementary Fig. 9). Second, there was no association across the genome between S-pTDT ranking and either (1) presence of an autism-associated CNV (Supplementary Fig. 10) or (2) segmental duplication content of the region (Supplementary Fig. 11). Third, we queried the specificity of the S-pTDT finding at 16p to autism by performing an analogous analysis using a cohort of 1,634 attention deficit hyperactivity disorder (ADHD) trios and an external iPSYCH ADHD GWAS; we did not replicate the finding in ADHD (Supplementary Fig. 12 and Methods). Finally, we did not see evidence that the autism S-pTDT signal at 16p extends to the q-arm of chromosome 16 (Supplementary Fig. 13). In summary, over-transmission of autism’s polygenic influences at 16p is not driven by CNV carriers in our data, genomic structural features genome wide or as a crosstrait finding. We analyzed the gene composition of 16p in relation to the 73 other 33-Mb control regions and asked whether gene density could explain the S-pTDT signal (Methods). With 433 genes, 16p is the third most gene-dense region (Fig. 1f). Furthermore, with 62 genes specifically expressed in the brain, 16p ranks second highest relative to the other 33-Mb control regions and has 37% more than predicted by the number of total genes—the greatest excess of any region (P = 0.001; Fig. 1f). In contrast, 16p does not have a significant excess of genes implicated in autism from exome associations studies (P = 0.44; Supplementary Fig. 14). Given that 16p exhibits polygenic over-transmission and is gene dense, we tested the hypothesis that polygenic over-transmission reflects gene density. Across all 74 33-Mb partitions, S-pTDT was not related to density of all genes (r = 0.03, P = 0.83; Fig. 1f inset), brain-specific genes (r = 0.08, P = 0.48), constrained genes (r = −0.02, P = 0.85) or genes associated with autism via exome sequencing (r = 0.15, P = 0.21) (Supplementary Fig. 15). Moreover, we do not observe a trend for gene-dense regions having higher S-pTDT z-scores, for example, of the 13/74 partitions with >300 genes, the largest is 16p at z = 3.37 and the second largest at z = 1.32. We also did not observe a relationship between the S-pTDT signal and the density of fetal brain enhancers (r = 0.02, P = 0.88; Methods). This analysis suggests that although 16p is gene dense and enriched in brain-specific genes, these findings alone cannot explain a region’s degree of polygenic over-transmission. Finally, we sought to functionally characterize the genes on 16p. We did not observe an enrichment of genes differentially expressed between individuals with autism and controls in 16p (P > 0.68; Methods). We also performed gene ontology (GO) analysis of genes on 16p, but regional clustering of functionally related genes complicates interpretation (see Supplementary Table 1 for discussion). This challenge motivated us to pursue new functional approaches to characterize the genes on 16p.
Whereas the 16p11.2 CNV locus is 0.7 Mb, we observed S-pTDT signal across the entire p-arm of chromosome 16p. We hypothesized that the 16p11.2 deletion exerted effects on gene expression across 16p. A previous report with endogenous (nonengineered) 16p11.2 deletion lines noted differential expression effects extending up to 5 Mb from the 16p11.2 CNV. We sought to extend the analysis to the entire p-arm using engineered 16p11.2 deletions on an isogenic background. We analyzed a resource of clustered regularly interspaced short palindromic repeats (CRISPR)–Cas9-mediated heterozygous deletion of the 16p11.2 locus in induced plurippotent stem cells (iPSCs; n = 7 biological replicates). These iPSCs were differentiated into NGN2-induced neurons, and differential expression analysis was performed on the deletion lines relative to control neuronal lines without the 16p11.2 deletion (n = 6 biological replicates; Fig. 2a). We then asked whether, on average, the 200 neuronally expressed genes on 16p were differentially expressed in response to the 16p11.2 deletion (Methods). Genes on 16p had significantly lower expression in the deletion lines (mean log2(fold-change) = −0.015, P = 0.02; mean fold-change t-statistic = −0.16, P = 0.01; Fig. 2b). The deletion’s effect on 16p genes was different from the effect on all other 8,533 neuronally expressed genes in the genome (P = 0.02), the expression of which was not on average changed by the deletion (mean fold-change t-statistic = −0.01, P = 0.44) (Fig. 2c). In contrast, for the 189 genes on 16p with lower baseline neuronal expression (below all-gene median), expression did not significantly change in response to the 16p11.2 deletion (P > 0.2 for all comparisons; Supplementary Fig. 16). This analysis suggests that one of the most common autism-associated deletions is associated with transcriptional perturbation of genes in the surrounding region. Recurrent deletions at 15q13.3 are also observed in autism. To explore the specificity of our findings at 16p11.2, we explored the consequences of deletion of 15q13.3 in the same isogenic neuronal model (n = 11 heterozygous deletion replicates, n = 6 controls; Methods). In contrast to 16p11.2, 15q13.3 was not associated with transcriptional perturbation of 100 neuronally expressed genes in the surrounding region (mean log2(fold-change) = −0.01, P = 0.54; mean fold-change t-statistic = 0.09, P = 0.42) and was not different from the effect on all other 8,087 neuronally expressed genes (P = 0.37) (Supplementary Fig. 17). These results suggest that the transcriptional observations at 16p11.2 are not an artifact of the CRISPR-mediated deletion because the 15q13.3 and 16p11.2 models share experimental design. These results also suggest that the regional transcriptional effects observed at 16p11.2 are not shared across all autism-associated CNVs.
Our analysis of 16p11.2 deletion lines suggests that this genetic event causes transcriptional perturbation across 16p. Given that our S-pTDT analysis identified excess polygenic influence on autism across 16p, we tested the hypothesis that this common variant factor would also associate with decreased mean expression across 16p. We analyzed paired genotype and expression data from three sources. First, we drew on data from ongoing single-nucleus RNA-sequencing (snRNA-seq) analysis of prefrontal cortex (Brodmann area 46) from 122 European ancestry donors from the Harvard Brain Tissue Resource Center/National Institutes of Health (NIH) NeuroBioBank (HBTRC) (Supplementary Fig. 18); we performed our analyses in glutamatergic neurons because they were the most abundant cell type and the most similar to the induced neurons from the in vitro deletion analysis. Next, we analyzed paired genotype and bulk cortical RNA-seq from the CommonMind Consortium, split into two ancestry-specific subgroups (n = 193 individuals of African ancestry, n = 229 individuals of European ancestry) (Supplementary Fig. 19). Both the HBTRC and the CommonMind cohorts included donors with and without schizophrenia, and we controlled for this diagnostic status in our analyses. Within each cohort, we constructed regional PGSs for autism within the 33-Mb partitions described above and regressed average regional gene expression on the regional PGS (Methods). To increase power, and to be consistent across datasets, we restricted each of the three analyses to half the genes with the highest expression in glutamatergic neurons in the HBTRC data (n = 8,878 genes; Methods). Increased autism PGS within 16p was associated with decreased expression of genes throughout the 16p region (Fig. 3a; n = 183 genes, combined cohort permutation P = 0.03; Methods). Relative to the 73 other control regions, 16p had the second most negative association between regional PGS and mean gene expression (Fig. 3b). In addition, 16p exhibited by far the most consistently negative association between PGS and gene expression across the three cohorts (Fig. 3c). We performed two additional sensitivity analyses: first, we found a weaker association in the half of genes with lower expression in glutamatergic neurons (Supplementary Fig. 20) and, second, we showed the association to be robust to an alternative approach to controlling for sample ancestry (Supplementary Fig. 21). In summary, we observed across independent cohorts that increased 16p autism PGS is associated with an average decrease in gene expression within the partition.
Given that both the 16p11.2 deletion and 16p autism PGS are associated with decreased average gene expression in 16p, we asked whether these effects converged at the level of individual genes. We found a positive association between the per-gene expression effects of the 16p11.2 deletion and the 16p autism PGS across 168 glutamatergically expressed genes shared across both datasets (r = 0.18, P = 0.02; Fig. 4a and Supplementary Fig. 22). This observation suggests that the common variant 16p autism PGS and the rare variant 16p11.2 deletion share downstream functional impact on gene expression. We also note that genes with expression decreased in response to both the 16p autism PGS and the 16p11.2 deletion are enriched at the end of 16p (Ch16: 0–5.2 Mb, ‘telomeric region’; χ2 P = 0.003 for negative t-statistic in both cohorts and telomeric region location; Methods). This telomeric region of chromosome 16 is very gene dense—with 182 genes, it is the second most gene dense of 526 5.2-Mb regions in the genome. As with 16p more broadly, it is enriched in genes specifically expressed in adult cortex (n = 33, 83% more than expected by chance, P = 0.0002). The correlation in transcriptional effects associated with the 16p PGS and 16p11.2 deletion motivated us to explore genomic structural factors that could help to explain these coordinated effects across a large segment of the genome. We hypothesized that the 16p region may have increased within-region chromatin contact, which could explain the apparent nonindependence of genetic and expression variation on megabase scale. To examine chromatin contact within 16p, we used two published Hi-C datasets: a dataset of lymphoblastoid cell lines (LCLs) and a dataset from the primarily neuronal midgestational cortical plate. The i,jth entry of a Hi-C contact matrix estimates the degree of physical interaction between the ith and jth regions of the genome. We estimated contact within 33-Mb partitions as the mean of the off-diagonal values of the contact matrix. As segmental duplication content and gene density of the partition are associated with mean Hi-C estimates (Supplementary Fig. 23), we regressed them out to yield a per-partition z-score, which we interpreted as the Hi-C regional contact corrected for these genomic features. The 16p partition exhibits high levels of within-region contact in both cohorts: 4/74 highest partition in LCL (z-score: 1.50) and 2/74 highest in the cortical plate dataset (z-score: 2.05) (Fig. 4b). We hypothesize that this diffusely elevated within-region contact at 16p could facilitate the influence of regional polygenic effects on gene expression across 16p, via complex distal regulatory interactions. Our analysis of the in vitro 16p11.2 deletion neurons (Fig. 2b) revealed decreased gene expression at the gene-dense telomeric region of chromosome 16. We hypothesized that this is because the 16p11.2 locus has increased physical interaction with this telomeric region. Consistent with this hypothesis, in midgestational cortical plate Hi-C data, the 16p11.2–telomeric contacts (n = 291 100-kb × 100-kb contacts) are 2.9× more frequent than contacts between distance-matched control regions on 16p (n = 1,808 100-kb × 100-kb contacts, P = 5.8 × 10−12; Fig. 4c and Methods). In conclusion, these results suggest that the three-dimensional conformation of 16p may mediate convergent autism-related genetic effects on gene expression via regulatory interactions across megabases of separation.
Our observations motivate us to hypothesize the following model (Fig. 4d): genetic influence on autism emerges from the well-established 16p11.2 deletion and from common polygenic variation that is distributed across the region. Both of these influences are associated with an average decrease in cortically expressed genes across 16p and their expression effects are correlated at the gene level. We hypothesize that these transcriptional changes increase the likelihood of an autism diagnosis subsequent to the unusually large number of genes specifically expressed in the cortex at 16p. We also hypothesize that the region’s elevated internal chromatin contact may facilitate the transcriptional convergence of these two distinct influences. This hypothesis is consistent with work demonstrating that both single nucleotide and structural variation can cause transcriptional and chromatin perturbation. The distributed effect is also consistent with the results of a recent large-scale, exome-sequencing study of autism, which found that no single gene within the 16p11.2 locus was strongly associated with autism. Our model adds to a literature of multi-gene and genetic network effects associated with the 16p11.2 CNV and integrates common variation and chromatin architecture with 16p11.2 and the broader 33-Mb 16p region. Our analysis of large regions of genome is noncanonical in complex trait genetics, contrasting with a common approach focused on mapping disease-associated variants to the genes through which they act. Existing approaches such as transcriptome-wide association studies aggregate individually modest genetic effects on expression to associate genes with phenotype. In the present study, we aggregate both genetic effects on expression and effects across many genes in a region, increasing power to observe modest effects. Regional analysis also allows new perspectives into gene function, including the observation of a region enriched in genes specifically expressed in the brain or enriched in chromatin contact. Our results suggest that chromatin landscapes can facilitate convergent genetic and transcriptional effects within large regions of the genome. This insight supports the viability of a new approach for extracting biological insight from genetic association data across large genomic regions. Our observations raise many questions for future study. Why are the genetic and transcriptional associations at 16p related to autism? On the one hand, we found that the region harbors an unusual concentration of genes specifically expressed in the brain, but, on the other, not an unusual number of genes implicated in autism from exome association studies. We did not find a 16p signal in ADHD trios using the S-pTDT analysis, arguing against viewing 16p as equally relevant across neurodevelopmental traits. It is also possible that the genic relevance of the region will become apparent only through analysis of the biological networks into which 16p proteins interact and integrate; growing resources of protein–protein interaction data will facilitate this line of inquiry. The mean expression effects are modest, especially compared with the decrease in gene expression associated with heterozygous gene deletion such as that seen with the 16p11.2 CNV. Future studies will probe the biological consequence of modest expression changes spread across many genes. This analysis also raises the question of whether there are other regions of the genome in which common and rare variation converge in a similar fashion with relevance for either autism or other traits. In conclusion, our analysis presents a new statistical approach for partitioned polygenic association and uncovers surprising functional convergence of common and rare variant influences on autism at 16p.
We confirm that the present study was reviewed and approved by Massachusetts General Brigham institutional review board (IRB). The study name is Molecular Study of Cognitive and Behavioral Variation (IRB: 2015P002376). The principal investigator was E.B.R. The iPSYCH study was approved by the Danish Data Protection Agency and the Scientific Ethics Committee in Denmark.
For autism PGS analysis in autism trios, we used a GWAS from the iPSYCH collection in Denmark because there is no sample overlap with the autism trio samples (19,870 individuals with autism, 39,078 controls; Supplementary Table 2). For all other autism PGS analysis (for example, PGS–expression analyses), we used a meta-analysis of the same iPSYCH autism samples, plus autism samples from the PGC (combined: 26,067 individuals with autism, 46,455 controls). For analysis of ADHD, we used a nonoverlapping iPSYCH-only ADHD GWAS (25,895 individuals with ADHD, 37,148 controls). To generate PGS weights, we first applied LDpred v.1.0.11 on the marginal effect sizes from GWASs. We used LDpred under the infinitesimal genetic architecture model with LD reference from Hapmap 3 SNPs (n = 503 European ancestry samples). All PGSs were calculated using the --score function in PLINK 1.9 (ref. ). As LDpred estimates posterior causal effect sizes from GWAS marginal effect sizes, we include all SNPs in PGS analysis, including when constructing stratified PGSs for S-pTDT.
The collection, imputation and quality control of the SSC and Simons Foundation Powering Autism Research (SFARI) have been described previously (Supplementary Table 3). The autism trios from the PGC Autism group (PGC) are as described previously, with the modification of the inclusion of probands from multiplex families. We defined a European ancestry subset of PGC for analysis by generating principal components of ancestry using PLINK 1.9 and by visual inspection relative to HapMap reference populations (Supplementary Fig. 1). We defined a family as European ancestry if both parents and proband were of European ancestry by principal component analysis (PCA) (4,335 of 5,283 trios, 82%).
We performed genome-wide pTDT to assess power for S-pTDT analyses in SSC, SPARK and PGC. We estimated polygenic transmission as described previously, with the exception of an adapted approach for the case/pseudocontrol genotypes in PGC (Supplementary Note). The results for each of the three cohorts are displayed in Supplementary Fig. 2.
S-pTDT is identical to pTDT, except that, instead of testing for transmission of a PGS constructed from all SNPs, it tests for transmission of a PGS constructed from a subset of SNPs:where PGSS,C is the stratified PGS of child C and PGSS,MP is the mid-parent-stratified PGS (average of the two parents). S-pTDT is a one-sample two-sided Student’s t-test for whether the S-pTDT distribution has a mean different from 0. S-pTDT estimates of a given PGS for a given cohort is equal to the average S-pTDT value for all families in the cohort. We created stratified PGSs by dividing SNPs into sets of equal sizes. We varied this partitioning in two ways to complete a comprehensive survey of regional transmission: first, we divided the SNPs into partitions of varying sizes (2,000, 3,000, 4,000, 5,000 and 6,000 SNPs) and, second, we started the partitioning from either the beginning or the end of the chromosome. For example, for creating genomic blocks of 2,000 SNPs, we identified the first PGS SNP on chromosome 1 (the SNP closest to the first basepair), counted 2,000 PGS SNPs and defined this as the first partition. Then, we counted the next 2,000 PGS SNPs on chromosome 1, defined this as the next partition, until there were fewer than 2,000 SNPs remaining on chromosome 1. Next, we repeated the same procedure on chromosome 2 and for all remaining chromosomes. By varying the number of SNPs in the partition and whether SNP counting began at the start or the end of the chromosome, we produced 2,006 (often overlapping) partitions (1,003 from the start of chromosomes, 1,003 from the end of chromosomes). Before partitioning, we subsetted the PGS SNPs to those present in all three autism trios cohorts (SSC, SPARK and PGC) to avoid bias from SNP missingness across partitions. We then estimated stratified PGSs for each partition in each of the three cohorts using linear scoring (--score) in PLINK 1.9 and performed S-pTDT on each partition as described above. Partition length and SNP count were predictive of over-transmission (Supplementary Fig. 4). We regressed out expected over-transmission using a linear model—S-pTDT ≈ (number of PGS SNPs) + (length of partition in basepairs)—and normalized the model residuals by the s.d. of the model residual distribution. This procedure yields for each partition a residual z-score, which estimates the number of s.d.s by which the partition is over- or under-transmitted relative to expectation. If partitions included a gap between adjacent SNPs >1 Mb, we adjusted the contribution of that gap down to 1 Mb, which accounts for decay in LD but avoids inappropriately correcting the S-pTDT signal in the over-transmission model noted above. For analysis of 33-Mb partitions, the S-pTDT z-score regressed out only SNP number, because the basepair length of all partitions was the same.
First, we confirmed that autism-associated loci through GWAS were enriched in the S-pTDT distribution. We defined an autism-associated locus as the five loci from the most recently published autism GWAS reaching genome-wide significance from analysis of autism alone (index SNPs: rs910805, rs10099100, rs201910565, rs71190156 and rs111931861). Next, we analyzed the distribution of autism-associated CNVs in the S-pTDT distribution. We identified autism-associated CNVs from the set on SFARI Gene (https://gene.sfari.org/database/cnv) and then identified the S-pTDT partitions with at least one of these CNVs within the boundary (16p11.2, 16p13.11, 16p13.3, 16p12.2, 2p16.3, 15q13.3, 7q11.23, 17q12, 3q29, 1q21.1, 17p11.2, 8p23.1, 17q11.2, 2q11.2, 22q11.2, 22q13.3 and 5q35; Supplementary Fig. 10). We also estimated the association between segmental duplication content and S-pTDT for the 33-Mb partitions: we annotated each partition for segmental duplication rate by calculating the fraction of nucleotides in each partition that overlapped at least one segmental duplication per the University of California, Santa Cruz (UCSC) Genome Browser. Coverage calculations were performed using BEDTools v.2.30.0 (Supplementary Fig. 11). To rule out the contribution of 16p CNV carriers driving the S-pTDT signal, we repeated S-pTDT analysis in SSC + SPARK after removing trios where the proband carried an inherited or de novo neurodevelopmental disorder-associated CNV at 16p (we could not perform this analysis in PGC because we did not have exome sequencing for this cohort). We adopted a literature-based definition of neurodevelopmental disorder-associated CNVs from a recent autism sequencing study. Of the 5,048 trios in the SSC + SPARK analysis, we removed 51 (1.0%) with a qualifying CNV and repeated the S-pTDT analysis (Supplementary Fig. 9). We tested the hypothesis that S-pTDT relates to density of accessible chromatin in the developing human brain as tagged by H3K27ac histone marks. We analyzed a published resource of chromatin immunoprecipitation (ChIP)–sequencing profiling of two biological replicates of 7-week post-conception human cortex. Within each replicate, we summed the count of H3K27ac peaks within each of our defined 33-Mb partitions, scaled the counts to mean = 0 and s.d. = 1, and then averaged the z-scores between the two replicates. To evaluate the specificity of the S-pTDT finding on 16p, we performed an analogous analysis in ADHD. We used 1,634 European ancestry ADHD trios from the PGC and an external ADHD GWAS from the iPSYCH consortium with 25,895 individuals with ADHD and 37,148 controls. We partitioned the genome into blocks of 2,000, 3,000, 4,000, 5,000 and 6,000 SNPs as described above, starting from the beginning of chromosomes, and estimated the S-pTDT for each partition. We then estimated a residual z-score, regressing out the number of SNPs and partition size as in the autism analysis (Supplementary Fig. 12). We next evaluated whether the polygenic signal at 16p could be explained by a specific locus within the region. To perform this analysis, we partitioned 16p into 25 LD-independent blocks, each of approximately 1.5 Mb in size, as previously defined. We then estimated S-pTDT using the iPSYCH-only autism summary statistics in SSC and SPARK for each of these 25 blocks (Supplementary Fig. 8). To evaluate the contribution of individual loci, we estimated the decay in 16p S-pTDT signal as a function of removing the most over-transmitted remaining S-pTDT blocks. Specifically, we (1) estimated per block transmission, (2) ranked the blocks from most to least over-transmission, (3) estimated over-transmission using SNPs from all blocks, (4) estimated over-transmission using SNPs from all blocks minus SNPs from the most associated remaining block and (5) repeated step 4 until only a single block remained (Fig. 1e). For example, the first block (‘number of 16p partitions removed from PGS = 0’) includes all the 7,658 SNPs in the 16p PGS. The next block (‘number of 16p partitions removed from PGS = 1’) subtracts 287 SNPs from the most associated block in 16p, leaving this new block with 7,371 SNPs. We next evaluated the regional polygenic signal of 16p relative to equally sized comparison partitions across the genome. As 16p spans approximately 33 Mb of the genome, we constructed control partitions of 33 Mb by starting at the beginning of chromosomes and defining adjacent 33-Mb blocks (Supplementary Table 4). We defined the start coordinate of a chromosome by the minimum of (1) the first SNP in 1000 Genomes Phase 3 EUR and (2) the start position of first gene in gnomAD. Similarly, we defined the end coordinate of a chromosome by the maximum of (1) the last SNP in 1000 Genomes Phase 3 EUR and (2) and end position of the last gene in gnomAD. This approach yielded 74 partitions, including 16p. We performed S-pTDT using these boundaries by constructing stratified PGSs from all SNPs within a given partition.
We first compiled a consensus gene list for gene-density analyses. We defined this consensus list as the intersection of (1) autosomal genes with unique gene names and nonmissing pLI constraint estimates from gnomAD and (2) genes with estimated specific expression in Genotype-Tissue Expression (GTEx) cortex (‘Brain_Cortex’). This consensus list included 17,909 genes. We further annotated this list with the 102 genes implicated in autism via exome sequencing in a recent analysis. We then mapped genes to the above-defined 33-Mb boundaries if their gene body midpoint was located within the boundary. We built linear models predicting specific expression in the cortex (top 10% of specific expression t-statistic), from the density of all the genes, and calculated two-sided P values from the residual z-scores of the regression.
We performed GO analysis to evaluate enrichment of genes on 16p in annotated biological pathways (http://geneontology.org). We used the same 17,909 genes from the gene-density analysis as reference genes. We tested for enrichment of all genes on 16p (midpoint <33,000,000 bp, n = 433 genes) across three classes of annotations: biological process, molecular function and cellular component. The results for Bonferroni’s significant enrichments in each of the classes of annotations are reported in Supplementary Table 1.
We analyzed whether genes on 16p are overrepresented in differentially expressed genes between individuals diagnosed with autism and controls. We used a previously published differential expression dataset of human brain RNA-seq (n = 51 individuals with autism, n = 936 controls). We defined genes as differentially expressed at Bonferroni’s significant level correcting for the number of genes overlapping between the dataset and our consensus gene list described above (n = 15,288 genes, n = 83 differentially expressed genes, n = 383 genes on 16p). We performed a χ2 test for enrichment of differentially expressed genes on 16p.
Design of the CRISPR-mediated deletion of the 16p11.2 loci and differential expression analysis is described in a published resource. Design of the 15q13.3 CNV deletion lines followed the same protocol, with the deletion defined with boundaries Ch15 30,787,764-32,804,328 (GRCh37). We generated n = 11 heterozygous deletion lines, with an additional n = 6 controls exposed to CRISPR construct but not to guide RNA. When estimating the effect of the deletion on the region, we excluded genes ±100 kb of the deletion window, because the cis-regulatory regions of these genes may have been perturbed by the creation of the deletion itself.
We generated paired genotype and single-nucleus expression profiles from the dorsolateral prefrontal cortex (DLPFC) of postmortem brain tissue from the HBTRC/NIH NeuroBioBank. The generation of expression profiles will be described in detail in a forthcoming manuscript from the authors of the present report. In brief, we developed and optimized a workflow for creating and analyzing pools of nuclei sampled from brain tissue (DLPFC, BA46) from 20 different donors per pool. In this workflow, we started by dissecting a defined amount of tissue from each donor, obtaining a similar mass of tissue from each specimen while being careful to represent all cortical layers. The frozen tissue samples were then immediately pooled for simultaneous isolation of their nuclei; all subsequent processing steps, including nuclear isolation, encapsulation in droplets and preparation of snRNA-seq libraries, involve all of the donors together. This ‘dropulation’ workflow allows us to minimize experimental variability, including any technical effects on messenger RNA ascertainment and any effects of cell-free ambient RNA. Each nucleus in these experiments was then reassigned to its donor of origin using combinations of hundreds of transcribed SNPs; although the individual SNP alleles are shared among many donors, the combinations of many SNPs are unique to each donor in the cohort. Nuclei were assigned to seven major cell classes (astrocytes, endothelial cells, γ-aminobutyric acid (GABA)-ergic neurons, glutamatergic neurons, microglia, oligodendrocytes and polydendrocytes) by global clustering and identification of marker genes expressed in each cluster. Median cell-type proportions were: glutamatergic neurons 47.9%, GABA-ergic neurons 18.8%, astrocytes 13.5%, oligodendrocytes 8.0%, polydendrocytes 5.2%, microglia 1.5% and endothelia 1.0%. All downstream analyses used expression data from glutamatergic neurons. The cell type-specific gene-by-donor expression matrices were processed with VST normalization. We performed a number of pre-association quality control (QC) steps. The majority of genotyped samples were European ancestry (1,707/1,770, 96%) and we identified these samples for downstream analysis using PCA (Supplementary Fig. 18). Next, we identified any samples as expression outliers with mean expression >3 s.d. from the cohort mean (3/125 samples). This yielded a final EUR subset of 122 samples. We identified genes with expression above median across the 122 samples in the count matrix, with the only processing step of normalization of expression count sum equal for all samples. We used these genes (n = 8,878) in all subsequent regional PGS–expression analyses.
We next analyzed paired genotype and bulk DLPFC expression data from donors in the CommonMind consortium. Generation of expression count matrices is described in the CommonMind publication. Within CommonMind, we restricted analysis to donors from the National Institute of Mental Health (NIMH) Human Brain Collection Core (HBCC) and the University of Pittsburgh (PITT) biobanks due to previous analysis demonstrating increased concordance with the snRNA-seq resource described above. We performed variance stabilization on the count matrices separately in HBCC and PITT using the SCTransform package in Seurat with the goal of closely paralleling the approach of the single-nucleus resource (parameters: do.scale = FALSE, do.center = FALSE, return.only.var.genes = FALSE, seed.use = NULL, n_genes = NULL). The CommonMind collection is ancestrally heterogeneous: the two largest groups are African and European ancestry donors. Accordingly, we used PCA to identify donors of African ancestry (n = 193) and European ancestry (n = 229) and subsequently analyzed each separately (Supplementary Fig. 19). For consistency with the single-nucleus resource, we restricted analysis to donors diagnosed with schizophrenia or controls.
For all samples, we calculated regional autism PGC using the largest autism GWAS (iPSYCH + PGC; see Supplementary Table 2) using Plink 1.9 score with a genotype QC (SNP missingness <1%, minor allele frequency >0.1%, imputation INFO >95%). We performed two classes of local PGS–gene expression association. The first class is a per-gene association, as in Fig. 3a. The second is average gene association, as in Fig. 3b,c. To be consistent across datasets, we restricted all analyses to half the genes most expressed in glutamatergic neurons in the HBTRC data (n = 8,878 genes). The association in Fig. 3a is a per-gene association meta-analyzed across the three cohorts. We combined individual-level expression and genotype PGS across the three cohorts; before concatenating the PGSs and expression matrices used in the individual cohort analyses, we within-cohort scaled per-gene expression and per-partition PGS to mean = 0 and s.d. = 1. Per-gene association followed the linear model: gene expression ≈ regional PGS + schizophrenia diagnostic status + ancestry (binary for yes/no African ancestry) + single cell (binary yes/no). The association t-statistic is from the regional PGS covariate. For maximum power to detect mean effects, we assessed significance of the mean PGS–expression association using permutation. Specifically, we calculated the mean(t-statistic) in 16p, then shuffled the PGS–donor IDs within each cohort, performed association and calculated the mean(t-statistic), repeated 1,000×. The permutation P value is the number of times the observed PGS was more negative than the permuted PGS. For the second class of association, we first averaged the gene expression per partition, then performed the association. For per-cohort association, we used the linear model: mean expression of gene ≈ regional PGS + schizophrenia diagnostic status. For combined analysis, we used the linear model: mean gene expression ≈ regional PGS + schizophrenia diagnostic status + ancestry (binary for yes/no African ancestry) + single cell (binary yes/no). We performed a sensitivity analysis for genetic ancestry using the principal components in Supplementary Fig. 21.
Per-chromosome Hi-C count matrices were downloaded from http://hic.umassmed.edu at 1-Mb resolution for GM06990 LCL. As the count matrices were built in hg18, we converted the 33-Mb partitions from hg19 to hg18 using the National Center for Biotechnology Information’s (NCBI’s) Genome Remapping Service (www.ncbi.nlm.nih.gov/genome/tools/remap). We matched the boundaries of the 33-Mb partitions with their closest 1-Mb cutoffs in the count matrix. For this analysis, we did not analyze partitions spanning centromeres, yielding 56 partitions for analysis. For each partition, we estimated raw within-partition contact frequency as the mean of the off-diagonal elements of the Hi-C count matrix.
We downloaded 0.1-Mb resolution, Hi-C contact matrices from NCBI’s Gene Expression Omnibus (GEO) from a resource of midgestational cortical plate samples from three donors. As above, we mapped the boundaries of the 33-Mb partitions to the 0.1-Mb boundaries of the Hi-C matrix. In contrast to the LCLs, the diagonal elements were zeroed out; thus, the estimated raw within-partition contact frequency was estimated as the mean of all elements of the matrix. We analyzed the same 56 partitions as in the LCL analysis. As our hypothesis pertained to average contact behavior over large regions of the genome, as opposed to more fine-grained analysis of topologically associated domains or gene–enhancer interactions, we analyzed the largest bin window available within each cohort to increase the signal:noise ratio.
Raw within-partition contact frequency varies with gene density and segmental duplication contact (Supplementary Fig. 23). In the LCLs, this covariance is probably due to an increased number of Hi-C reads mapping to regions with increased segmental duplication content. In cortical lines, there are large chunks of zeroed-out elements of the contact matrix, rates of which correlate strongly with segmental duplication content, probably due to intentional zeroing of elements in regions that are difficult to map because of segmental duplication content. Gene density remains a significant predictor of contact frequency after conditioning on segmental duplication content, motivating us to condition on-gene density as well and to extract normalized residuals from the following model: contact frequency ≈ gene count + segmental duplication content. Our primary analysis in Fig. 4b reports these normalized residuals for each partition.
We next analyzed the chromatin contact between the 16p11.2 locus and the distal gene-dense start of chromosome 16. We performed this analysis in the midgestational cortical plate data only because the 1-Mb bin resolution of the LCL resource did not have sufficient resolution. We defined the telomeric region based on the gene-dense segment at the start of chromosome 16, from 0 Mb to the closest 100-kb segment after the endpoint of the final brain-expressed gene in that window in the 16p11.2 deletion dataset (5.2 Mb). We defined the 16p11.2 locus as Ch16: 29.5–30.2 Mb. To define control contact regions, we first calculated the minimum (24.3 Mb) and maximum (30.2 Mb) distances spanned by the contact matrix defined by 0–5.2 Mb (telomeric region) and 29.5–30.2 Mb (16p11.2 locus). We then defined control contacts on 16p as all contacts of distance >24.3 or <30.2 that were not located in the telomeric–CNV contact range described above. These results are robust to inclusion of elements of the contact matrix with ‘0’, which probably reflects segmental duplication-rich regions (telomeric–CNV versus control P < 1 × 10−10 for both approaches).
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Any methods, additional references, Nature Research reporting summaries, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01203-y.
Supplementary InformationSupplementary Figs. 1–23, Note and Tables 1–4. Reporting Summary Peer Review File |
PMC9649441 | 36333500 | Stefan Butz,Nina Schmolka,Ino D. Karemaker,Rodrigo Villaseñor,Isabel Schwarz,Silvia Domcke,Esther C. H. Uijttewaal,Julian Jude,Florian Lienert,Arnaud R. Krebs,Nathalie P. de Wagenaar,Xue Bao,Johannes Zuber,Ulrich Elling,Dirk Schübeler,Tuncay Baubec | DNA sequence and chromatin modifiers cooperate to confer epigenetic bistability at imprinting control regions | 04-11-2022 | Epigenetics,High-throughput screening,Gene regulation | Genomic imprinting is regulated by parental-specific DNA methylation of imprinting control regions (ICRs). Despite an identical DNA sequence, ICRs can exist in two distinct epigenetic states that are memorized throughout unlimited cell divisions and reset during germline formation. Here, we systematically study the genetic and epigenetic determinants of this epigenetic bistability. By iterative integration of ICRs and related DNA sequences to an ectopic location in the mouse genome, we first identify the DNA sequence features required for maintenance of epigenetic states in embryonic stem cells. The autonomous regulatory properties of ICRs further enabled us to create DNA-methylation-sensitive reporters and to screen for key components involved in regulating their epigenetic memory. Besides DNMT1, UHRF1 and ZFP57, we identify factors that prevent switching from methylated to unmethylated states and show that two of these candidates, ATF7IP and ZMYM2, are important for the stability of DNA and H3K9 methylation at ICRs in embryonic stem cells. | DNA sequence and chromatin modifiers cooperate to confer epigenetic bistability at imprinting control regions
Genomic imprinting is regulated by parental-specific DNA methylation of imprinting control regions (ICRs). Despite an identical DNA sequence, ICRs can exist in two distinct epigenetic states that are memorized throughout unlimited cell divisions and reset during germline formation. Here, we systematically study the genetic and epigenetic determinants of this epigenetic bistability. By iterative integration of ICRs and related DNA sequences to an ectopic location in the mouse genome, we first identify the DNA sequence features required for maintenance of epigenetic states in embryonic stem cells. The autonomous regulatory properties of ICRs further enabled us to create DNA-methylation-sensitive reporters and to screen for key components involved in regulating their epigenetic memory. Besides DNMT1, UHRF1 and ZFP57, we identify factors that prevent switching from methylated to unmethylated states and show that two of these candidates, ATF7IP and ZMYM2, are important for the stability of DNA and H3K9 methylation at ICRs in embryonic stem cells.
Epigenetic regulation of gene activity depends on multiple layers of chromatin modifications that are maintained during DNA replication. By definition, these epigenetic mechanisms act independently of the DNA sequence at the genomic sites they occupy. However, several studies have highlighted a contribution of DNA sequence to the regulation and maintenance of chromatin modifications, preventing a clear distinction between epigenetic and genetic control of gene activity. Genomic imprinting is an epigenetic phenomenon, where DNA methylation marks on either the maternal or paternal ICRs dictate parental-specific activity of transcripts in cis. ICRs inherit parental-specific DNA methylation marks from either the oocyte or sperm, which are then propagated in all somatic tissues of the next generation. The inheritance of differential epigenetic states on the parental chromosomes, despite identical DNA sequence, identical chromosomal location and exposure to the same regulatory factors in the nucleus, make ICRs a great model to study the individual contribution of DNA sequence and chromatin modifications to epigenetic memory. Several factors and mechanisms have been identified that regulate the maintenance of DNA methylation at ICRs. Once methylation marks have been deposited in the germline, the maintenance methyltransferase DNMT1 and its accessory protein UHRF1 are responsible for the maintenance of methylation during DNA replication. In addition, several factors have been identified to regulate H3K9me3 at the DNA-methylated ICRs, including SETDB1, KAP1 and G9A. Importantly. the KRAB zinc-finger factor ZFP57 binds the methylated hexanucleotide DNA sequence TGCmCGC and recruits KAP1 and other associated factors to establish a feedback between DNA methylation and H3K9me3 at ICRs. Indeed, binding of ZFP57 and recruitment of KAP1 are crucial steps in regulating imprints, as knockout (KO) of Zfp57 in mice results in loss of almost all imprints and embryonic lethality, and ZFP57 is required for maintenance of DNA methylation and H3K9me3 at ICRs in cellular systems. Although the factors that control DNA and histone methylation at ICRs have been widely investigated, the DNA sequence properties of ICRs have not been explored in detail. Furthermore, it is also not known if additional key players contribute to the epigenetic maintenance at ICRs. By iterative integration of ICR DNA sequences to the same genomic site in mouse embryonic stem cells (mESCs), we show that ICRs are autonomous genetic elements that can recapitulate the epigenetic states observed at the endogenous locations. Using this setup, we show that by presetting DNA methylation, we can establish two opposing epigenetic states that are faithfully propagated by the ectopic ICR. This DNA-methylation-dependent switch is unique to ICRs. Systematic integrations of variant and synthetic ICRs allowed us to identify the sequence requirements that are necessary and sufficient for this switch-like behavior. Furthermore, by using the ectopic ICRs as DNA-methylation-sensitive reporters in loss-of-function genetic screens, we confirm DNMT1, UHRF1 and ZFP57 as the core epigenetic regulators of genomic imprinting. In addition, we identify ATF7IP and ZMYM2 as factors involved in regulating maintenance of epigenetic states at ICRs.
We hypothesized that the DNA sequence of ICRs should contain sufficient information to establish and maintain the distinct epigenetic states observed on the parental alleles (Extended Data Fig. 1a). We selected four ICRs from the Airn, Kcnq1ot1, Zrsr1 and H19 imprinting clusters and used recombinase-mediated cassette exchange (RMCE) to integrate them individually into the genome of mESCs (Fig. 1a). To mimic the differential DNA methylation states of the ICRs, we performed RMCE in parallel for unmethylated ICRs and ICRs that were premethylated by the bacterial CpG methyltransferase M.SssI (Fig. 1a and Extended Data Fig. 1b). As a control sequence, we used the Igf2r DMR (differentially methylated region), a promoter that acquires differential DNA methylation only during differentiation. Furthermore, we included a set of inactive gene promoters (Hes3, Tcl1 and Syt1), which were previously shown to be protected from de novo DNA methylation when integrated to the same RMCE site (Fig. 1b). After successful integration, we measured DNA methylation at the RMCE site by bisulfite conversion PCR (bsPCR). All four ICRs maintained their preestablished DNA methylation status at the ectopic site, although in some cases, minor de novo methylation at the unmethylated ICRs was observed (Fig. 1b,c and Extended Data Fig. 1c–e). In contrast, maintenance of preestablished DNA methylation was not observed for the Igf2r DMR and the control promoter elements (Fig.1b and Extended Data Fig. 1f–i). The differential DNA methylation states at the ectopic Airn ICR were stably maintained after prolonged cultivation of mESCs for more than 20 passages, or upon integration to a different RMCE position in the genome, and also following in vitro differentiation of mESCs to neuronal progenitors (Extended Data Fig. 2a–c). Furthermore, DNA methylation of the ectopic Airn ICR was still retained at high levels after cultivation of mESCs in 2i medium for 10 days, despite the global reduction in 5-methylcytosine resulting from acquiring a naïve stem cell state (Extended Data Fig. 2a,d). Besides DNA methylation, endogenous ICRs further display differential histone modifications (Extended Data Fig. 1a), whereby the methylated ICR is decorated by H3K9me3 and the unmethylated ICR by H3K4me2 (refs. ). We performed chromatin immunoprecipitation (ChIP) quantitative PCR (qPCR) for H3K9me3 and H3K4me2 and compared the enrichment of these marks at the RMCE integrations of the Airn and Kcnq1ot1 ICRs with their endogenous counterparts (Fig. 1d). The unmethylated ICRs at the RMCE site showed lack of H3K9me3 and increased H3K4me2, whereas the premethylated ICRs revealed the opposite pattern, with increased H3K9me3 and absence of H3K4me2 (Fig. 1d). Previous studies identified the DNA-methylation-specific KRAB-Znf protein ZFP57 to be required for maintenance of DNA methylation and H3K9me3 at endogenous ICRs. This regulation is recapitulated at the ectopic ICR, as CRISPR-Cas9 deletion of Zfp57 in mESCs results in rapid and complete loss of DNA methylation at both ectopic and endogenous sites (Extended Data Fig. 2e).
We set out to test if the ICR DNA sequence is required for epigenetic memory. First, we aimed to identify if smaller ICR fragments would also efficiently memorize preset DNA methylation patterns and repeated the same experiments with four smaller fragments from the Airn ICR (Fig. 2a and Extended Data Fig. 2f). None of the tested fragments could faithfully recapitulate the differential methylation maintenance. The same was observed for the paternally methylated H19 ICR (Extended Data Fig. 2g,h). Previous studies focusing on non-ICR regulatory regions (promoters, CpG islands or enhancers) have revealed that CpG density, GC content and/or nucleotide sequence can influence establishment of DNA methylation patterns. Based on their CpG density and GC content, the ICRs tested here are in the range of genomic elements overlapping with unmethylated CpG island promoters (Extended Data Fig. 3a). To investigate if the CpG density and GC content of the ICRs contribute to the maintenance of methylated and unmethylated states, we selected four genomic regions that are highly similar to the Airn ICR in size, GC%, CpG number and distribution (Extended Data Fig. 3b–d). These ‘Airn-like’ elements failed to maintain the differential methylation and adopted a hypomethylated state like their endogenous counterpart, suggesting that DNA sequence length, CpG density and GC content are not sufficient to establish two distinct epigenetic states (Extended Data Fig. 3e,f). To further distinguish the direct requirement of DNA sequence from CpG and GC content, we generated a synthetic DNA element based on the Airn ICR sequence, where we permutated the inter-CpG DNA sequences until 78% mismatch was reached (Extended Data Fig. 4a,b). Importantly, the permutation of the original sequence retained the local GC content and the number and position of the original CpGs. This replacement removed all inter-CpG DNA sequence information, allowing us to distinguish the contribution of DNA sequence from CpG frequency and distribution. We repeated the RMCE experiments with this ‘shuffled’ Airn ICR and observed that it failed to maintain the preset epigenetic state (Fig. 2b). In both cases, DNA methylation reached an intermediate value of 40.3% for the unmethylated and 23.5% for the premethylated insertion, with disordered methylation patterns (Fig. 2b). The sequence alterations further led to reduced establishment of the H3K9me3 and H3K4me2 at the RMCE site, independently of the preset methylation state (Fig. 2c). The shuffling of the inter-CpG DNA sequence in the Airn ICR disrupted all ZFP57 binding motifs, which might explain the observed lack of maintenance, in agreement with an in silico evaluation of ZFP57 binding to wild-type and shuffled Airn ICR sequence using BPNet (Extended Data Fig. 4c,d). Accordingly, we wanted to investigate if ZFP57 motifs are sufficient for the maintenance of the epigenetic state. Therefore, we restored the ZFP57 binding motifs in the shuffled ICR (Extended Data Fig. 4e) and introduced this methylated and unmethylated DNA element to the RMCE site in mESCs. Although the unmethylated version failed to maintain the hypomethylated state, the premethylated ICR was able to maintain a fully hypermethylated state (Fig. 2d). Given these observations, we wondered if the requirement for ZFP57 binding sites is dependent on the cellular context, especially as Zfp57 gene expression is tissue specific. Therefore we introduced the shuffled Airn ICR to RMCE-competent mouse erythroleukemia (MEL) cells and performed targeted bisulfite sequencing. Both methylated and unmethylated shuffled ICRs retained the preset DNA methylation patterns (Fig. 2e), indicating that in MEL cells, CpG content is sufficient for the memory of DNA methylation states.
Endogenous ICRs are cis-regulating elements that dictate the allelic expression of nearby transcripts based on their DNA methylation state. We first wanted to test if ICR sequences can silence three different reporter constructs in presence of DNA methylation when integrated together to the RMCE site (Fig. 3a and Extended Data Fig. 5a). We selected three commonly used constitutive promoters (pCAGGS, hEF1alpha and hPGK) and showed that they can maintain expression of a GFP reporter at the RMCE integration site in absence of ICRs (Extended Data Fig. 5b). Next, we measured the ability of three methylated ICRs (Airn, Kcnq1ot1 and Peg10) to stably repress these promoters at the same RMCE integration site (Fig. 3b). All tested ICR sequences showed stable repression in combination with the Ef1alpha and hPGK promoters. In contrast, the methylated promoters without ICRs, or in combination with the Dazl promoter, which is known to be regulated in a DNA-methylation-dependent manner, were not able to maintain a repressed state (Fig. 3b). The synthetic pCAGGS promoter gave varying results, depending on the used ICR, suggesting that the strength of this composite promoter can overcome the epigenetic repression induced by some ICRs (Fig. 3b). The DNA-methylation-dependent repression was maintained over longer periods, as measured by GFP activity in multiple clonally derived populations after 16, 23 and 30 days (Extended Data Fig. 5c). The same methylated ICR-dependent repression was observed for the paternally methylated H19 ICR (Extended Data Fig. 5d). This setup allowed us to test the contribution of DNA methylation and sequence on the silencing potential of ICRs. For this, we made use of the Airn-pEF1a-GFP reporter construct that showed stable maintenance of GFP expression when inserted unmethylated and stable silencing when inserted methylated (Fig. 3c). When we replaced the Airn ICR with the shuffled Airn version, we observed loss of silencing in most of the measured clones already after 16 days and even more after prolonged cultivation, suggesting that methylation-dependent silencing in cis requires an intact ICR sequence (Fig. 3c). Finally, we introduced the shuffled Airn sequence containing reconstituted ZFP57 binding sites. Although the unmethylated version led to stochastic loss of transcriptional activity, the methylated construct gave rise to stable repression of the nearby promoter for multiple generations, indicating that ZFP57 binding is not only required for the maintenance of epigenetic memory at ICRs but also sufficient for epigenetic silencing in cis (Fig. 3c). To test if the DNA methylation of ICRs is required for the repression of the nearby promoter, we challenged the established reporter cell lines by culturing them in 2i and 2i + vitamin C media. Both conditions reduce genome-wide DNA methylation levels, whereas addition of vitamin C results in further removal of DNA methylation from ICRs and repetitive elements. GFP repression was maintained in 2i medium; however, repression was progressively lost in presence of 2i + vitamin C (Extended Data Fig. 5e–g). To further test the dependency on DNA methylation for maintaining the repressed state at the ICR reporters, we performed KO experiments of the general DNA methylation maintenance factors Uhrf1 and Dnmt1 (ref. ). As expected, removal of DNA methylation in these KO cells led to a reactivation of the ICR reporter within 7 days (Extended Data Fig. 6a,b). The low percentage of cells that show GFP reactivation in these assays is due to low KO efficiency in the CRISPR-targeted pool of cells. Therefore, we cultured the Airn-ICR reporter in presence of the DNMT1 inhibitor GSK-3484862 (ref. ) for 2 days. We observed complete reactivation with over 95% of cells expressing GFP (Fig. 3d and Extended Data Fig. 6c). The use of this DNMT1 inhibitor further allowed us to test if the reactivation is reversible; therefore, we removed GSK-3484862 from the medium and continued cultivation for 7 more days after washout (Fig. 3d and Extended Data Fig. 6c). We observed no resilencing of activated reporters, indicating that once the ICR is switched on, it cannot revert to a silent state.
After establishing multiple ICR-specific reporter cell lines, we wanted to screen for proteins required for maintenance of repressive ICR states. We first established the CRISPR screen workflow using a targeted library against 1,051 chromatin-related factors with 6,204 guide RNAs (ChromMM library) and a control library with 500 non-targeting guides in the pCAGGS-Airn reporter cell line (Fig. 4a and Extended Data Fig. 7a), and we determined the time point to collect positive clones (Extended Data Fig. 7b). We performed the screen in three methylated ICR reporter lines (Airn, Kcnq1ot1 and Peg10) and collected GFP-positive cells after 8 days and repeated the screen for Airn, Kcnq1ot1 in sensitized 2i medium conditions (Extended Data Fig. 7c,d). As expected, the three positive controls Zfp57, Uhrf1 and Dnmt1 scored as the top hits in all screens (Fig. 4b,c, Extended Data Fig. 8a–c and Supplementary Table 1). Additionally, other heterochromatin-associated factors like Cbx1, Cbx5, Atrx, Daxx and Setdb1 were enriched in the GFP-positive fraction. The list of high-confidence hits that were repeatedly found in all screens was enriched for Zfp57, Uhrf1 and Dnmt1, whereas other hits were identified in individual ICR reporter cell lines (Fig. 4c). We redesigned an extended CRISPR library (EpiTF) consisting of 20,470 guide RNAs against 4,095 genes encoding nuclear factors to cover a large fraction of the KRAB zinc-finger protein family and repeated the screen using the Airn ICR reporter (Supplementary Table 1). Despite the increased complexity of the library, we did not identify additional transcription factors to play a role in the maintenance of Airn reporter silencing (Fig. 4c and Extended Data Fig. 8d,e). Several candidates identified in more than one screen were tested by single-KO validation. Zfp57, Uhrf1 and Dnmt1 showed consistent upregulation in all three reporter lines, whereas other candidates resulted in lower or stochastic reactivation in some of the tested reporter lines (Extended Data Fig. 8f).
Two factors were identified in at least three different screens (Fig. 4c and Extended Data Fig. 8g): ATF7IP, responsible for SETDB1-mediated silencing of transposable elements, as well as ZMYM2, an ATF7IP-interacting factor associated with growth restriction of human pluripotent cells. Given their association with H3K9me3 and reported involvement in transcriptional silencing of repetitive elements, we tested their contribution to regulation of epigenetic maintenance at ICRs. In addition human ATF7IP was recently identified to be a repressor of paternally expressed imprinted genes and required for silencing sperm-specific genes. We first wanted to see if these factors indeed localize to the endogenous ICRs and analyzed existing mESC ChIP-seq datasets available for SETDB1 (ref. ), ZFP57 (ref. ), ATF7IP and ZMYM2 (ref. ). We observed a strong colocalization of all factors at the endogenous ICRs used in the CRISPR screens (Fig. 5a). By further expanding our analysis to all annotated ICRs, we see that almost all ICRs are co-bound by ATF7IP, ZMYM2, ZFP57 and SETDB1 (Fig. 5b). Notable exceptions are MCTS2/H13, where ATF7IP is absent, and H19, which shows a reduced localization of ZMYM2. As a general trend, we observe that ATF7IP and ZMYM2 always colocalize in presence of SETDB1 and ZFP57, suggesting that they localize to ICRs as part of the H3K9me3 machinery. Interestingly, genome-wide analysis of ATF7IP, ZMYM2, ZFP57 and SETDB1 peaks indicates that this colocalization is not always observed outside of ICRs. Although the majority (85%) of the few ATF7IP peaks that we detected overlap with ZFP57 and SETDB1 sites, only 30% of ZMYM2 peaks colocalize with ZFP57 and SETDB1 (Extended Data Fig. 9a). ZMYM2 peaks outside of ZFP57/SETDB1 sites show lower H3K9me3 and DNA methylation compared to peaks overlapping with ZFP57/SETDB1, suggesting that ZMYM2 is involved in multiple regulatory pathways independently of SETDB1 (Extended Data Fig. 9b,c). Regardless of this binding, we see a reduction of ATF7IP and ZMYM2 localization to the Airn ICRs in absence of ZFP57 (Extended Data Fig. 9d). To further interrogate the link between ATF7IP and ZMYM2 at ICRs, we recruited the proximity biotin ligase TurboID to methylated ICRs via a ZFP57-TurboID fusion protein expressed from the RMCE site and performed BioID as previously described (Fig. 5c). As a background control, we generated a cell line expressing just the NLS-TurboID (nTurbo) and included a cell line expressing only the KRAB domain of ZFP57 fused to the TurboID ligase to distinguish between proteins that interact with ZFP57 when not bound to chromatin. Mass-spectrometric detection of enriched proteins included several factors previously associated with ZFP57 (KAP1, CBX3, CBX5 and MORC3). Among them, we detected ATF7IP (Fig. 5c, Extended Data Fig. 9e and Supplementary Table 1), supporting the results obtained from the CRISPR screen and genome-wide analysis. In the case of ZMYM2, we could not detect the protein in the biotinylated fraction or the background sample, suggesting that its enrichment was either below the detection limit or not specifically interacting with ZFP57.
Next, we wanted to test if absence of these factors that are expressed during early mouse development influences the epigenetic state at endogenous ICRs, and we generated KO mESCs for Atf7ip and Zmym2 using CRISPR-Cas9 (Extended Data Fig. 9f,g). Whole-genome bisulfite sequencing (WGBS) revealed a reduction of DNA methylation at the majority of analyzed ICRs, despite limited loss of methylation genome-wide (Fig. 5d and Extended Data Fig. 10a–c). Peg13 and Meg3/Rian ICRs retained DNA methylation in both KO cell lines, whereas H13/Mcts and Gnas/Nespas specifically retained methylation in absence of ZMYM2 and Zrsr1/Commd1 and H19 in absence of ATF7IP. Loss of ICR methylation was further confirmed by targeted bisulfite sequencing around the binding sites of ATF7IP and ZMYM2 at the Airn, Kcnq1ot1 and Peg10 ICRs (Extended Data Fig. 10d). Finally, we profiled H3K9me3 in the same KO cell lines and observed loss of H3K9me3 at all ICRs, except for Peg13 and Meg3/Rian, which retained H3K9me3 in both KO lines. In addition, Zrsr1/Commd1 and H19 retained H3K9me3 in Atf7ip KO cells (Fig. 5e and Extended Data Fig. 10e). The concordant changes in DNA methylation and H3K9me3 at ICRs in the absence of ATF7IP or ZMYM2 indicate that these factors are required for preventing switching of ICRs from methylated to unmethylated states in mESCs.
Here, we set out to study the genetic and epigenetic determinants that allow ICRs to maintain their differential DNA methylation. Toward this, we isolated ICRs from their endogenous chromosomal context and inserted them into a heterologous position in the mESC genome. When integrated unmethylated, the tested ICRs maintained a DNA-methylation-free and euchromatic state, suggesting sequence-specific mechanisms that prevent de novo methylation. This behavior is similar to CpG island promoters, which are protected from DNA methylation through elevated CpG density. Indeed, based on their CpG density and GC percentage, most ICRs fulfil the definition of CpG islands. In contrast, integration of DNA-methylated ICR sequences to the same site overwrites this default state and leads to stable propagation of DNA methylation with subsequent establishment of heterochromatin marks. Thus, ICRs are autonomous DNA sequence elements that can recapitulate the epigenetic regulatory mechanisms observed at their endogenous position. This finding is in line with previous work indicating that the DNA sequence of ICRs is sufficient to recapitulate the establishment of imprints during mouse development. Importantly, this switching between two opposing chromatin states based on DNA methylation was not observed for non-ICR promoters and other DNA sequences of similar size, CpG density or GC content, suggesting that specialized properties of the full-length ICR are required for this ‘epigenetic bistability’. The ectopic ICRs enabled to systematically study the DNA sequences and chromatin regulatory factors required for creating and maintaining epigenetic memory at ICRs in a controlled genomic environment. Through introducing synthetic ICRs with modified DNA sequences, we observe that GC content and CpG density is not sufficient for encoding bistability in mESCs but that additional sequences, such as ZFP57 binding motifs, play an important role in maintaining DNA and H3K9 methylation. This finding is in line with previous work, where mutations of the methylated CpGs of the ZFP57 recognition motif resulted in loss of methylation maintenance over the entire Snrpn ICR. In addition, we show that ZFP57 binding is not only required but also sufficient for the epigenetic memory at the methylated Airn ICR state in mESCs. In the case of the unmethylated allele, the same sequence changes result in loss of protection from de novo methylation, suggesting sequence-specific mechanisms that protect from de novo methylation, potentially similar to those observed at regulatory regions of nonimprinted genes. Nevertheless, because maintenance of differential Airn ICR methylation in MEL cells was independent of DNA sequences outside of CpGs, we suggest a cell-type-specific requirement for sequence-specific factors involved in epigenetic maintenance. In the case of ZFP57, this would be in line with its restricted transcriptional activity to germ cells and during early development. Having identified the robust establishment and maintenance of heterochromatin at methylated ICRs, we could generate reporter cell lines that respond to DNA methylation. In contrast to previous strategies that used the Snrpn promoter to report changes in methylation at endogenous gene promoters, our cell lines directly report regulatory changes at the introduced ICRs. We used these reporters to screen for factors required for maintenance of the repressed state. Targeted CRISPR screens identify Dnmt1, Uhrf1 and Zfp57 as the most relevant genes required to maintain the DNA methylation status at all tested ICRs, confirming the suitability of our setup. In addition, our functional screens identified, and thus validate, additional factors that have been described to regulate H3K9me3 throughout the genome and to associate with ICRs, including DAXX, ATRX, CBX1 and CBX5 (refs. ). Among the obtained hits, we identified ATF7IP and ZMYM2 as factors involved in the maintenance of epigenetic repression at ICR reporters. ATF7IP and SETDB1 show functional overlap in the regulation of endogenous retroviral elements, with ATF7IP acting as a cofactor of SETDB1 by stimulating its enzymatic activity, protecting it from proteasomal degradation and facilitating its nuclear localization. Although loss of SETDB1 is lethal in mESCs, the absence of ATF7IP reduces levels of SETDB1, sufficient for viability but insufficient to maintain all repressed sites in the genome. The C-terminal fibronectin type-III domain of ATF7IP has been shown to interact with ZMYM2 (also ZFP198), and this interaction was suggested to be important for the silencing of a few germline-specific genes, including the imprinted FKBP6 gene. ZMYM2 was also described to interact with H3K9me3-marked chromatin and furthermore required for endogenous retroviral element silencing, thereby preventing the transition to two-cell-like cells in mESC culture. The role of ZMYM2 in restricting potency is further supported by the fact that ZMYM2 is required for exit from pluripotency. We show that ATF7IP and ZMYM2 colocalize together with ZFP57 and SETDB1 at the majority of endogenous ICRs in mESCs and are required for the memory of the epigenetic state at methylated ICRs. This is in line with a publication showing a role of ATF7IP in regulating sperm-specific genes and paternally expressed imprinted genes, including Peg13 in human parthenogenetic ESCs. Our results indicate that, in mESCs, ATF7IP could play a broader role in regulating all methylated ICRs, independently of the parental origin. If and how these two factors contribute to maintenance of imprints during zygote formation and early development remains to be tested. In mESCs, their absence resulted in impaired maintenance fidelity and sporadic loss of H3K9me3 at multiple ICRs, independently of their parental origin. We suggest that this destabilizes the repressive feedback loop between DNA methylation and H3K9me3, allowing switching of the ICR to the default unmethylated state. While we observe differences in regulatory activities of ATF7IP and ZMYM2 at some ICRs (for example, Mcts2/H13, Zrsr1/Commd1 and H19), it remains to be determined if this is due to a specificity of these factors toward these ICRs. Alternatively, this could reflect stochasticity in ICR switching to an unmethylated state in absence of either factor, which is memorized in clonally derived cells.
RMCE-competent mESCs (TC-1 (ref. ), obtained from A. Dean, National Institutes of Health (NIH)) were cultured on 0.2% gelatin-coated dishes in mESC medium containing DMEM (Invitrogen), 15% fetal calf serum (Invitrogen), 1× non-essential amino acids (Invitrogen), 1× Glutamax (Invitrogen), 0.001% 2-mercaptoethanol (Invitrogen) and titrated leukemia inhibitory factor (made in-house) at 37 °C in 7% CO2. Alternatively, mESCs were cultured in 2i medium containing 50% Neurobasal medium (Invitrogen), DMEM/F12 medium (Invitrogen), 1× non-essential amino acids (Invitrogen), 1× Glutamax (Invitrogen), 0.001% 2-mercaptoethanol (Invitrogen), 1× N2 supplement (Invitrogen), 1× B27 supplement (Invitrogen), titrated leukemia inhibitory factor, 3 µM CHIR99021 (Sigma-Aldrich) and 1 µM PD0325901 (Sigma-Aldrich). Where indicated, l-ascorbic acid (Stemcell Technologies) was added at a concentration of 100 µg ml−1 (ref. ). Differentiation to neuronal progenitor cells was performed as previously described without feeder cells. For DNMT1 inhibition, GSK-3484862 (MedChemExpress) was added to a final concentration of 10 µM, as previously determined. RMCE-competent MEL cells (obtained from D. Schübeler, FMI Basel) cells were cultured in suspension in DMEM (Invitrogen) supplemented with 10% fetal calf serum (Invitrogen) and 1× Glutamax (Invitrogen). All RMCE-competent cell lines (TC-1 and MEL) were authenticated based on selection and PCR on the RMCE resistance cassette.
Targeted cell line integrations in mESCs were obtained through RMCE using either electroporation of 2 × 106 cells with the Amaxa Nucleofector (Lonza) or Lipofectamine 3000 (Invitrogen) transfections of 2.5 × 104 cells. All RMCE vectors were cotransfected with a CRE-expressing plasmid at a ratio of 1:0.6 µg, using either a total of 40 µg plasmid for the Amaxa Nucleofector kit or 1 µg for Lipofectamine 3000. Two days after transfection, cells were selected with 3 µM Ganciclovir for more than 8 days. The obtained cell lines were kept as pools and when necessary clonal cell lines were obtained through limited dilution. Pools or clonal cell lines were genotyped using integration site specific PCRs. The parental cell line for all reporter cell lines used in the CRISPR screens contains a stably expressed Cas9 gene from the Rosa26 locus, obtained by TALEN-mediated integration as previously described. Single-clone KO cell lines were obtained by CRISPR-Cas9 using the px330-hSpCas9 (Addgene, 42230) plasmid together with a pRR-Puro recombination reporter. A total of 1 µg plasmid DNA at a ratio of 1:0.1 of px330 to pRR-Puro was transfected using Lipofectamine 3000. Puromycin selection was started 36 h after transfection for 36–48 h at a concentration of 2 µg ml−1. KO cell lines were validated using targeting site-specific PCR. RMCE in MEL cells was performed using Lipofectamine 3000 (Invitrogen), plating 5 × 105 cells in 6-well plates for suspension cells. A total of 2.5 µg plasmid DNA, using the same ration as described before, was transfected according to the manufacturer’s instruction. After 48 h, cells were transferred into T75 flasks, and cells that underwent recombination were selected with 5 µM Ganciclovir containing media for more than 8 days.
A backbone containing two inverted loxP sites was used to clone several empty reporter vectors containing a 60-bp universal entry site with a central EcoRV restriction site, followed by a promoter (pCAGGS, hPGK and Ef1alpha) that drives an eGFP or mScarlet gene for the ChroMM and EpiTF screens, respectively, followed by a downstream BGH-poly(A) and a WPRE sequence. Individual ICR or control sequences were amplified from genomic DNA (Supplementary Table 1). Gibson assembly was performed according to the NEB Gibson Assembly Master Mix protocol. In vitro methylation was performed with up to 40 µg plasmid DNA using the NEB M.SssI methyltransferase in two consecutive reactions of at least 4 h with 600 µM SAM (NEB, B9003S) and 1.5 U M.SssI (NEB, M0226L) per microgram DNA. Complete methylation of plasmids was confirmed by using the CpG methylation sensitive restriction enzyme HpaII (NEB) and a methylation insensitive control reaction with MspI (NEB). Cell lines were generated as described before. Individual clones were genotyped using PCR with primers spanning the loxP sites. Methylation of the integrated reporter construct was validated on selected clones.
Flow cytometry data acquisition was performed on a BD FACSCanto II or a BD LSR Fortessa cell analyzer. FACS was performed with a BD FACSAria III cell sorter. Data analysis was done with FlowJo (version 10.7) or BD FACSDiva (9.1.2). All samples were gated for single cells, using forward scatter area (FSC-A) versus side scatter area (SSC-A), followed by FSC-A versus forward scatter height (FSC-H). GFP-negative and positive populations were quantified using GFP-negative wild-type cells as a reference. For cell surface marker staining, a uniform cell suspension was prepared by trypsinization and filtering through a 40-μm cell strainer (BD Bioscience). Cells were stained with an allophycocyanin (APC)-conjugated CD90.1 antibody (Invitrogen, 17-0900-82) for 30 min at 4 °C with a saturated antibody concentration (1 µl per 15 million cells).
BPnet (version 0.0.23) was used to determine sequence motif and context of ZPF57 binding in mESCs. ZFP57 ChIP-seq data and corresponding input files were aligned to the mouse genome (NCBI Build 37 mm9, July 2007) using bowtie2 (version 2.3.5.1) after removal of adapters using trimgalore (version 0.6.6). Aligned reads were filtered for PCR duplicates using Picard (version 2.23.9), and only reads with a mapping quality (MAPQ) > 40 were kept for further analysis. All replicates were merged before peak calling using MACS2 (version 2.1.1.20160309) with the following parameters: callpeak -g mm–keep-dup all -q 0.05–call-summits. Reads mapped to the positive and negative strand of the merged datasets were split into individual files and trimmed to the 5′ base as input tracks for BPnet. A model was trained with the default bpnet9 architecture (https://github.com/kundajelab/bpnet), using chromosomes 1, 8 and 9 as test set, and peaks on chromosomes 2, 3 and 4 as validation sets. Peaks on chromosomes X and Y were excluded from model training. After calculation of the contribution scores with BPnet’s DeepLIFT method, motifs were determined using BPnet’s TF-MoDISco method. To determine contribution scores on the Airn and shuffled Airn sequences, the input DNA was one-hot encoded before subjecting them to the trained model to generate ZFP57 binding predictions. For the walking mutations, 10 nt of the shuffled sequence was swapped with the original Airn sequence and shifted by 1 bp per prediction.
Up to 2 µg genomic DNA, or the total amount to eluted material from ChIP, was used for bisulfite conversion using the EpiTect Bisulfite Kit (Qiagen). Bisulfite PCR was carried out using the PhusionU polymerase (Thermo Fisher Scientific) with the primers indicated in Supplementary Table 1 using the following conditions: initial denaturation at 95 °C for 5 min, followed by 45 cycles of 1 min at 95 °C, 1 min at 50–60 °C (dependent on the primer pair) and 1 min at 72 °C, followed by 5 min of final extension at 72 °C. Amplicons were cloned into the CloneJET vector (Thermo Fisher Scientific), sequenced by Sanger sequencing and analyzed using QUMA.
Targeted bisulfite sequencing libraries were made from equimolar pooled bisulfite PCR fragments. Two independent PCR reactions were run per target with annealing temperatures at 50 °C and 58 °C to mitigate amplification bias. Indexed libraries were prepared using the NEBNext Ultra II kit (NEB) starting from 10 ng pooled amplicons according to the manufacturer’s protocol. Sequencing was done on an Illumina NovaSeq6000 machine with 150-bp paired-end reads. Fastq files were trimmed using trim_galore (version 0.6.6) and alignment was performed with Bismark (version 0.23.0) with the parameter non_directional. CpG methylation was extracted using the Bismark methylation extractor and average CpG methylation was calculated in R, excluding CpGs that were covered less than 500 times.
WGBS of Atf7ip and Zmym2 KO mESCs was performed as described previously. In short, 10 µg genomic DNA was sonicated to a length of approximately 400–500 bp. For each sample, 2 µg sheared genomic DNA was mixed with 10 ng equimolar pooled sonicated methylated phage T7 and unmethylated phage Lambda DNA. Adapter-ligation was carried out with the NEBNext Ultra II kit (NEB E7645L) using methylated adaptors (NEB, E7535S), before bisulfite conversion using the Qiagen Epitect bisulfite conversion kit, according to the manufacturer’s instructions. After conversion, libraries were amplified for 10 cycles using the Pfu TurboCx Hotstart DNA polymerase (Agilent) and the NEB dual index primers (NEB, E7600S). PCR reactions were run with the following parameters: 95 °C for 2 min, 98 °C for 30 s, followed by 10 cycles of 98 °C for 15 s, 65 °C for 30 s and 72 °C for 3 min, ending with 5 min at 72 °C. The PCR reactions were cleaned up using 1.2× AMPure XP beads (Beckman Coulter) and eluted in 20 µl EB buffer (Qiagen). Library quality was checked on an Agilent TapeStation and sequencing was done on an Illumina NovaSeq 6000 machine.
Approximately 15 × 106 cells were harvested per IP and fixed with 1% methanol-free formaldehyde for 8 min. Crosslinking was quenched by adding glycine to a final concentration of 0.125 nM and incubated for 10 min at 4 °C on ice. Cells were pelleted at 600 × g for 5 min, washed with cold PBS and incubated for 10 min on ice in a buffer containing 10 mM EDTA, 10 mM Tris pH 8, 0.5 mM EGTA and 0.25% Triton X-100. After centrifugation, cells were incubated in 1 mM EDTA, 10 mM Tris pH 8, 0.5 mM EGTA and 200 mM NaCl for 10 min on ice. Chromatin was extracted in a high-salt buffer containing 50 mM HEPES, pH 7.5, 1 mM EDTA, 1% Triton X-100, 0.1% deoxycholate, 0.2% SDS and 500 mM NaCl for 2 h at 4 °C, and chromatin was sheared using a Bioruptor Pico sonicator (Diagenode). Then, 100 µg of chromatin was used per IP reaction with 30 µl pre-blocked magnetic Protein A beads (Invitrogen). Beads were blocked with 1 mg BSA and 100 ng yeast tRNA (Sigma) in TE buffer containing proteinase inhibitor mix (Roche) before use. Prior to the IP, chromatin was precleared with 20 µl blocked beads for 1 h at 4 °C. Next, 5% of input material was kept at −20 °C and decrosslinked along the IP material. Then, 5 µg antibody was used per IP for overnight incubation at 4 °C. The next day, 30 µl blocked beads was added to the chromatin and incubated for 4 h at 4 °C. Beads were separated on a magnet and washed twice for 8 min with high-salt buffer, one time with 50 mM LiCl, 0.5% NP-40, 0.5% deoxycholate, 1 mM EDTA and 10 mM Tris, pH 8. After two additional washes with TE for 8 min, chromatin was eluted after 30 min incubation at 37 °C with 60 µg RNaseA (Roche) in 1% SDS and 100 mM NaHCO3, followed by 3-h incubation adding 10 mM EDTA, 40 mM Tris, pH 8, and 60 µg Proteinase K (Roche). Final decrosslinking was done overnight at 65 °C. Eluted material was cleaned up using phenol chloroform extraction and quantified using a Qubit 2.0 fluorometer (Thermo Fisher Scientific). The following antibodies were used for ChIP: H3K9me3 (Abcam, ab8898, 5 µg per IP), H3K4me2 (Diagenode, C15410035, 5 µg per IP), H3K4me3 (Abcam, ab8580, 5 µg per IP), ATF7IP (Bethyl, A300-169A, 5 µg per IP) and ZMYM2 (Bethyl, A301-711A, 10 µg per IP).
qPCR reactions were run as technical duplicates on a Rotor Gene Q machine (Roche) using the KAPA SYBR Fast universal qPCR kit (Sigma) in 10-µl reactions with 1 µl eluted DNA for the IP material or 1 µl of a 1:10 dilution of the input material. Delta Ct values were calculated over input, followed by delta Ct and fold change over an intergenic region. Primers are listed in Supplementary Table 1. Corresponding plots were generated with Prism (5.0a).
ChIP-seq libraries were prepared using the NEBNext ChIP-seq Library Prep Master Mix set for Illumina (NEB, E6240) or NEBNext Ultra II Kit, following the manufacturer’s protocol. Final libraries were visualized and quantified on a 2200 TapeStation System (Agilent) and pooled with equal molar ratios before sequencing on an Illumina NovaSeq6000 machine with 150-bp paired-end reads.
Published mESC genome-wide datasets were obtained from GEO (WGBS; H3K9me3, H3K4me3, H3K36me3 and H3K27me3 (ref. ), DNase-seq and RNA-seq, SETDB1 (ref. ), ZFP57 (ref. ), ZMYM2 (ref. ) and ATF7IP. Sequencing reads from published datasets and ChIP-seq reads generated in this study were filtered for low‐quality reads as well as adaptor sequences using trimgalore (version 0.6.6) and mapped to the mouse genome (NCBI Build 37 mm9, July 2007). Mapping of H3K9me3, H3K4me3, H3K36me3, H3K27me3, DNase-seq and RNA-seq was done with QuasR (1.30.0) in R with standard qAlign() settings. Wig tracks were obtained with QuasR qExportWig() command and visualized using the UCSC genome browser (https://genome.ucsc.edu). Mapping of WGBS data was done with QuasR using qAlign() with following settings: genome = ‘BSgenome.Mmusculus.UCSC.mm9’, aligner = ‘Rbowtie’ and bisulfite = ‘dir’. CpG methylation calls were extracted using qMeth() and filtered to contain only CpGs covered at least 10×. ChIP-seq peak coordinates were obtained using MACS2 (version 2.1.1.20160309) with the following parameters: callpeak -g mm–keep-dup all -q 0.05–call-summits. Coordinates were imported into R as GenomicRanges objects and peaks larger than 1 kb were removed from further analysis. Overlaps between peaks were calculated using the findOverlaps() function in R, with maxgap=1000 L. Heatmaps over ICRs and peak regions were generated with genomation() in R using the ScoreMatrixList() and multiHeatMatrix() functions.
The ChromMM and EpiTFs library was constructed as a subpool of the Vienna sgRNA library as described previously. Lentivirus was produced in HEK293T (obtained from G. Schwank, University of Zurich) cells as described. Nonconcentrated virus was titrated with different amounts following the same transduction procedure used for the actual CRISPR screens. Transduction was performed with 1.25 × 106 cells seeded in gelatin-coated 6-well plates in embryonic stem cell medium containing 8 µg ml−1 polybrene (Merck), spinning for 60 min at 500 × g at 37 °C. After centrifugation, cells were incubated for 12 h at 37 °C, before transferring them on multiple 15-cm plates and culturing them for another 24 hours. After 36 h, transduced cells were selected using FACS. For the ChromMM library cells were stained against the CD90.1 cell surface marker using an APC-conjugated antibody (Invitrogen, 17-0900-82, 1 µl per 15 million cells), gating on APC-positive and GFP-negative single cells. For the EpiTF library, cells were gated on GFP-positive and mScarlet negative single cells. After the sort, cells were seeded sparsely on multiple 15-cm dishes and only passaged once after 4 days to avoid bottlenecks in the library representation. On day 10 after transduction, GFP-positive cells were sorted and further processed for genomic DNA extraction using the DNeasy Blood and Tissue kit (Qiagen). All screens were performed with at least 30 million cells and a low multiplicity of infection between 0.1 and 0.2, yielding a total cell number of at least 3 million cells and a guide representation of at least 450× per guide after the first sort. For the final sort, the same number of initially transduced cells were used for the sort and kept as the reference pool. The screen with the EpiTF library was performed as described above, however 90 million cells were used for transduction at an multiplicity of infection of 0.2. Initially, all screens were performed as technical duplicates or triplicates with individual reporter clones and later repeated once more as independent experiments. To score essential and growth-restricting genes, the pooled cells at indicated days were compared to the initial plasmid library.
Library preparation was done for the entire amount of extracted genomic DNA in two consecutive PCR amplification steps. In the first PCR, the integrated guide sequences were amplified using the Herculase II Fusion DNA polymerase (Agilent) according to the manufacturer’s instruction with a maximum of 500 ng DNA input per 50 µl reaction using library specific primers with 3′ adapter sequences for barcoding (Supplementary Table 1). The PCR mix contained 1.5% DMSO and had a final concentration of 3 nM of MgCl2. Amplified products were first purified using the MinElute Gel extraction kit (Qiagen) and potential primer dimers were removed using AmpureXP beads (Beckmann) at a ratio of 0.7× volume. Sample specific barcoding was done in a second PCR using NEBNext Multiplex Oligos (NEB) and the NEBNext Q5 Hot Start HiFi PCR Master mix (NEB) according to the manufacturer’s manual with 10% of the eluted product from the first PCR and 7 amplification cycles. For the EpiTF library, barcoding was done with the i5 primers from the NEBNext Multiplex Oligo kit (NEB) and a custom primer that carries the P7 sequence (Supplementary Table 1). Sequencing was done on an Illumina NovaSeq6000 or a MiSeq machine, specifying a 10-bp index read 1 for the EpiTF library.
Demultiplexing was performed using the standard pipeline of Illumina. For the EpiTF library, demultiplexing was only performed on the i5 index, as the i7 index contains the UMI. Fastq files were trimmed to only include the guide RNA sequence using cutadapt (version 3.10) specifying -g 5′-TAGCTCTTAAAC...GGTGTTTCGTC-3′ for the linked adapter sequences in the lentivirus backbone for the ChromMM library or -g aaacaccg…gtttaaga for the EpiTF library. Alignment was done using bowtie2 (version 2.3.5.1) against a reference genome built from the sgRNA sequences, specifying the following alignment parameters: -k 1–very-sensitive. BAM files were converted into bed files using bedtools (version 2.27.1) bamtobed function. Bed files were imported into R to create a count matrix for MAGeCK (version 0.5.9.2). For the final analysis, counts from technical replicates as well as different GFP high and GFP low bins were aggregated. MAGeCK was run with–norm-method set to total and run against the unsorted pool as the control sample, specifying the independent replicates.
Single-guide validation was done with one guide RNA that was included in the library and one independently designed guide with high on-target and low off-target activity, as described in ref. (Supplementary Table 1). Guides were cloned into the px459 backbone (Addgene, 62988) which allows for puromycin selection. For this, 1,000 cells were seeded in gelatin-coated wells of a 96-well plate 1 day before transfection. Next, 100 ng plasmid DNA was transfected per well as technical replicates using Lipofectamine 3000 (Invitrogen). After 36 h, transfected cells were selected for 36–48 h with 2 µg ml−1 puromycin using untransfected cells as a control. Reactivation of the reporter was evaluated 12 days after transfection using flow cytometry, and GFP reactivation was quantified over cells transfected with nontargeting control guides.
For western blotting, 20–35 μg protein was separated on 6% or 10% polyacrylamide gels and transferred on polyvinylidene fluoride membranes using the TransBlot Turbo system (Bio-Rad). For antibody-based staining, the membrane was washed once with TBS-T (10 mM Tris, pH 8.0, 150 mM NaCl and 0.1% Tween-20), blocked with 5% non-fat dry milk in TBS-T and stained with primary antibodies against ATF7IP (Bethyl, A300-169A), ZMYM2 (Bethyl, A301-711A-M), or LAMIN B1 (Santa Cruz Biotechnology, 374015) at 4 °C overnight. Next day, membranes were washed three times with TBS-T for 10 min before incubation with species-specific horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature. After additional three washes with TBS-T for 10 min each, signal was detected using the Amersham ECL Western blotting detection reagent (GE Healthcare Life Sciences; RPN2109) and exposure on Amersham Hyperfilm ECL (GE Healthcare Life Sciences; 28906836) in a darkroom.
Cell lines were generated as described in Villasenor et al.. The coding sequence of the BioID2 enzyme of the original entry vector was exchanged for the coding sequence of the TurboID enzyme. Cells were either transfected with the entry vector, containing only the TurboID with a nuclear localization sequence, the full-length mouse ZFP57 cDNA sequence cloned upstream of the TurboID, or the KRAB domain of ZFP57 as annotated on UniProt. All cells were validated using western blot of cells incubated with 50 µM biotin (Sigma-Aldrich) for 12 h as previously described with minor adjustments to accommodate the biotin detection. In short, membranes were blocked in 5% BSA in TBS containing 0.1% Triton X-100 for 1 h and stained with streptavidin-horseradish peroxidase (1:20,000) in TBS containing 0.1% Triton X-100 overnight at 4 °C. The membrane was washed twice with TBS containing 0.3% Triton X-100, twice with TBS containing 0.3% Triton X-100 and additional 500 mM NaCl, before one final wash with TBS containing 0.3% Triton X-100 for 10 min at room temperature each.
TurboID samples were prepared as described in Villaseñor et al.. In brief, cells were grown as quadruplicates on 15-cm plates, incubated with 50 μM biotin (Sigma-Aldrich) for 12 h upon 70% confluency and harvested with trypsin. In the following, samples were handled at 4 °C or on ice. Cell pellets were swelled in 5× volume of nuclear extraction buffer 1 (NEB1; 10 mM HEPES, pH 7.5, 10 mM KCl, 1 mM EDTA, 1.5 mM MgCl2, 1 mM dithiothreitol (DTT), 1× EDTA-free complete protease inhibitor cocktail (PIC; Roche) for 10 min, before spinning at 2,000 × g for 10 min. Cells were homogenized using a loose Dounce pistil in 2× volumes of NEB1. Nuclei were collected by centrifugation at 2,000 × g for 10 min, resuspended in 1× volume nuclear extraction buffer 2 with 450 mM NaCl (NEB2; 20 mM HEPES, pH 7.5, 0.2 mM EDTA, 1.5 mM MgCl2, 20% glycerol, 1 mM DTT and 1× PIC) and homogenized 10 more times with a tight Dounce pistil, followed by an incubation for 1 h with overhead rotation. Debris was removed by centrifugation at 2,000 × g for 10 min before adjusting the salt concentration of the supernatant to 150 mM NaCl with 2× volumes of NEB2 and adjusting the final NP40 concentration to 0.3%. Protein extracts were quantified using the Qubit Protein Assay Kit (Thermo Fisher Scientific, Q33211) and equal amounts of protein extracts were used per IP. For each IP, 40 µl of Streptavidin M-280 Dynabeads (Thermo Fisher Scientific), pre-blocked in IP buffer (IPB; NEB2, 150 mM NaCl, 0.3% NP40, 1 mM DTT, 1× PIC) containing 1% cold fish gelatin, were added to the extracts, and incubated at 4 °C overnight while rotating. Next, beads were washed twice with 2% SDS in TE containing 1 mM DTT and 1× PIC for 10 min rotating at RT, followed by one 10 min wash with a high salt buffer (50 mM HEPES, pH 7.5, 1 mM EDTA, 1% Triton X-100, 0.1% deoxycholate, 0.1% SDS, 500 mM NaCl, 1 mM DTT, 1× PIC), one wash with DOC buffer (50 mM LiCl, 10 mM Tris, pH 8.0, 0.5% NP40, 0.5% deoxycholate, 1 mM EDTA, 1 mM DTT and 1× PIC) and twice with TE buffer containing 1 mM DTT, 1× PIC. After the washes, beads were pre-digested with 5 µg ml−1 trypsin (Promega; V5111) in 40 µl digestion buffer (1 M urea in 50 mM Tris, pH 8.0, 1 mM Tris-(2-carboxyethyl)-phosphine) for 2.5 h at 26 °C and shaking at 600 rpm. The supernatant was further reduced with 2 mM Tris-(2-carboxyethyl)-phosphin for 45 min at room temperature, alkylated with 10 mM chloroacetamide for 30 min at room temperature and protected from light. For the final digest, the protein solution was incubated with additional 0.5 µg trypsin overnight at 37 °C. The next day, the digested samples were prepared for loading on C18 StageTips by addition of trifluoracetic acid (TFA) to a final concentration of 0.5% and acetonitrile (ACN) to a final concentration of 3%. In-house produced C18-StageTips (Functional Genomics Center Zurich) were humidified with 100% methanol, cleaned twice with the elution solution (60% ACN, 0.1% TFA) and prepared for loading by washing twice with 3% ACN and 0.1% TFA. After loading of the peptide solution, samples were centrifuged and the supernatant was loaded on more time, before washing twice with 3% ACN and 0.1% TFA. Finally, peptides were eluted twice with the elution solution, shock frozen in liquid nitrogen, dried in a speed vacuum centrifuged and reconstituted in 3% ACN, 0.1% formic acid, containing internal retention time standard peptides (iRTs, Biognosys). Samples were run on an Easy-nLC 1000 HPLC system coupled to an Orbitrap Fusion mass spectrometer (Thermo Fisher Scientific) with block randomized samples
MaxQuant (version 1.5.3.30) was used for protein identification and label-free quantification based on the mouse reference proteome (UniProtKB/Swiss-Prot and UniProtKB/TrEMBL) version 2018_12 combined with manually annotated contaminant proteins, with a protein and peptide FDR values set to 1%. Perseus was used for statistical analysis as described previously. For this, only proteins were kept that were identified in three out of four samples per group. Missing values were imputed from a 1.8 standard deviations left-shifted Gaussian distribution with a width of 0.3. A t-test was used to identify potential interactors using an FDR threshold of < 0.05 and an S0 value of 1. Data were visualized using R (version 4.0.3).
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01210-z.
Reporting Summary Peer Review File Supplementary Table 1Supplementary Table containing additional information and numerical values related to CRISPR screens, bioID MS experiments and DNA sequence information. |
PMC9649442 | 36333502 | Pauline Robbe,Kate E. Ridout,Dimitrios V. Vavoulis,Helene Dréau,Ben Kinnersley,Nicholas Denny,Daniel Chubb,Niamh Appleby,Anthony Cutts,Alex J. Cornish,Laura Lopez-Pascua,Ruth Clifford,Adam Burns,Basile Stamatopoulos,Maite Cabes,Reem Alsolami,Pavlos Antoniou,Melanie Oates,Doriane Cavalieri,S. M. Wood,Jack Zhuang,Jane Gibson,Anika V. Prabhu,Ron Schwessinger,Daisy Jennings,Terena James,Uma Maheswari,Martí Duran-Ferrer,Piero Carninci,Samantha J. L. Knight,Robert Månsson,Jim Hughes,James Davies,Mark Ross,David Bentley,Jonathan C. Strefford,Stephen Devereux,Andrew R. Pettitt,Peter Hillmen,Mark J. Caulfield,Richard S. Houlston,José I. Martín-Subero,Anna Schuh | Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features | 04-11-2022 | Chronic lymphocytic leukaemia,Genomics | The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia. | Whole-genome sequencing of chronic lymphocytic leukemia identifies subgroups with distinct biological and clinical features
The value of genome-wide over targeted driver analyses for predicting clinical outcomes of cancer patients is debated. Here, we report the whole-genome sequencing of 485 chronic lymphocytic leukemia patients enrolled in clinical trials as part of the United Kingdom’s 100,000 Genomes Project. We identify an extended catalog of recurrent coding and noncoding genetic mutations that represents a source for future studies and provide the most complete high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and genomic complexity. We demonstrate the relationship of these features with clinical outcome and show that integration of 186 distinct recurrent genomic alterations defines five genomic subgroups that associate with response to therapy, refining conventional outcome prediction. While requiring independent validation, our findings highlight the potential of whole-genome sequencing to inform future risk stratification in chronic lymphocytic leukemia.
Chronic lymphocytic leukemia (CLL), the most common adult hematological malignancy in Western countries, is characterized by diverse treatment outcomes even in the era of targeted agents. The full complement of genomic events contributing to this clinical diversity have yet to be determined. Thus far, only mutations in TP53 influence clinical practice. Other prognostic markers, including the immunoglobulin heavy chain variable (IGHV) region mutational status, and existing molecular classifications have limited predictive value in individual patients. Previous sequencing studies of CLL have focused largely on mutations affecting protein-coding genes, and whole-genome sequencing (WGS) has been reported for only a small number of CLL patients, mostly with low-risk disease. Hence, the association between clinical parameters and genomic alterations has largely been restricted to driver coding mutations and copy number changes. Here, to provide the largest and most comprehensive analysis of the entire genomic landscape of CLL and its relationship to clinical outcome, we performed WGS of 485 clinical trial patients recruited to the United Kingdom’s 100,000 Genomes Project. The results of our study provide additional insights into coding and noncoding single nucleotide mutations. We then exploit WGS data to provide a detailed map of structural alterations and global features, including telomere length, mutational signatures and genomic complexity (GC). Finally, we integrate the different modes of genetic alterations to define five genomic subgroups (GSs) of CLL and relate these to clinical outcome. Our results provide a springboard to indepth functional validation of putative drivers and our integrated genome-wide approach could, after independent clinical validation, refine current clinical outcome prediction.
We performed WGS of tumor and matched normal samples from 485 patients with treatment-naïve CLL enrolled in clinical trials to a median depth of 109× and 36×, respectively (Supplementary Tables 1–3). A second tumor sample was available for a subset of 25 patients at relapse. In addition, RNA sequencing (RNA-seq; n = 73) and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq; n = 24) data were generated for a subset of CLL samples with recurrent noncoding mutations (Supplementary Table 4).
We initially identified putative coding drivers by (1) screening for genes impacted by single nucleotide variants (SNVs) and small insertion/deletions (indels) and (2) integrating SNV/indels with copy number alterations (CNAs) (Fig. 1a; Methods). We identified 36 known and 22 putative driver genes (Fig. 1b and Supplementary Fig. 1), which were not found associated with CLL in the literature and also not prevalent above 1% in two landmark genomic studies in CLL. These were classified as previously unknown putative drivers and included the immune checkpoint regulator IRF2BP2 (4.3%) (Supplementary Table 4). We identified 74 regions of the genome that were recurrently affected by CNAs in at least four samples (Fig. 1c, Extended Data Fig. 1a and Supplementary Table 6). Using DNA microarray data, 85% of CNAs could be validated (Supplementary Table 7). In addition to 14 well-known CNAs, including del13q14.2, del11q22.3 and del17p13.1, we identified a further 60 regions, of which 27 were previously not recognized. The breakpoints of the remaining 33 CNAs could be refined to a smaller minimally overlapping region. By combining SNVs/indels with CNAs (discovery method 2; Methods), we predicted the most likely target gene for nine known regions, including TP53/del17p13.1, and seven additional regions including PCM1/del8p, IRF2BP2/del1q42.2q42.3 and SMCHD1/del18p11.32-p11.31 (Fig. 1d, Extended Data Fig. 1b,c and Supplementary Table 8). We also found 66 additional genes affected by recurrent CNAs using more permissive criteria (Methods). While these are potentially interesting, they were not considered to be putative CLL drivers and were not taken forward for downstream analyses (Supplementary Table 9). A major advantage of WGS is the power to identify inversions and translocations. We identified 1,248 inversions (Extended Data Fig. 2a; Methods) with frequent breakpoints involving either the immunoglobulin light chain kappa (IGK) locus (n = 65, 13.4%), the immunoglobulin heavy chain (IGH) locus (n = 65, 13.4%) or chr13q14.2 (n = 40, 8.7%) (Extended Data Fig. 2b and Supplementary Tables 10 and 11). We detected 993 translocations, of which two occurred in more than ten samples and affected known genes with no previously documented role in CLL, including t(14;22) with breakpoints within WDHD1 (n = 12, 2.6%) and t(5;6) (CTNND2-ARHGAP18, n = 11, 2.4%) (Fig. 1e and Extended Data Fig. 2c). The 22 potential coding driver genes were altered by truncating mutations or also affected by CNAs (Fig. 2a, Extended Data Fig. 3a–d, Supplementary Table 12 and Supplementary Figs. 2 and 3). Most mutations occurred in protein domains, and 62% of mutations were detectable in more than 50% of tumor cells (median cancer cell fraction (CCF) ≥0.5) and 89% in at least 20%. All previously unreported CNAs for which we could predict a target gene(s) were also clonal (median CCFs ≥0.8) (Fig. 2b and Extended Data Fig. 3e). Candidate driver mutations affected multiple biological pathways including the DNA damage/cell-cycle and RNA-ribosome processing (Fig. 2c). Performing RNA-seq on representative CLL samples from 74 patients with known and potential coding mutations (for 40 of the 58 drivers, n variants = 118, Supplementary Table 4; Methods), we validated the expression of 73% of variants at the RNA level (Extended Data Fig. 4a and Supplementary Table 13). As expected, most (29/43) mutations that were either not detectable or were seen at low expression levels were truncating mutations consistent with nonsense-mediated decay (Supplementary Table 13). Additionally, allelic skewing and/or a reduction of mutant transcript expression compared with the mean expression of wild-type (WT) transcripts across the cohort was shown, notably for specific mutations in SPEN, SETD2, TP53 and IRF2BP2 (Fig. 2d). When considering all mutations, significantly reduced gene expression was demonstrated for TP53, ATM and SETD2 (refs. ) (Extended Data Fig. 4b). When we associated the 36 known and 22 putative drivers and regions of CNAs with other biological variables such as disease stage, TP53 alterations, IGHV mutation status (unmutated, u-IGHV; and hypermutated, m-IGHV) and stereotyped B cell receptor immunoglobulin subsets (BCR IG) including IGHV3-21 usage (Fig. 2e and Supplementary Table 14; Fisher’s exact test, false discovery rate (FDR) < 0.05), we found that SETD2/del3p21.31, del9p21.3 and gains of chr17q21.31 were associated with relapsed/refractory (R/R) disease and TP53 disruption, whereas MED12 and DDX3X mutations were associated with u-IGHV CLL. BCR IG subset 2, representing about 3% of all CLL, and known to be associated with poor prognosis, was linked to the putative driver FAM50A. The IGHV3-21 rearrangement was also enriched for FAM50A and for ATM/del11q22, SF3B1 mutations and chr21q21.3-q22.3 gains.
We examined the relationship between recurrent gene mutations and disease evolution in three different cohorts (Fig. 3a and Supplementary Table 4; Methods): (1) unpaired frontline-treated versus R/R (main cohort, unmatched, n = 443 versus 30—excluding the 12 early CLL); (2) paired samples from the CLL and Richter’s syndrome (RS) phases of the same patient (previously published cohort, matched, n = 17) and (3) a second sample taken from a subset of the 485 patients at relapse who had already been profiled before frontline treatment: paired frontline-treated versus relapsed (main cohort, matched, n = 25/485). Recurrent coding gene mutations were linked to disease evolution in all three cohorts. They presented higher mutation counts and frequency in the RS compared with the CLL phase (P = 2.1 ×10−2; Extended Data Fig. 4c,d) and higher CCFs at the more advanced stages with a median CCF > 0.8 (Fig. 3b and Extended Data Fig. 4e–g). Restricting analysis to patients with information on long-term survival outcome (n = 243 / 485), 13 known or putative drivers and recurrent CNAs were significantly associated with progression-free survival (PFS) and 11 with overall survival (OS) (FDR < 0.05) (Fig. 3c and Supplementary Tables 15 and 16). Out of the 22 putative drivers, 21 were also related to disease progression (Extended Data Fig. 4c–f), including two of the most commonly mutated ones. IRF2BP2 (interferon regulatory factor 2 binding protein 2), located in the minimally deleted region of chr1q42.3 (Fig. 3d) was also affected by deleterious mutations and CNAs (Fig. 3e) (in total, n = 28/485, 5.8%) with high CCFs (Fig. 3f, left panel). Mutations showed evidence of clonal expansion in more advanced disease (Fig. 3f, right panel) and altered RNA expression (Fig. 2d). This gene contributes to the differentiation of immature B-cells and is associated with a familial form of common variable immunodeficiency disorder. Similarly, SMCHD1 (structural maintenance of chromosomes flexible hinge domain containing 1), previously reported as a candidate tumor suppressor in hematopoietic cancers was affected by copy number losses (del18p11.32-p11.31) (Fig. 3g) and truncating SNVs/indels with high CCFs (Fig. 3h) (n = 24/485, 5.0%). SMCHD1 mutations showed clonal expansion (Fig. 3i) and were associated with adverse OS (median = 48.2 months, P value < 1 × 10−4, log-rank test) (Fig. 3j).
To gain insight into the significance of noncoding mutations, we first identified CLL-specific regulatory elements (REs) by integrating ATAC-seq and H3K27ac profiles as well as chromatin states from publicly available primary CLL (Fig. 4a; Methods). Out of the 29,224 promoters and 56,137 enhancers identified, 90% were present in CLL as a whole, whereas the remaining 10% were specific for IGHV subgroups and were used for the IGHV subtype-specific annotation (Methods). Mapping noncoding mutations to REs (Fig. 4b; Methods), we could identify 29 untranslated regions (UTRs), 25 enhancers (23 of them cataloged by the GeneHancer database) and 72 promoters that had hotspot mutations or were recurrently mutated more frequently than expected (FDR < 0.1), defined as significantly mutated (Extended Data Fig. 5a and Supplementary Table 17). Next, we defined the candidate target genes of these 126 mutated noncoding regulatory elements. Mutations within UTRs and promoters were annotated predominately according to proximity (Methods). For enhancers, we calculated the correlation between H3K27ac levels for each regulatory elements and the gene expression levels of surrounding genes located within the same topologically associated domain (TAD) of the B cell lymphoblastoid cell line GM12878 (Methods). In total, 29 regulatory elements had target genes known to be CLL drivers or cancer drivers in the COSMIC database (Fig. 4c); 89 were linked to other genes (Fig. 4d) and 8 to none (Extended Data Fig. 5a and Supplementary Table 17). Four mutated regulatory elements were specific for u-IGHV (Extended Data Fig. 5b) and none for m-IGHV. Overall, genes targeted by mutated regulatory elements were enriched for gene ontology terms linked to the immune system, lymphocyte activation and cell death (Fig. 4e and Supplementary Table 18). Of the 29 mutated UTRs, 58% (n = 17) had a median CCF ≥ 0.5, and 83% had a CCF > 0.2, thus indicating their selection during CLL pathogenesis (Extended Data Fig. 5c). These included the 3′ UTR mutations of NOTCH1 creating a splice site that leads to increased gene expression (n = 16; FDR = 4.57 × 10−2). The NF-κB signaling gene NFKBIZ (n = 8, FDR = 2.38 × 10−2) was also found significantly mutated, confirming previous findings and known to increase levels of mRNA and protein in lymphoma. We observed clonal mutations in the 5′ UTR of IGLL5 (n = 28; FDR < 2.2 × 10−16), previously found to be associated with reduced expression. Previously unreported significantly mutated UTRs included the 5′ UTR of BCL2 (n = 6; FDR = 1.01 ×10−6, Fig. 5a). We performed RNA-seq on samples carrying these mutations (Supplementary Table 4; Methods) demonstrating that 5′ UTR mutations were associated with BCL2 overexpression (P = 4.3 × 10−2; Fig. 5b), which is noteworthy given that BCL2 inhibitors are used therapeutically in CLL. A high clonality (>0.5) was also observed when considering the 97 significantly mutated promoters and enhancers; 72% had a median CCF >0.5 and 97% of a CCF >0.2 (Supplementary Fig. 4a). Six discrete regions spanning 117 kb contained 50 variants and were annotated in the previously reported PAX5 superenhancer (Extended Data Fig. 5a and Supplementary Fig. 4b). Another region spanning 325 kb on chr3q27.2 contained seven significantly mutated enhancers and linked to BCL6 (Extended Data Fig. 5a and Supplementary Table 17). RNA-seq of eight samples with mutations in this region showed overall increased expression of BCL6, although the effect was heterogenous (Fig. 5c), suggesting that some variants are more or less pathogenic than others and variants might exert a positional effect (Fig. 5d). When considering the 72 significantly mutated promoters, we found mutations of known CLL drivers including BIRC3 (n = 31, 6.4%, FDR < 1.15 ×10−15), IKZF3 (n = 12, 2.5%, FDR = 8.16 × 10−13) and TP53 affecting splicing regions of noncoding exons/5′ UTR/promoter region (n = 2, 0.4%, FDR = 5.55 × 10−6). Next, we investigated mutations in these promoters further to identify those predicted to change chromatin state, using DeepHaem, a deep neural network trained on chromatin feature data of 73 immune cell types. Seventy-four variants were predicted to lead to a loss of open chromatin (that is, loss-of-function variants), including those in the BACH2 promoter (Fig. 6a and Extended Data Fig. 6a). A recent study showed that decreased BACH2 expression in CLL is associated with adverse outcomes. Notably, the mutations we detected in this promoter were mostly clonal (median CCF = 0.99). We therefore investigated this promoter further by performing ATAC-seq and RNA-seq (Fig. 6b) on mutated samples, when available (13 variants investigated, Supplementary Table 12; Methods) to understand the impact of these variants on chromatin accessibility and gene expression. Three variants within a 14-bp region were associated with allelic skew in the ATAC-seq compared with WGS data, demonstrating a preference for accessibility on the reference allele (Fig. 6c), which mirrored the decrease in chromatin accessibility in that region compared with WT samples (Fig. 6d). This allelic skew was also detected at the RNA level (Fig. 6e and Extended Data Fig. 6b). In addition, the same three samples also showed decreased BACH2 gene expression (Fig. 6f). Finally, we analyzed 20 cases with paired WGS, ATAC-seq and RNA-seq data (Supplementary Table 4). We identified five recurrently mutated promoters with allelic skewing of chromatin accessibility and RNA expression. Three, BTG2, CCND1 and ST6GAL1, were associated with allelic skewing towards the mutant allele, whereas ATAD1 and BIRC3 showed the opposite effect (Extended Data Fig. 6c). In the case of ATAD1, which plays a role in mitochondria protein degradation, we additionally observed reduced expression in promoter-mutated samples (P = 7.0 × 10−4) (Extended Data Fig. 6d–f). Collectively, these data suggest that a small subset of the noncoding mutations in CLL have characteristics indicative of a driver and target regulatory elements of genes that are critical for B cell development and function as well as cancer progression. However, the effects on chromatin accessibility and gene expression levels were subtle and require further indepth functional characterization.
We recalculated the occurrence of mutations in each known or putative driver in CLL by combining coding mutations, noncoding mutations in regulatory elements and CNAs (Fig. 7a and Supplementary Table 19). In total, 33 of the 58 coding, known or putative driver genes were also affected by noncoding mutations in associated regulatory elements or by CNAs. Overall, 412 (29%) of all alterations in these genes were either CNAs or affected regulatory elements. ATM and BIRC3 were most frequently targeted by genetic lesions. The median number of mutated known or putative drivers in each tumor was 2 (0–7) or 5 (0–21) when excluding or including CNA/copy neutral loss of heterozygosity (cnLOHs) and noncoding variants, respectively (Fig. 7b). A higher number of mutated genes was associated with worse PFS, especially when noncoding variants were included (Extended Data Fig. 7a,b and Supplementary Tables 15 and 16). Furthermore, the number of samples containing mutations in particular pathways also increased (by 3.3%) (Fig. 7c and Supplementary Fig. 5), in particular for the NOTCH and the transcriptional regulations pathways. We explored whether global genomic features could also be associated with clinical outcome. Firstly, we evaluated telomere length and observed that it was reduced in CLL samples compared with paired germline (median length of 2.7 kb versus 3.8 kb, P < 2.2 × 10–16, median content of 405 versus 467, P = 3.9 × 10−6, paired Wilcoxon test) (Fig. 7d and Extended Data Fig. 8a,b). Shorter telomeres were significantly enriched in samples with p53 pathway alterations (P = 1.99 × 10−36; Fig. 7d), with R/R samples compared with frontline (FDR = 5.37 × 10−7; Supplementary Table 14) and were associated with poorer PFS (FDR = 4.39 × 10−4; Supplementary Table 15 and Extended Data Fig. 8c,d). Secondly, we explored the clinical associations of mutation signatures including single base substitution (SBS), doublet base substitutions (DBS) and small insertions and deletions (ID) (Fig. 7e,f and Supplementary Table 20). Considering signatures with known or probable etiology, the most prevalent were SBS5 (clock-like), DBS11 (APOBEC activity) and ID2 followed by other clock-like signatures: SBS1 (deamination of 5-methylcytosines), SBS8, DBS2 and the AID signature SBS9. As previously documented, SBS9 was highly enriched in m-IGHV CLLs (FDR = 4.80 × 10−57, Fisher’s exact test; Supplementary Table 14), was mutually exclusive with TP53 alterations (2.29 × 10−3) and associated with good PFS (Supplementary Table 15 and Extended Data Fig. 8e). De novo signature ID83C was found associated with TP53 alterations (FDR = 2.53 × 10−2; Supplementary Table 14) and poorer PFS (1.57 × 10−2; Extended Data Fig. 8f and Supplementary Table 15). SBS1 was also associated with adverse outcome (3.70 × 10−2; Supplementary Table 15 and Extended Data Fig. 8g). Thirdly, we analyzed GC using unsupervised clustering (multiple correspondence analysis (MCA)) of 17 features related to CNAs (Extended Data Fig. 9a,b; Methods). These defined eight groups (GC1–GC8) (Extended Data Fig. 9c,d) with distinct genomic profiles (Fig. 7g and Extended Data Fig. 9e). GC4 (presenting CN losses only, n = 210) was enriched in del13q14.2 (FDR = 3.26 × 10−23). GC7 (presenting both CN gains and losses, n = 127) was associated with ten recurrent CNAs and seven known coding drivers including XPO1 (FDR = 3.98 × 10−11) and TP53 (FDR = 8.36 × 10−9). Together with GC8 (presenting trisomy, CN gains and losses, n = 15), GC7 comprised the most patients with conventional genomic complexity, defined by the presence of at least four CNAs (Extended Data Fig. 9f). None of the genomic complexity groups was significantly enriched in stereotyped subsets (Extended Data Fig. 9g). For the subset of samples with survival data (n = 243), we combined genomic complexity groups with copy number gains only, copy number losses only and both copy number gains and losses to increase statistical power. Interestingly, the eight groups were associated with different PFS and OS (Extended Data Fig. 10a,b), independent of TP53 status (Extended Data Fig. 10c,d). Furthermore, patients with both TP53 mutations and GC7/8 changes had ultrahigh-risk disease (median PFS = 8 months, median OS = 15 months) and fared worse compared with patients with TP53 mutations but no GC7/8 status (P = 0.03; Fig. 8a,b).
To evaluate the potential clinical relevance of combining different genomic features, we first used penalized multivariate regression analysis for least absolute shrinkage and selection operator. This analysis led to the identification of 56 individual genomic features that predicted PFS and/or OS including SMCHD1/del18p11.32-p11.31, which retained significance as an independent predictor of OS (Extended Data Fig. 10e and Supplementary Fig. 6a). Next, we applied non-negative matrix factorization (NMF) to identify robust subgroups of CLLs sharing subsets of the 186 different genetic alterations (Supplementary Table 21; Methods). Considering the profound clinical impact of the IGHV mutational status, we initially divided patients into m-IGHV and u-IGHV. Using this approach, we identified five distinct GS: three were u-IGHV (u-GS1, 2 and 3) and two m-IGHV (m-GS1 and 2) (Fig. 8c,d and Supplementary Table 22). When considering u-IGHV CLL (Fig. 8c and Supplementary Table 23), u-GS1 was characterized by the presence of high-risk features including TP53 disruption, GC7, short telomeres and mutations in targetable pathways such as MAPK, PI3K and apoptosis, but there was no DNA damage response signature. By contrast, u-GS2 was defined by ATM/BIRC3/del11q22.2-22.3 alterations, as well as mutations in DNA damage response pathways, but without TP53 mutations or genomic complexity as defining features. Patients in u-GS2 were predominately male. u-GS3 had a high number of mutations in known and putative coding drivers, introns and UTRs, CN gains including trisomy 12, NOTCH1 mutations, and was enriched for older patients. All three subgroups included patients with BCR IG subsets 1 and 8, which are known to be associated with aggressive disease (Supplementary Fig. 6b). Although u-GS2 and u-GS3 were clearly distinct, they were associated with similar PFS after chemoimmunotherapy (Fig. 8e). Regarding m-IGHV CLL (Fig. 8d), m-GS1 was similar to u-GS1 (cosine similarity of 0.81) and also to u-GS2 (cosine similarity of 0.7) (Supplementary Table 24). In contrast, m-GS1 was enriched for older men, BCR IG subset 2 (FDR = 2.96 × 10−6) and IGHV3-21 (FDR = 7.50 × 10−9) (Supplementary Fig. 6b), although most patients in m-GS1 did not have any defined CLL stereotype. m-GS2 had high mutation burden in enhancers, UTRs and promoters, was enriched for del13q4.2 but no other CNAs and had longer telomeres compared with the mean length in CLL. Additional clustering (Methods) further refined m-GS2 into distinct two clusters (Supplementary Fig. 6c). m-GS2 cluster 1 stood out by the high frequency of SBS9, the presence of GC4 and the absence of any other features. In comparison, m-GS2 cluster 2 had MYD88 mutations, trisomy 12 and other CN gains but no CN losses (Supplementary Table 25). Both clusters of m-GS2 had a very favorable PFS of 75% and showed a plateau of PFS, implying cure after chemoimmunotherapy (Fig. 8f). By contrast, patients belonging to m-GS1 had a shorter PFS than u-GS2/u-GS3 (median PFS = 38 versus 50 months; Fig. 8e) and there was no plateau. In our analysis of patients treated with chemoimmunotherapy, NMF subgroups could not be defined without the different acquired local and global noncoding genomic changes, since combining all known coding drivers and the four common recurrent CNAs did not cluster patients into the GSs (Supplementary Figs. 6d and 7). Based on this observation, we examined whether the NMF method could be used to prospectively and precisely assign individual patients into their subgroup for individualized outcome prediction in the clinic. Our validation, performed by subsetting the dataset (Methods) showed that a total of 15/16 m-IGHV samples and 48/51 of u-IGHV samples were assigned correctly to their respective subgroup (Fig. 8g).
Our study presents the first comprehensive WGS analysis of a large series of CLL patients requiring treatment. A main strength of our study is that it is based on patients enrolled into multicenter clinical trials, thereby reducing heterogeneity. This allowed us to not only define the genomic landscape of different stages of CLL, but also to identify mutations associated with disease relapse and transformation. Based on a strict pipeline for discovery of coding drivers, we selected the top ranked recurrently mutated genes, which comprised 36 known CLL drivers and 22 putative drivers. Only 32% of variants in those putative driver genes were missense variants, with most being truncating and stop-gain mutations. Although these putative drivers shared characteristics of known drivers (that is, damaging mutations in protein domains, impact on RNA expression, high CCF that further increased at disease progression, association with survival), we cannot exclude the possibility that some may simply represent passengers. We defined recurrent translocations (with breakpoints in WDHD1; CTNND2-ARHGAP18) and 126 candidate noncoding drivers within REs pinpointing potentially druggable target genes (NOTCH1, DTX1, NFKBIZ, NTRK2 and BACH2). For a small subset of selected noncoding candidate mutations, we were able to demonstrate a modest impact on chromatin accessibility and/or target gene expression (5′ UTR of BCL2, enhancer of BCL6, promoter of BACH2 and promoter of ATAD1). Exploring different layers of genomic data including coding, noncoding and genome-wide global changes allowed us to (1) derive a WGS-derived genomic complexity classification that further refines risk by identifying an independent ultrahigh-risk group associated with complex genomic alterations (GC7/8); (2) more precisely predict individual patients who achieve a plateau after chemoimmunotherapy (m-GS2) and are functionally cured, thereby clearly differentiating them from progressors in the m-GS1 subgroup. Ideally, only genomic features experimentally validated as disease drivers should be included in any prognostic classification system, even if they were selected by very stringent criteria as those applied in this study (see above). However, it is well recognized that some genomic features are clearly not disease drivers, yet carry prognostic relevance. For example, in CLL, the IGHV mutation status representing the cell-of-origin or telomere length reflecting proliferative activity, are associated strongly with clinical outcome, but are not considered disease drivers. In our NMF model using only the known coding drivers and recurrent CNAs did not allow us to recover the same level of discrimination as that afforded by inclusion of additional local and global noncoding information. This observation implies that the combination of coding and noncoding information in the classifier increases the precision of clinical risk prediction at least in our cohort of clinical trial patients. Although treatment algorithms for CLL are shifting away from chemoimmunotherapy to targeted agents, the subgroups we define remain potentially clinically relevant as they reflect distinct biological entities. Collectively, our study provides a springboard for downstream functional analyses of putative coding and noncoding drivers. Robust testing on independent cohorts of patients undergoing targeted therapy will be required to further establish the clinical utility of this WGS-based classifier.
All patients gave written informed consent and the study was approved under the 100,000 Genomes Project Ethics and the CLL Pilot ethics (MREC 09/H1306/54). A total of 485 patients with CLL were included in the study. A small subset was enrolled into CLEAR (CLL Empirical Antibiotic Regimen, early stage of the disease, n = 12, NCT01279252) and CLL210 (ref. ) (relapsed/refractory patients, n = 30, EudraCT 2010-019575-29). All other patients were treatment-naïve and required treatment according to iwCLL criteria. They were either fit patients receiving frontline treatment with fludarabine, cyclophosphamide, rituximab (FCR)-based treatment in ARCTIC (Attenuated dose Rituximab with ChemoTherapy In CLL, n = 61, EudraCT Number:2009-010998-20) or AdMIRe (Does the ADdition of Mitoxantrone Improve REsponse to FCR chemotherapy in patients with CLL, n = 65, EudraCT number: 2008-006342-25) or frail patients receiving ofatumumab with either bendamustine or chlorambucil chemoimmunotherapy in RIAltO (A Trial Looking at Ofatumumab for People With Chronic Lymphocytic Leukemia Who Cannot Have More Intensive Treatment, n = 92, NCT01678430). Patients recruited into FLAIR (Front-Line therapy in CLL: Assessment of Ibrutinib + Rituximab, n = 225, EudraCT 2013-001944-76) were randomized to ibrutinib alone or in combination with rituximab or venetoclax or standard first-line FCR treatment. In line with the studies’ data monitoring committees, baseline characteristics and clinical outcomes data were available only from studies once closed to recruitment (see Supplementary Table 1 for details of all patients recruited into the 100,000 Genomes Project). For patients recruited into the FLAIR study, these data are still awaited. For a subset of 25 patients, we obtained a sample taken at relapse (Supplementary Table 4). To investigate findings in more advanced disease, we reanalyzed WGS data coming from a cohort of 17 patients from whom two concurrent samples were collected: the CLL phase and the transformed phase (RS). This cohort includes samples and data generation as described in Klintman et al.. Only samples with a lymphocyte count of greater than 25 × 109 l–1 were included in the study ensuring a tumor purity greater than 80% and a median lymphocyte count of 80 × 109 l–1 (range, 33.9–166.5) (Supplementary Table 1). Peripheral blood mononuclear cells (PBMCs) and a saliva sample were collected from each patient, which served as a source of tumor and germline DNA, respectively. DNA was extracted from PBMCs and saliva using QIAamp DNA mini kit (Qiagen) and the Oragene DNA saliva kit (DNA Genotek Inc) kits, respectively, according to the manufacturer’s instructions. DNA quality was assessed using Nanodrop (Thermo Fisher Scientific) and quantified using Qubit (Thermo Fisher Scientific) technology. RNA was extracted from PBMCs using the RNeasy Mini Kit (Qiagen) according to the manufacturer’s instructions. The quality of RNA was assessed using the Agilent 4200 Tapestation System, using High Sensitivity tapes. The concentration was assessed using the GeminiTM XPS Microplate Spectroflurometer from Molecular Devices and the Quant-iT HS RNA assay.
Whole-genome 125 bp paired-end TruSeq PCR–free libraries were sequenced using Illumina HiSeq2500 technology. Raw sequencing data was aligned with using Isaac v.03.16.02.19 to GRCh38. Alignment and coverage metrics were calculated using Picard v.2.12.1 and Bwtool showing a mean read depth of 36× and 109× for normal and tumor samples, respectively. All downstream analysis of WGS data was performed on the whole dataset of 485 samples, unless otherwise stated.
Libraries were prepared from samples of 74 patients using the Illumina Stranded Total RNA Prep, Ligation with Ribo-Zero Plus, with additional custom depletion probes, using 100 ng RNA. Libraries were sequenced on a NovaSeq 6000 system (Illumina) using 100 base paired-end chemistry (108–455 million read-pairs per sample). Sequencing reads were processed and aligned to Human Reference genome GRCh38 using the Illumina Dragen RNA pipeline v.3.8.4. Gentoyping was performed using bcftools mpileup. Allele specific read counts were generated at sites of acquired SNVs determined by WGS.
ATAC-seq was performed as previously described. Briefly 7.5 × 104 cells per technical replicate were resuspended in lysis buffer (10 mM Tris-HCl, pH 7.5, 10 mM NaCl, 3 mM MgCl2, 0.1% IGEPAL CA-630). Nuclei were pelleted (500g for 10 min), PBS was discarded and nuclei were resuspended in tagmentation buffer (25 µl 2× tagmentation DNA buffer, 2.5 µl Tn5 Transposase (Illumina) and 22.5 µl water) then incubated (37 °C for 30 min). DNA was extracted using the MinElute PCR Purification Kit (Qiagen), half the DNA was amplified (NEBNext High-Fidelity 2× PCR Master Mix (New England Biolabs)) and purified with the QIAquick PCR Purification Kit (Qiagen). Libraries were sequenced using 40-bp paired-end reads (Illumina NextSeq). Reads were mapped to GRCh38 using the PEPATAC pipeline with prealignment to the mitochondrial genome and default settings. Gentoyping was performed using bcftools mpileup. Allele specific ATAC-seq read counts were generated at sites of acquired SNVs determined by WGS.
To determine the IGHV status of our cohort, we prioritized data from Sanger sequencing, followed by WGS-derived data including IgCaller results and the presence of noncanonical AID mutational signature (SBS9). This prioritizing scheme resulted in 54% (264/485) cases classified by Sanger sequencing, 40% (194/485) by the IgCaller algorithm and 6% (27/485) by the mutational signature SBS9. The correlation between these three methodologies was high, as can be seen in Supplementary Table 26. In addition, the IgCaller algorithm was used to further characterize the IG genes, including to define the IGHV3-21 rearrangement in 10% (47/485) of cases and CLL stereotypy in 27% (132/485). To assign CLL stereotypes, the IgCaller output was used as input for AssignSubsets online tool, which annotates the 19 main subsets, including subsets 1, 2, 4 and 8, as recommended by ERIC guidelines. In cases more than one rearrangement were detected, we selected the rearrangement with the highest score to define the main CLL stereotype. In cases where a rearrangement was not assigned, but there was a proximal rearrangement reported, we included this rearrangement in our analysis.
SNVs and indels were called using Strelka v.2.8.4 7 adopting default parameters. Filtering of SNVs/indels was performed as follow: depth required greater than ten and allele fraction (AF) greater than 0.05; the quality filter annotation should be ‘PASS’ and quality score greater than 30; variants with allele frequency less than 0.05 from 1KGP phase 3 1405.34_GRCh38.p8 and EXAC v.0.3 data (annotated from using Ensembl VEP GRCh38 release v.89.4 (ref. )). Additional filters according to the Illumina v.4 Genomics England annotation pipeline removed variants as follows: variants with a population germline frequency greater than 1% in either the Genomics England dataset or in the gnomAD v.3; recurrent somatic variants with a frequency greater than 5% in the Genomics England cohort; variants overlapping with LINE repeats or simple repeats found with Tandem Repeats Finder v.4.09 (ref. ); calls within 50 bp either side of an indel where at least 10% of variants have been filtered due to quality; locus depth is greater than three times mean chromosomal depth in the germline sample; contains multiple alternate alleles; germline sample is not the homozygous reference or indel Q-score is less than 30; variant quality score recalibration (VQSR) score less than 2.75; most overlapping reads do not map uniquely to variant position; within ten bases of Genomics England inhouse database or Gnomad v.3 germline indel with frequency greater than 1%; SNVs resulting from systematic mapping and calling artefacts; fails somatic panel of normal Phred cut-off (< 80). The Supplementary Notes include details on cancer cell fraction calculation as well as coding and noncoding variant annotations. In addition, it includes our approach for assigning target genes of regulatory elements, identifying of coding and noncoding candidate drivers.
The structural variant (SV) calling pipeline for detection of inversions and translocations was as follows. (1) Delly was used to call variants in each tumor–germline pair, with the following steps: complete somatic prefiltering, genotype all potentially somatic sites across all CLL germline samples, postfilter for somatic SVs using control samples. Variants with an alternative AF less than 0.05 were removed. (2) Lumpy v.0.2.13 (ref. ) and (3) Manta 0.28.0 were also used to call SVs. Variants with an alternative AF < 0.05 or for which there was any evidence in the germline were removed for consistency. (4) The pcawg-merge-sv consensus calling pipeline was adapted for this analysis. SVs supported by two or more callers were reported.
We used both DNA microarray (n = 109 samples) and WGS (n = 485 samples) to determine CNAs and observed high concordance between the two methods. Of 282 CNAs detected by WGS, 240 (85%) were also reported by DNA microarray with high confidence (Supplementary Table 6). In addition, we further reduced false positive signals using a combination of intersects between several variant callers and visual inspection as detailed below. Samples from subset of 109 patients enrolled in ARCTIC and AdMIRe trials were genotyped using HumanOmni2.5-8 BeadChip arrays (Illumina Inc.). Genotypes were called using GenomeStudiov.2009.2 (Illumina Inc.). CN gains and losses greater than 50 kb and cnLOH less than 5 Mb were reported using Nexus Copy Number v.10 (BioDiscovery, Inc.), as previously described, with the following settings (SNPRank Segmentation): significance threshold, 1 × 10–5; max contiguous probe spacing (kb), 1000.0; minimum number of probes per segment, 5; high gain, 0.6; gain, 0.2; loss, –0.2; big loss, –1.0; 3:1 sex chromosome gain, 1.2; homozygous frequency threshold, 0.95; homozygous value threshold, 0.8; heterozygous imbalance threshold, 0.4; minimum LOH length (kb), 20; percentage outliers to remove, 3%. We also inspected all genomes to scan visually for changes not identified using these analysis settings using Nexus visualization tool. In the case of WGS, Canvas v.1.3.1 (ref. ) and Manta v.0.28.0 were used to call CNAs, filtering out centromeric and telomeric regions as defined in the UCSC cytoband table. Variants reported by Canvas with a quality score less than ten were filtered out. Variants reported by Manta were filtered out as follows: (1) variants with a normal sample depth near one or both variant break-ends three times higher than the chromosomal mean, and (2) variants with somatic quality score of less than 30. For each remaining CNA, its presence and type (gain or loss) were confirmed by visually inspecting the genome-wide mean coverage and B-allele frequency data, derived from the aligned reads in 100 kb windows. Calls with continuous copy number changes of length greater than 100 kb were kept. The Supplementary Notes include details on cancer cell fraction calculation.
We calculated the total number of drivers in each patient by the following methodologies: we established (1) the total mutational burden by counting the number of functional variants (that is, with the following exonic consequences splice acceptor variant, splice donor variant, stop gained, frameshift variant, stop lost, start lost, transcript amplification, in-frame insertion, in-frame deletion, missense variant, protein-altering variant or incomplete terminal codon variant), (2) the number of mutated coding drivers (out of 58) SNVs/indels and (3) the number of mutated coding (SNVs/indels and CNAs) and noncoding drivers.
Two pathway datasets were used: PANCANCER containing 14 pathways from The NanoString PanCancer Pathways Panel and KEGG containing 23 signaling pathways. For the six pathways in common between the two lists, the PANCANCER pathway was selected, resulting in 31 unique pathways included in the analysis. We counted the number of patients with mutations per pathway considering (1) a gene panel of the coding drivers (n = 58); (2) the exome (coding drivers plus exonic mutation with high impact according to VEP annotations: splice_acceptor_variant, splice_donor_variant, stop_gained, frameshift_variant, stop_lost, start_lost); (3) a larger driver panel containing both coding drivers and regulatory candidate drivers (n = 58 + 126) and (4) all of the above combined (coding and noncoding drivers plus exonic mutation with high impact according to VEP annotations).
Telomere analysis was carried out on all 485 CLL tumor-normal pairs. Telomere content was estimated using Telomere Hunter v.1.1.0 (ref. ). Telomere content is normalized by the total number of overall reads that comprise a ‘telomere-like’ GC-content range (48–52%). Telomere length in basepairs was estimated using Telomerecat v.1.0 (ref. ). We found that telomere content assessed using Telomere Hunter and telomere length assessed using Telomerecat were highly correlated (P = 0.84, P < 2.2 × 10–16, Extended Data Fig. 8a). We compared the telomere lengths and contents between CLL samples and matched saliva samples as germline, considering that different cell types can naturally present different telomere lengths.
Chromothripsis was identified using Shatterseek, which aims to detect candidate regions on the basis of oscillating copy number states (using CNAs as previously described), as well as intersection with clusters of interleaved structural variants (SVs; that is, deletions, duplications, inversions and translocations) identified from the SV consensus pipeline previously described. Potential regions of chromothripsis were classified as ‘high confidence’ or ‘low confidence’ using criteria as per Cortés-Ciriano et al..
Extraction of SBS, DBS and small ID signatures was performed using SigProfilerExtractor v.1.0.1810 (ref. ). SigProfilerExtractor de novo signature extraction and decomposition were carried out according to default parameters, with potential de novo extracted signature solutions tested between 1 and 25 signatures. Signatures were referenced to the Catalogue of Somatic Mutations in Cancer (COSMIC) v.3; SigProfilerExtractor signatures were decomposed based on a cosine similarity greater than 0.9. Following decomposition to COSMIC signatures, SigProfilerExtractor estimated the overall signature contributions per tumor, as well as the per tumor signature estimates for each mutation context. Through associating these context estimates back to the original mutations, signature estimates were attributed to individual driver mutations, as well as genomic regions (exome, promoters, UTRs, and so on).
We investigated the presence of GC using an unsupervised multiple correspondence analysis with FactoMineR. We included 17 genomic measures as binary data, including variables binned as less than median or greater than or equal to median: number of SNVs, number of indels, telomere lengths, telomere content and variables binned as presence/absence: SV breakpoint, CNA, CN gain, CN loss, cnLOH, trisomy, aneuploidy, CN gain excluding trisomy, CN loss excluding aneuploidy, cnLOH excluding whole chromosome cnLOH, inversion, translocation and chromothripsis.
All genomic alterations derived from WGS were combined and included as follows: noncoding candidate drivers mutated in more than 5% of samples; coding drivers were combined according to the presence of an SNVs/indels and CNAs (union); recurrent CNAs that significantly co-occurred (mean square contingency coefficient, mu > 0.3) and defined in the same chromosome were combined (union). In addition, only genomic alterations with at least five occurrences across all the samples were included in the analysis. In total, 186 genomic remained including 58 coding drivers, 36 recurrent CNAs, 44 noncoding drivers, 12 pathways affected by genetic alterations, 28 global genomic features and mutational signatures, and eight genomic complexity groups (Supplementary Table 21). We tested for enrichment (two-sided Fisher’s exact test, FDR ≤ 0.05) of each genomic alteration in several known risk factor and disease state groups for samples with available data: age (195 samples < median age versus 216 samples ≥ median age); sex (338 male versus 136 female); disease stage (443 frontline versus 30 R/R); TP53 status (420 WT versus 65 disrupted); IGHV mutational status (197 hypermutated versus 288 unmutated); minimal residual disease (MRD; 59 negative versus 57 positive); BCR IG subset 2 (33 presenting 2 versus 450 others); IGHV3-21 rearrangement (47 with versus 436 without).
We examined the relationship between each of the 186 genomic features as detailed above (Supplementary Table 21) and patient outcomes using Cox proportional hazards models on 243 patients for PFS and 245 patients for OS. FDR-corrected P values were reported as significant if less than 0.05. In addition, several particular comparisons with more than two groups were performed using Kaplan–Meier curves and the log-rank test. These were: number of mutated drivers, the eight genomic complexity groups and the combination of different structural rearrangements. We also performed a multivariate analysis using penalized Cox regression, as implemented in the R package glmnet, to find a minimal set of predictors with maximal predictive power. An optimal value of the penalization parameter λ was selected using leave-one-out cross-validation; specifically, the value of λ that minimizes the cross-validation error.
All 186 genomic features, as well as IGHV status including percent homology to germline (labeled MS), age and sex were selected for unsupervised clustering using non-negative matrix factorization using the NMF v.0.22.0 R package with the offset method. Data were converted to a binary matrix using either presence or absence of a feature, or above or below the mean to avoid a mixture of binary and nonbinary data (Supplementary Note). After removal of samples without age information, samples were divided into m-IGHV (n = 168) and u-IGHV (n = 243) as defined above. The number of permitted NMF clusters in either the m- or u-IGHV subset was determined using a combination of rank estimation methods including the cophenetic correlation coefficient. Data were randomized and the ranks estimated for comparison to avoid overfitting. NMF was carried out on each IG subset of samples separately to produce GSs. DeconstructSigs v.1.9.0 (ref. ) was designed to use the mutation catalog of a sample to define the linear combination of COSMIC signatures that best reconstruct that sample’s mutational profile. Here, we used this tool to define the linear combination of GSs calculated using the NMF method that best reconstruct the genomic features of a sample. The proportions of each GS within all patients were then clustered using mclust v.5.4.6 (ref. ) and assigned a cluster that maximized parsimony whilst still producing an adequate prediction. The defined GSs were then compared with known subgroups such as BCR IG subsets and patients harboring an IGHV3-21 rearrangement. Testing of the method was carried out as follows: Data were randomly split into two trial groups each representing 50% of the dataset: and further divided into m-IGHV and u-IGHV CLL. The NMF was then performed on all genomic features on each group and evaluated using cosine similarity between group signature matrices (Supplementary Table 22); all samples used for NMF were split into 80% (m-IGHV: n = 133, u-IGHV: n = 195) training and 20% (m-IGHV: n = 34, u-IGHV: n = 49) testing at random. The NMF was performed on the training data as described above to produce GS matrixes (m-GS, u-GS). The training data were then assigned to a GS using deconstructSigs to identify the combination of GSs that best reconstructed a sample’s genomic feature matrix and then assigning the signature that occurred at the highest percentage. The signature assigned to the test samples was then compared with the signatures assigned to those same individuals when 100% of data was used for both training and testing (Fig. 8g).
Plotting of data was performed using tidyverse v.1.3.0 (refs. ) in R v.3.6.2 (ref. ). Mutation hotspot graphics were plotted using the package GenVisR v.1.18.1 (ref. ). Lollipop plots were plotted with the MutationMapper from cbioportal accessible from https://www.cbioportal.org/mutation_mapper. Genomic views were prepared using the UCSC genome browser.
The sample size calculation was critical to the success of this program. Our power calculations considered the heterogeneity of CLL and a background somatic mutation frequency of 0.8 mutations per megabase. This means that, to reliably detect somatic mutations recurring in 2% of patients with CLL, we need to sequence approximately 500 CLL genomes (Supplementary Fig. 8). No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-022-01211-y.
Supplementary InformationSupplementary Methods, Methodology, Figs. 1–7 and individual consortia authors. Reporting Summary Peer Review File Supplementary TableSupplementary Tables. |
PMC9649452 | Biao Zuo,Ling Zuo,Xu-Qin Du,Su Yuan,Chen Xuan,Yu-Di Zhang,Zhi-Wei Chen,Wen-Fu Cao | Yiqi Huayu decoction alleviates bleomycin-induced pulmonary fibrosis in rats by inhibiting senescence 10.3389/fphar.2022.1033919 | 28-10-2022 | Yiqi Huayu decoction,pulmonary fibrosis,network pharmacology,cellular senescence,senescence-associated secretory phenotypes | Overview: In treating pulmonary fibrosis (PF), traditional Chinese medicine (TCM) has received much attention, but its mechanism is unclear. The pharmacological mechanisms of TCM can be explored through network pharmacology. However, due to its virtual screening properties, it still needs to be verified by in vitro or in vivo experiments. Therefore, we investigated the anti-PF mechanism of Yiqi Huayu Decoction (YHD) by combining network pharmacology with in vivo experiments. Methods: Firstly, we used classical bleomycin (BLM)-induced rat model of PF and administrated fibrotic rats with YHD (low-, medium-, and high-dose). We comprehensively assessed the treatment effect of YHD according to body weight, lung coefficient, lung function, and histopathologic examination. Second, we predict the potential targets by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) combined with network pharmacology. In brief, we obtained the chemical ingredients of YHD based on the UHPLC-MS/MS and TCMSP database. We collected drug targets from TCMSP, HERB, and Swiss target prediction databases based on active ingredients. Disease targets were acquired from drug libraries, Genecards, HERB, and TTD databases. The intersecting targets of drugs and disease were screened out. The STRING database can obtain protein-protein interaction (PPI) networks and hub target proteins. Molecular Complex Detection (MCODE) clustering analysis combined with enrichment analysis can explore the possible biological mechanisms of YHD. Enrichment analyses were conducted through the R package and the David database, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Reactome. Then, we further validated the target genes and target proteins predicted by network pharmacology. Protein and gene expression detection by immunohistochemistry, Western blot (WB), and real-time quantitative PCR (rt-qPCR). Results: The results showed that high-dose YHD effectively attenuated BLM-induced lung injury and fibrosis in rats, as evidenced by improved lung function, relief of inflammatory response, and reduced collagen deposition. We screened nine core targets and cellular senescence pathways by UHPLC-MS/MS analysis and network pharmacology. We subsequently validated key targets of cellular senescence signaling pathways. WB and rt-qPCR indicated that high-dose YHD decreased protein and gene expression of senescence-related markers, including p53 (TP53), p21 (CDKN1A), and p16 (CDKN2A). Increased reactive oxygen species (ROS) are upstream triggers of the senescence program. The senescence-associated secretory phenotypes (SASPs), containing interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-β1 (TGF-β1), can further exacerbate the progression of senescence. High-dose YHD inhibited ROS production in lung tissue and consistently reduced the SASPs expression in serum. Conclusion: Our study suggests that YHD improves lung pathological injury and lung function in PF rats. This protective effect may be related to the ability of YHD to inhibit cellular senescence. | Yiqi Huayu decoction alleviates bleomycin-induced pulmonary fibrosis in rats by inhibiting senescence 10.3389/fphar.2022.1033919
Overview: In treating pulmonary fibrosis (PF), traditional Chinese medicine (TCM) has received much attention, but its mechanism is unclear. The pharmacological mechanisms of TCM can be explored through network pharmacology. However, due to its virtual screening properties, it still needs to be verified by in vitro or in vivo experiments. Therefore, we investigated the anti-PF mechanism of Yiqi Huayu Decoction (YHD) by combining network pharmacology with in vivo experiments. Methods: Firstly, we used classical bleomycin (BLM)-induced rat model of PF and administrated fibrotic rats with YHD (low-, medium-, and high-dose). We comprehensively assessed the treatment effect of YHD according to body weight, lung coefficient, lung function, and histopathologic examination. Second, we predict the potential targets by ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) combined with network pharmacology. In brief, we obtained the chemical ingredients of YHD based on the UHPLC-MS/MS and TCMSP database. We collected drug targets from TCMSP, HERB, and Swiss target prediction databases based on active ingredients. Disease targets were acquired from drug libraries, Genecards, HERB, and TTD databases. The intersecting targets of drugs and disease were screened out. The STRING database can obtain protein-protein interaction (PPI) networks and hub target proteins. Molecular Complex Detection (MCODE) clustering analysis combined with enrichment analysis can explore the possible biological mechanisms of YHD. Enrichment analyses were conducted through the R package and the David database, including the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Reactome. Then, we further validated the target genes and target proteins predicted by network pharmacology. Protein and gene expression detection by immunohistochemistry, Western blot (WB), and real-time quantitative PCR (rt-qPCR). Results: The results showed that high-dose YHD effectively attenuated BLM-induced lung injury and fibrosis in rats, as evidenced by improved lung function, relief of inflammatory response, and reduced collagen deposition. We screened nine core targets and cellular senescence pathways by UHPLC-MS/MS analysis and network pharmacology. We subsequently validated key targets of cellular senescence signaling pathways. WB and rt-qPCR indicated that high-dose YHD decreased protein and gene expression of senescence-related markers, including p53 (TP53), p21 (CDKN1A), and p16 (CDKN2A). Increased reactive oxygen species (ROS) are upstream triggers of the senescence program. The senescence-associated secretory phenotypes (SASPs), containing interleukin 6 (IL-6), tumor necrosis factor-alpha (TNF-α), and transforming growth factor-β1 (TGF-β1), can further exacerbate the progression of senescence. High-dose YHD inhibited ROS production in lung tissue and consistently reduced the SASPs expression in serum. Conclusion: Our study suggests that YHD improves lung pathological injury and lung function in PF rats. This protective effect may be related to the ability of YHD to inhibit cellular senescence.
Pulmonary fibrosis (PF) is a chronic, progressive, and irreversible fibrotic lung disease, and its prominent feature is matrix stiffening (Moss et al., 2022). Symptoms of PF are mainly characterized by worsening dyspnea and ventilatory and ventilatory dysfunction (Martinez et al., 2017). Patients with PF have an inferior prognosis, with hypoxemia and respiratory failure leading to death (Moss et al., 2022). With a median survival of only 2.5–3.5 years, PF’s terrible prognosis rivals some of the worst malignancies. With a high mortality rate and incurable properties, PF has received more attention, especially after the peak of the Coronavirus disease outbreak in 2019 (COVID-19) (Gentile et al., 2020; Spagnolo et al., 2020; Aul et al., 2021; Tanni et al., 2021). However, there are currently no radical drugs in clinical practice to cope with this devastating lung disease (Spagnolo et al., 2021). Therefore, the development of effective treatment for PF is urged. PF is an aging-related disease, and cellular senescence is crucial in the aging phenotype (Schafer et al., 2017). The accelerated senescence of alveolar epithelial cells is increasingly recognized as a primary cause of epithelial dysfunction and PF pathogenesis (Yao et al., 2021). Senescent alveolar epithelial cells not only lose their ability to regenerate and repair, but also exert deleterious effects on neighboring cells by secreting various pro-inflammatory cytokines, profibrotic factors, and growth factors (Kadota et al., 2018). Those secretions are defined as senescence-associated secretory phenotypes (SASPs). Recently, senotherapeutics and senolytics have become emerging hotspots (Justice et al., 2019; Merkt et al., 2020; Wissler Gerdes et al., 2021). Because TCM can improve patient quality of life and survival rate, it has enormous potential in treating PF(Li and Kan, 2017). Yiqi Huayu decoction (YHD), a modified traditional Chinese prescription, has been applied to the clinical treatment of PF(Zhang et al., 2018). YHD consists of two botanical drugs: Astragalus mongholicus Bunge and Salvia miltiorrhiza Bunge. This prescription can improve PF by tonifying the lung, benefiting Qi, activating blood circulation, and removing blood stasis. Increasing evidence suggests that YHD can potentially prevent or treat various fibrotic diseases by suppressing inflammatory responses, inhibiting myofibroblast activation, and promoting collagen degradation (Qin et al., 2018; Han et al., 2021). The clinical application of YHD in PF has been validated, but its mechanism is unclear because its components and targets are complex. Therefore, it is valuable to further investigate the mechanism of YHD in treating PF. In our study, we used a classical bleomycin-induced rat model of PF and administered different doses of YHD. The therapeutic effect of YHD was evaluated comprehensively by body weight, lung coefficient, survival rate, lung function, and pathological sections of rats. Network pharmacology is an effective tool for predicting complex pharmacological mechanisms and has been widely used by TCM researchers. Through component identification (UHPLC-MS/MS analysis) and network pharmacology, we screened out key targets and signaling pathways to reveal the mechanism of YHD in treating PF. We then further validated key markers on cellular senescence signaling pathways with different approaches. An overview of our study is shown in Figure 1.
YHD granules are provided by Yifang Pharmaceutical (Guangdong, China) to ensure accurate dosage. Each herb underwent a series of processes, including decocting, extracting, concentrating, drying, and finally preparing into granules. These granules were identified by professor Wen-fu Cao of Chongqing Medical University. The granules sample are saved in Chongqing Key Laboratory of Traditional Chinese Medicine for Prevention and Cure of Metabolic Diseases. Detection kits for hydroxyproline are available from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Detection kits for reactive oxygen species (ROS) are available from Nanjing Fengfeng Biomedical Technology Co. (Nanjing, China). The TGF-β1, IL-6, and TNF-α detection kits were provided by Jiubang Biotechnology (Fujian, China). All reagents for rt-qPCR were from Takara Bio (Kusatsu, Japan). All primers for qPCR were synthesized by Tsingke Biology Technology (Beijing, China). Acetonitrile, methanol, and formic acid (LC-MS grade) were purchased from CNW Technologies (Dusseldorf, Germany). Antibodies against GAPDH (ab181602), p16 (ab51243), and P21 (ab109199) were offered by Abcam (Cambridge, United Kingdom). Antibodies against P53 (sc-99) were purchased from Santa Cruz. Antibodies against Collagen I (GB11022-3), Collagen Ⅲ(GB111629), and α-SMA (GB111364) were acquired from Servicebio (Wuhan, China).
YHD consists of two botanical drugs: Astragalus mongholicus Bunge (AM) and Salvia miltiorrhiza Bunge (SM). Table 1 shows detailed YHD information and composition ratios. All original medicinal materials are made into granules according to the procedures of the Chinese Pharmacopoeia. Specifically, the original herbs were soaked in 7 times the volume of purified water for 30 min, brought to a boil over high heat, and continued to decoct for 60 min. After filtering off the liquid, water was added again, brought to a boil over high heat, and the decoction was continued for 40 min. Finally, the liquid obtained from the two decoctions was mixed, dried, concentrated, and packaged into granules. 60 g of raw astragalus was concentrated into 12 g of granules, and 30 g of salvia was concentrated into 5.4 g of granules. The YHD oral liquids were made by mixing granules of single botanical drugs with double-distilled water and dissolving them. The daily dose of YHD granules in adults is 0.29 g/kg. Rats’ daily dose was calculated as 1.827 g/kg using the conversion ratio of surface area between rats and humans (6.3). This dose was used as the medium dose. The low dose of YHD was 0.9135 g/kg, whereas the high dose was 3.654 g/kg.
100 mg YHD sample dissolved in 500ul extraction solution (Methanol: water = 4:1, the internal standard concentration is 10 ug/mL). Vortex for 30 s, sonicate at 45 Hz for 4 min, and sonicate in an ice-water bath for 1 h; After standing at - 40°C for 1 h, the sample was centrifuged at 4°C, 12,000 rpm (centrifugal force 13,800 (×g), radius 8.6 cm) for 15 min; The supernatant was filtered through a 0.22 um microporous membrane. 5ul filtered supernatant was detected for UHPLC-MS/MS analysis.
Thirty adult male Sprague-Dawley rats (weighing 200–220 g) were used as research subjects. All animals were supplied by the Laboratory Animal Center of Chongqing Medical University and kept in a specific pathogen-free room at the center. The laboratory was maintained at 22.9 (°C) with a relative humidity of 46.4% and a 12-h dark photoperiod. All rats were fed standard chow and water for 7 days in the laboratory before the experiment.
After 7 days of adoption, all rats were randomly divided into five groups (n = 6 in each group) as follows: control group, model group, YHD at low-, medium-, and high-dose group (0.9135,1.827, 3.654 g/kg/day). On day 0, rats in the control group were treated with an equal volume of saline, and the other groups’ rats were intratracheally injected with BLM dissolved in saline. Briefly, rats were anesthetized (2% sodium pentobarbital) and instilled with BLM solution (5 mg/kg) by the intratracheal route. From day 1 to day 21, rats in the control and model groups were treated with saline, while rats in the low-, medium-, and high-dose YHD groups were treated with the corresponding doses of YHD. All rats were sacrificed on day 21st. The lower 1/3 of the left lung was fixed with 4% paraformaldehyde and sectioned for H&E, MASSON, and immunohistochemical staining. The remaining lung tissues were stored at -80°C for future analyses.
On day 21st, rat lung function was measured using the FinePointe non-invasive testing system (FinePointe™ NAM, Data Sciences International). Specifically, the rat is secured in a closed box with an airflow monitoring apparatus attached to one end of the box. Different respiratory parameters such as breathing frequency (F), minute volume (MV), tidal ventilation volume (TV), specific airway conductance (sGaw), functional residual capacity (FRC), and specific airway resistance (sRaw) were derived from the airflow. The average values calculated by the system are counted as raw data.
Hydroxyproline (HYP) contents in lung tissues were measured using the Hydroxyproline Assay Kit (Nanjing Jiancheng Corp. Nanjing, China) according to the manufacturer’s instructions. In brief, 50 mg of lung tissue was mixed with 1 ml of hydrolase and placed in a boiling water bath for 20 min. The supernatant was centrifuged at 3,500 rpm/min for 10 min, and the absorbance was measured at 550 nm. The detection of reactive oxygen species (ROS) in lung tissue is according to the kit instructions. Specifically, 50 mg of lung tissue was homogenized in 1 ml of homogenizing Buffer, and the supernatant was harvested by centrifugation. We sequentially added 200 ul of supernatant and 2 μl of liquid containing the fluorescent probe to the 96-well plate. Fluorescence intensity was measured at excitation wavelength 510 nm and emission wavelength 610 nm. After measuring the protein concentration of the supernatant, the fluorescence intensity/protein concentration indicates the intensity of tissue reactive oxygen species.
The left lung tissues were fixed in 4% paraformaldehyde for 24 h, embedded in paraffin. According to the manufacturer’s instructions, histological sections were used for hematoxylin-eosin (H&E), Masson, and immunohistochemical (IHC) experiments. Alveolitis was assessed with H&E-stained sections, and fibrosis was assessed with Masson’s trichrome-stained sections. The inflammation and fibrosis scores were assessed quantitatively based on previous literature (Szapiel et al., 1979). All the sections were analyzed by microscopy (BX53, Olympus Corporation, Japan).
Paraffin sections were deparaffinized, stained with hematoxylin staining solution for 5 min, rinsed with distilled water, dehydrated with graded alcohol, and then stained with eosin staining solution for 5 min, and dehydrated and sealed. Sections were rinsed with 1% glacial acetic acid for differentiation, dehydrated with absolute ethanol, and fixed.
Sections were immersed in Masson A solution overnight and washed with water. And the sections were filled into a dye solution mixed with Masson B solution and Masson C solution in equal proportions, soaked for 1 min, washed with water, differentiated with 1% hydrochloric acid alcohol, and washed with water. Next, the sections were plunged into Masson D solution for 6 min, rinsed with water, and plunged into Masson E solution for 1 min. After a slight drain, the sections were directly stained with Masson F solution for 2–30 s. Sections were rinsed with 1% glacial acetic acid for differentiation, dehydrated with absolute ethanol, and fixed.
We incubated the sections at 95°C for 20 min with citrate antigen retrieval solution. These sections were incubated with primary antibodies Collagen-I (GB11022-3), Collagen-Ⅲ(GB111629), and α-SMA (GB111364) overnight, and then secondary antibodies were incubated with these sections for 50 min. Image-Pro Plus software (Media Cybernetics, United States) calculated cumulative optical densities.
The mRNA expression levels of p53, p21, p16, and GAPDH in lung tissue were detected by rt-qPCR. Trizol reagent (Takara) extracted total RNA from lung tissue following the manufacturer’s protocol. Reverse transcription was carried out using the PrimeScript RT Reagent Kit (Takara). qRT-PCR was performed with the SYBR PrimeScript PCR kit II (Takara). A housekeeping gene, GAPDH, was used to standardize Ct values. Fold changes in mRNA expression were calculated by relative quantification (2−ΔΔCt). Primer sequences used for PCR are shown in Table 2.
Hub target proteins were subsequently validated by western blotting using specific antibodies. Briefly, we extracted proteins from rat lung tissue using lysates and protease inhibitors. Protein content was quantified with the BCA reagent kit. Proteins were separated by electrophoresis on SDS-PAGE gels and then transferred to the PVDF membranes. The membrane and primary antibodies were incubated overnight at 4°C after blocking with 5% skimmed milk. Following 5 washes with TBST, the membrane was incubated for 1 h with HRP-conjugated secondary antibodies. Finally, a chemiluminescence reagent was added to the membrane surface, and the imaging system visualized the target protein.
A quantitative ELISA was used to determine the levels of TNF-α, TGF-β1, and IL-6 in serum. The ELISA kits were all purchased from Jiubang Biotechnology (Fujian, China). All operations were performed strictly following the kit instructions. Briefly, serum samples (10 μl) and diluent solutions (40 ul) were separately added to the wells on a 96-well plate. Next, each well was added to HRP-labelled secondary antibodies and then incubated at 37°C for 60 min. Finally, we measured the optical density of each hole at 450 nm after adding 50 ml termination solution within 15 min.
First, we screened the active ingredients of YHD from UHPLC-MS/MS analysis and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (Ru et al., 2014). The screening criteria are that the oral bioavailability (OB) ≥ 30% and the drug-like (DL) index ≥0.18 (Li and Kan, 2021; Xia et al., 2020). Based on the active compound, we searched compound-related target genes in different databases (TCMSP, HERB, Swiss Target Prediction) (Gfeller et al., 2014; Ru et al., 2014; Fang et al., 2021). With the help of Uniprot, an AMSM target gene set is acquired after gene symbol annotation (Apweiler et al., 2004). Then, we searched PF-related genes in four databases: the Drugbank database, Genecards database, HERB database, and TTD database (Rebhan et al., 1997; Chen et al., 2002; Wishart et al., 2008; Fang et al., 2021). By combining the search results, we established a set of PF-related genes. PF-related genes and AMSM target genes were intersected to determine the common targets between drugs and diseases. TCMSP database (https://www.tcmsp-e.com/). Swiss Target Prediction web server (http://www.swisstargetprediction.ch). HERB database (http://herb.ac.cn/). Uniprot database (http://beta.uniprot.org/). Drugbank database (https://go.drugbank.com). Genecards database (https://www.genecards.org). TTD database (http://db.idrblab.net/ttd/).
STRING database was used to construct the PPI network based on the common gene set (Damian et al., 2011). After setting the parameter as high confidence (0.9), the PPI network from STRING was imported into Cytoscape for further analysis. We applied two methods (CytoNca and CytoHubba) to screen the core subnetwork. Firstly, we used CytoNca(a plugin in Cytoscape) to analyze the PPI network (Tang et al., 2015). In detail, based on the primary score file calculated by CytoNca, we constructed a primary subnetwork consisting of the top 10 genes. Second, we used CytoHubba (another plugin in Cytoscape) to analyze the PPI network again (Chin et al., 2014). This approach analyzed the top 10 genes in the PPI network and constructed the critical subnetwork without checking the first-stage nodes. STRING (https://www.string-db.org). Cytoscape (version 3.8.2).
A series of enrichment analyses were performed to determine the underlying mechanism, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome pathway analysis (Kanehisa and Goto, 2000; Harris et al., 2004; Joshi-Tope et al., 2005). KEGG and GO enrichment analysis was conducted using R’s “ClusterProfile” package (version 3.4.0) (Yu et al., 2012). The enrichment analysis of the Reactome pathway is completed using the DAVID database (Dennis et al., 2003). MCODE, a plugin for Cytoscape, carried out the clustering analysis. MCODE is mainly based on PPI network density and K-score for clustering analysis (The following parameters were set: degree cutoff = 2, node score cutoff = 0.2, K-score = 2, max. depth = 100) (Bader and Hogue, 2003).
The statistical analyses were conducted using GraphPad Prism version 8.0 (GraphPad Software, United States). All raw data are shown as mean ± SEM. A one-way ANOVA was performed after satisfying a normal distribution, followed by a Tukey multiple comparison test. p-values less than 0.05 were considered statistically significant.
In order to identify the chemical components in Yiqi Huayu decoctions (YHD), UHPLC-MS/MS was used to analyze the samples. The total ion chromatograms (positive and negative) of YHD were obtained by UHPLC-MS/MS(Figures 2A–B). A total of 563 compounds were identified from the YHD samples. These compounds included 143 terpenoids, 86 flavonoids, 48 phenylpropanoids, 47 alkaloids, 45 miscellaneous, 34 phenols, 19 fatty acyls, 17 organic acids and their derivatives, 16 amino acid derivatives, 12 coumarins, etc. See the Supplementary Table S1 for detailed ingredient information. Figure 2 demonstrates six key compounds: 1) Prunetin, 2) Tectochrysin, 3) Tanshinone IIa, 4) Rosmarinic acid, 5) Protocatechualdehyde, and 6) Acacetin.
Rat survival rates, body weights, and lung coefficients were recorded to verify the therapeutic effects of YHD on PF. Following intratracheal instillation of BLM at day 0, total two rats in the model group died (on day 2 and day 6, respectively), and one in the low-dose YHD group died (on day 2). Conversely, no rats died in the remaining three groups (Figure 3A). The lung coefficient can reflect the degree of inflammation and edema in the lung tissue (Shen et al., 2020). Rats in the model and YHD-L groups had a higher lung coefficient than those in the control group (p < 0.001). The medium- and high-dose YHD reduced the lung coefficients of fibrotic rats (Figure 3B). The body weight reflects the health status of fibrotic rats. Compared to the normal control group, the rats in the model group showed significant early weight loss (on day 7) and later slowed weight growth (on day 21). Different doses of YHD had a tendency to improve rat body weight, although there was no statistical difference (Figure 3C). Pathological staining can more intuitively observe alveolar structure and degree of fibrosis. H&E staining showed obvious morphologic changes in the lung in the model group, including thickening of the alveolar septum, alveolar structure destruction, and heavy inflammatory cell infiltration in the alveolar space and lung interstitium (Figure 3D). In addition, the lung tissues of rats in the model group also displayed increased collagen deposition, as indicated by the increased blue fiber bundles in Masson-stained lung sections. The results of the inflammation and fibrosis scores showed that YHD treatment reduced the infiltration of inflammatory cells and decreased the deposition of collagen fibers. A significant dose effect was seen between the low-dose and high-dose groups (Figures 3E,F).
It is well known that PF can compromise lung function. Since high-dose YHD exhibited better therapeutic effects, we evaluated the improvement effect of high-dose YHD on lung function. High airway resistance is indicated by increased specific airway resistance (sRaw) and decreased specific airway conductance (sGaw). Decreased functional residual capacity (FRC) suggests alveolar contraction or collapse. Lung function results showed that sRaw and minute volume (MV) were elevated, while sGaw and FRC were decreased in model rats compared to control rats. After using high-dose YHD, sGaw and FRC increased, and sRaw decreased compared with the model rat (Figures 4A–D). These data indicated that high-dose YHD alleviated BLM-induced alveolar collapse and decreased airway resistance. High expression of α-SMA and excessive collagen deposition (mainly type I and type III) are characteristics of PF. By immunohistochemical staining of lung sections, we observed that a large amount of collagen (type I and type III) and α-SMA proteins were deposited in the lung interstitium of rats in the model group. However, high-dose YHD significantly reduced the expression of collagen protein (type Ⅰ and Ⅲ) and α-SMA (Figures 4F–I). In addition, hydroxyproline (HYP), an essential component of collagen synthesis, was also measured (Medugorac, 1980). The lung tissue of rats in the model group contained more HYP compared with the control group (p < 0.001). Conversely, high-dose YHD decreased lung hydroxyproline levels in fibrotic rats (Figure 4E). These results indicate that YHD can reduce collagen deposition and activation of myofibroblasts.
YHD effective compounds were collected from UHPLC-MS/MS analysis and the TCMSP database. Compounds that meet the requirements of oral bioavailability (OB) ≥ 30% and drug-like (DL) index≥0.18 can be considered potential drug candidate active ingredients (Xia et al., 2020). We screened 20 active compounds of Astragalus menbrunucrus (AM) and 65 active compounds of Salvia miltiorrhiza (SM). Supplementary Table S2 contains detailed information on these active compounds. Then, using the screened active compounds, we identified 209 a.m. target genes and 137 S M target genes from databases (TCMSP, HERB, and Swiss Target Prediction). In addition, we identified 5,917 pulmonary fibrosis (PF) target genes from databases (Drugbank, Genecards, HERB, and TTD). Finally, by taking the intersection, we get 88 overlapping common protein targets among AM, SM, and PF (Figure 5A). These common targets were used for further analysis. Supplementary Table S3 includes all target detail information.
We could identify hub target proteins through the protein-protein interaction (PPI) network. Figure 5B shows the PPI network obtain from the STRING database. The nodes in red represent key hub proteins. In addition, we used two algorithms (CytoHubba and CytoNCA) to find the critical top ten proteins in 88 common targets (Figures 5C,D). Finally, by taking the intersection, we found nine key proteins (Figure 5E): JUN, TP53, MAPK1, MYC, RELA, MAPK14, ESR1, IL-6, and TNF. Enrichment analysis can identify key signaling pathways and biological functions. The categories covered by GO enrichment analysis were molecular function (MF), cellular component (CC), and biological process (BP). Based on the 88 common targets, we obtained 174 KEGG pathways and 2513 GO terms, including 2266 BP, 81 CC, and 166 MF. As shown in Figure 6A, the top 10 terms in BP, CC, and MF are listed. The most enriched BP terms were associated with oxygen metabolism, including response to oxygen levels, response to reactive oxygen species, reactive oxygen species metabolic process, response to hypoxia, and response to decreased oxygen levels. We screened out the top 10 signaling pathways related to PF (Figure 6B). These pathways are mainly related to the inflammatory response (IL-17 signaling pathway and TNF signaling pathway), immune response (Th17 cell differentiation and T cell receptor signaling pathway), and cell cycle regulation (apoptosis and cellular senescence). Molecular Complex Detection (MCODE) analysis can screen out key functional modules of molecular networks. Through MCODE, we obtained 5 key cluster subnetworks (Figure 6C). Each clustering subnetwork is scored, with higher scores representing more critical. We performed Reactome pathway analysis on the first cluster subnetwork and found that cellular senescence signaling pathways were enriched again (Figure 6D). Figure 6E shows a cellular senescence signaling pathway diagram, with red labels representing 13 target proteins enriched in this pathway. Figure 6F shows the top ten proteins among the 13 senescence-related proteins, and the upper right table shows their degree rankings.
Because the cellular senescence pathway is enriched multiple times, we examined the key markers TP53 (p53) and CDKN1A (p21) on this pathway. In addition, CDKN2A (p16), another key marker of cellular senescence, was detected despite not being captured by network pharmacology. On both the protein and gene levels, the lung tissues of the model rats showed significantly higher expression of p53, p21, and p16 than the control group. Nevertheless, high-dose YHD significantly decreased the expression of these senescence markers (Figures 7B–H). Increased reactive oxygen species (ROS) can induce cellular senescence, so we examined ROS expression in lung tissue (Figure 7A). As expected, the model rats had significantly higher levels of ROS in their lung tissue than the control rats (p < 0.001), while high-dose YHD could reduce its expression (p < 0.05). Senescent cells can secrete various cytokines collectively known as the SASPs, including TGF-β1, TNF-α, and IL-6. SASPs can reinforce the senescence program and influence the tissue microenvironment. Therefore, we detected the contents of SASPs (TGF-β1, TNF-α, and IL-6) in rat orbital blood by ELISA once a week. ELISA results showed that the levels of TGF-β1, TNF-α, and IL-6 in the serum of model rats were significantly increased on day 7, 14, and 21 compared with the control group (p < 0.001). Conversely, different doses of YHD could decrease the content of these SASPs and show a significant gradient effect (Figures 8A–I). Taken together, these results suggest that YHD can inhibit senescence and SASPs.
There is currently no cure for PF, which frequently leads to organ failure and death (Moss et al., 2022). TCM is an excellent resource for drug innovation discovery, and we try to find shortcuts to treating PF from this treasure trove. Practitioners of TCM believe that the pathogenesis of PF is mainly Qi deficiency and blood stasis, so the therapy strategy is to tonify qi, activate blood, and remove stasis (Li and Kan, 2017). YHD consists of two botanical drugs: Astragalus mongholicus Bunge (AM) and Salvia miltiorrhiza Bunge (SM). Data mining analysis of TCM clinical prescriptions showed that AM and SM were the most frequently used herbs for tonifying Qi and activating Blood, respectively (Zhang et al., 2018). In our study, we confirmed the efficacy of YHD in an animal model and preliminarily explored the underlying mechanisms of this drug. The mechanism of YHD for PF is shown in Figure 9. Our study lays the foundation for developing novel drugs for PF while providing evidence support for the clinical promotion of YHD. The diagnosis of PF and assessment of drug efficacy are difficult in clinical practice (Richeldi et al., 2017). Therefore, we assessed the efficacy of YHD by a comprehensive evaluation of body weight, lung coefficients, lung function, and pathological lung structures. We used BLM for the PF model, which is currently the most widely used animal model (Martinez et al., 2017). Our study demonstrated that YHD treatment significantly improved BLM-induced weight loss and pathological lung changes in rats. Excessive collagen deposition and myofibroblast activation are features of PF, and the high expression of α-SMA is a marker of myofibroblast activation (Moss et al., 2022). Previous studies have shown that YHD can inhibit collagen deposition (He et al., 2012). Consistent with their results, we found that YHD notably decreased the content of collagen (type Ⅰ and type Ⅲ) and α-SMA in fibrotic lung tissues. To better meet clinical criteria, we examined fibrotic rats’ lung function. YHD could significantly improve high airway resistance in fibrotic rats. To summarize, these results demonstrated that YHD mitigated BLM-induced PF. Cellular senescence is thought to be a major driver of the malignant progression of PF (Schafer et al., 2017). The process of cellular senescence is caused by multiple physiological and pathological stresses that result in a permanent cell cycle arrest (Muñoz-Espín and Serrano, 2014). Cellular senescence consists of replicative and stress-related senescence (Hernandez-Segura et al., 2018). Replicative senescence is mainly due to the activation of p53 after DNA damage. Stress-related senescence is mainly due to oxidative stress damage, which activates the cyclin-dependent kinase inhibitor p16. Both pathways can activate the downstream cyclin-dependent kinase inhibitor p21 (Barnes et al., 2019). Activating p21 causes cells to exit the cell cycle, resulting in a permanent cell cycle arrest (Lv et al., 2021). In a recent study, researchers found that p53 and p21 were significantly highly expressed in the lung tissue of PF patients. Moreover, pifithrin-α, a specific p53 inhibitor, can reduce the senescence of type II alveolar cells, thereby alleviating experimental pulmonary fibrosis (Yao et al., 2021). We found that YHD inhibited cellular senescence markers, as evidenced by decreased gene and protein expression of p53, p21, and p16. Despite being in a state of cell cycle arrest, senescent lung epithelial cells are still metabolically active (Yao et al., 2021). These senescent cells can secrete various growth factors, cytokines, and proteases, known as SASPs. Unfortunately, the SASPs cause low-grade chronic inflammation, which further triggers senescence and massive accumulation of extracellular matrix (Hernandez-Segura et al., 2018). Our experiment dynamically detected the levels of SASPs (TGF-β1, TNF-α, and IL-6) in rat serum. ELISA results showed that the progression of PF was accompanied by continuous stimulation of these SASPs. High-dose YHD consistently suppressed the expression of these SASPs in serum during the progress of PF. These results suggest that YHD can inhibit cellular senescence and SASPs, which may be beneficial in explaining the anti-fibrotic mechanism of YHD. However, our study has some limitations. Because YHD intervention is difficult to perform on cell experiments, next, we will continue to screen active compounds of YHD and further explore their anti-fibrotic mechanisms.
In conclusion, our study suggests that YHD improves lung pathological injury and lung function in PF rats. This protective effect may be related to the ability of YHD to inhibit cellular senescence. |
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PMC9649476 | 36261775 | Feiyan Liu,Linda B. S. Aulin,Sebastiaan S. A. Kossen,Julius Cathalina,Marlotte Bremmer,Amanda C. Foks,Piet H. van der Graaf,Matthijs Moerland,Johan G. C. van Hasselt | A system pharmacology Boolean network model for the TLR4-mediated inflammatory response in early sepsis | 19-10-2022 | Boolean model,Sepsis,Toll-like receptor 4,Immune response,Inflammation,Treatment | Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis. Supplementary Information The online version contains supplementary material available at 10.1007/s10928-022-09828-6. | A system pharmacology Boolean network model for the TLR4-mediated inflammatory response in early sepsis
Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1β targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis.
The online version contains supplementary material available at 10.1007/s10928-022-09828-6.
Sepsis is a complex syndrome with high morbidity and mortality associated with multi-organ dysfunction driven by the host inflammatory response to an infection. The initial inflammatory response is mainly activated by pattern recognition receptors, where Toll-like receptor 4 (TLR4) activation is one of the key receptors associated with Gram-negative bacterial infections commonly producing sepsis [1, 2]. Organ dysfunction is a major cause of sepsis-associated mortality and morbidity, although the underlying mechanisms for these effects are only partly understood [3]. Besides treatment with antibiotics, very limited treatment options are currently available for sepsis. Considerable efforts in the past decades towards developing novel therapeutics against sepsis have failed during clinical trials [4, 5]. The complexity of underlying immune system interactions in sepsis in relation to harmful effects on organ systems may be an important reason for these failures, warranting more holistic approaches. A wealth of knowledge of isolated cellular and biochemical processes and their interactions associated with inflammation and sepsis is available in literature, but the utility of this is hampered by a lack of integration. To this end, the use of mechanistic mathematical modelling may help to integrate this knowledge in order to rationalize the design of treatment strategies, and the discovery of novel biomarkers that may be used to stratify patients and individualize therapies [6, 7]. Indeed, quantitative ordinary differential equation models have been used extensively in systems biology and systems pharmacology for this purpose. However, a requirement for constructing such models is the availability of kinetic parameters, which are lacking for various sepsis and inflammation associated interactions and processes. Boolean network (BN) models offer an attractive mathematical modelling strategy where inhibitory and stimulatory interactions that are commonly available in literature can be utilized, allowing a much more comprehensive integration of available biological knowledge. BN modelling approaches have been used previously to describe the behaviour of complex systems and to support identification of treatment targets [8, 9]. Briefly, a BN model consists of nodes and edges. Nodes can have an active or inactive state and typically represent biological components such as cells, mediatory molecules or genes [10]. Edges represent the interactions between the different nodes. The BN network is defined according to logic functions that determine the activation state of each node, which will also depend on the activation state of other nodes in the network. Within specific Boolean modelling tools, e.g. SPIDDOR [10], interactions between components can also be refined to cause specific activation, inhibition, and modulation of the nodes. Performing simulations with BNs can be used to identify stable states (known as attractors) of the system, which may be considered to correspond to phenotypes [11], thus providing insight into the probability of activation of endpoint nodes with clinical relevance. Comparing the attractors under different perturbations of nodes alone or in combination may be used to identify novel treatment strategies [12]. The aim of this study is to identify cellular or mediator-specific factors which modulate key clinically-relevant endpoints of sepsis, either as explanatory factors of inter-individual variation in treatment outcome, or, as target for potential mono- and combination treatment strategies. To this end, we developed a BN model for the TLR4-mediated host inflammatory response that plays an important role in the systemic inflammatory response in the early phase of sepsis.
An extensive literature search was performed in order to build the Boolean network model for TLR4-mediated sepsis. We collected experimental in vitro and in vivo data on activation or inhibitory events between key immune cells, intracellular signalling mediatory molecules such as inflammatory cytokines and membrane receptors, and sepsis pathogenesis endpoints including bacterial phagocytosis and thrombosis. The development of the initial version of the model was guided by several comprehensive reviews of the inflammatory response after TLR4 activation and sepsis, from where we systematically searched for each cell and/or mediator for all relevant additional interactions. The BN model was created by translating regulatory interactions between identified cells, receptors and molecules into Boolean functions: interactions of activation or inhibition between nodes were described as flexible combinations of Boolean operators AND, OR and NOT in a mathematical expression. The network was visualized using Cytoscape (v3.8.2) [13].
The Boolean network analysis was performed in R (v 4.1.2) using the package SPIDDOR (v 1.0) [10], which has multiple essential functionalities for capturing the behaviour of immune responses. One of these functionalities is the introduction of time delays in the network interactions. Such delays are incorporated using threshold arguments (THR) that represent lag times for the initiation of node activation or inhibition. In addition, SPIDDOR allows for modulating the intensity of the activations and inhibitions of the network, by adding a duration for these interactions to occur [10]. In that sense, a regulator node could activate or inhibit the regulated node for only some time steps in the simulation. To capture the stochasticity associated with biological systems, an asynchronous updating method was implemented for the simulations. This method assumes that only one node can be updated in a single time step and every node is equally likely to be updated [14]. In a BN simulation, each node is updated according to its Boolean function over the time steps, to either remain in, or switch to, one of the two possible states: 0 (inactive) or 1 (activated). The initial state of this BN is the onset of infection (i.e. Infection = 1, all other nodes = 0). The state sets of attractors for each simulated scenario, i.e. the percentage of activation (% activation) of each node in 100 repetitions, were used as readout.
Simulation scenarios were evaluated based on two types of endpoints: the ability of the immune system to fight the infection, through endpoint nodes Phagocytosis and membrane attack complex (MAC), and endpoints associated with organ damage, i.e. Thrombosis and angiopoietin-2 (Ang2). These aspects could be indirectly represented by nodes in the Boolean network. The extent of activation of these endpoint nodes was used to assess the effect of perturbations of the network.
We studied how variation in immune cell nodes activation can explain differences in activation of selected endpoint nodes to better understand potential causes for variation in clinical outcomes between patients. To this end, we performed a sensitivity analysis by specifying the % activation of each immune cell node in the network from 0 to 100% activation. The sensitivity analysis allowed us to investigate the impact of immune components on clinical endpoint node activation. These analyses were implemented using the polymorphism functionality in SPIDDOR, which modifies the fractional activation patterns of nodes. For instance, when a polymorphism of 50% activity is introduced in a node, this node is only activated 50% of the times in which its regulator nodes are activated [10], therefore, decreasing the normal activity of the node by 50%.
Mediatory molecules such as pro-inflammatory cytokines TNF-α and IL-1 are commonly investigated as therapeutic targets in drug development for sepsis [5]. For this analysis, we evaluated the potential of targeting each individual mediatory molecule that was included in the final network. Perturbations were performed via knocking-out or over-expressing a certain mediator node, either at infection onset or at a later stage of the infection until the activation of all nodes in attractors would not change over time steps. We then repeated this analysis where we modulate two nodes at the same time to study the effect of a combination treatment. Here, either we targeted to mediator nodes, or we combined modulation of a mediator with inhibition of the bacterial node to mimic antibiotic treatment. The resulting endpoints activations of attractors were compared to their activations without perturbation. An efficacy cut-off of 20% for the relative activation change between perturbed and non-perturbed scenarios was used to identify promising therapeutic targets.
A Boolean network (Fig. 1) associated with early phase TLR4-mediated sepsis was informed by data extracted from 108 publications (Table S1). The developed BN consisted of 42 nodes and 183 interactions. The underlying Boolean functions are defined further in Table 1 and Table S1. The developed network describes several different mechanisms underlying the disease progression of sepsis, including the regulation of immune cells, endothelial cells, complement and coagulation cascades, which contribute to bacterial clearance but may also lead to activation of harmful effects associated with organ damage. The network used modulations to account for changes in expression, auto-secretion and feedback relationships in a more refined manner (Fig. 1). Threshold parameters (see Methods) were applied to account and differentiate biological time delays for different events, including the clearance of bacteria (B_CL), early and late phagocytosis (Phag_E and Phag_L), cell apoptosis (Apop), production of tissue factor (T_TF), formation of membrane attack complex (T_MAC) and the release of anti-inflammatory cytokines (Anti_inflam). Thresholds were set to two time steps to represent the binding and functioning steps, while the threshold relating to early phagocytosis was set to one since it occurs earlier than the phagocytosis caused by other immune cells. The threshold of anti-inflammatory cytokines production, mainly IL-10, was set to three due to an additional required signal transduction for the cytokine synthesis [15].
We performed a sensitivity analysis to evaluated the impact of node activation alterations on innate and adaptive immune cell nodes as well as activated endothelial cells, by performing simulations where we decreased the activation of these nodes a 10% in each simulation and then compared the effect caused on the endpoints with the state of these endpoints on attractors with no alteration (100% activation). As a result, we identified three cell nodes whose activation situation had considerable effect on the selected endpoints: (1) activated endothelial cells (Act-EC) on angiopoietin-2 (Ang2), (2) activated monocytes (Act-Mon) on thrombosis (Thrombosis), and (3) activated platelets (Act-PLT) on thrombosis (Thrombosis) (Fig. 2). The activation level of endothelial cells was positively correlated with angiopoietin-2 activity, with higher activation of Act-EC as initial state leading to a higher activation of Ang2 on attractors. This finding is in line with previous studies, where activated endothelial cells have been shown to release more angiopoietin-2 into circulation during inflammation compared to non-inflammatory condition [16]. The risk of thrombosis, i.e., activation of the Thrombosis node in our network, was correlated with increasing monocyte and platelet activation. These two cell types play key roles in thrombosis as monocytes are the direct production source of tissue factor (TF) [17], while TF and platelet activation form the very foundation of thrombotic events. The increased activation of platelets could partly explain the increased risk of thrombosis and thromboembolism seen in the elderly patients [18].
We compared the relative change (Eq. 1) in % activation of endpoints between scenarios with different perturbation initiation times. In this analysis, we found that the relative changes were similar over perturbation initiation time in both singular and combination perturbation analysis. The result may indicate that variation in timing of the perturbation does not lead to relevant differences on the % activation on attractors of our selected endpoints (Fig. S1A–B). We identified a set of potential mono-therapeutic targets that were associated with a decreased activation of Ang2 and Thrombosis and/or to increase MAC (Fig. 3A). Two targets (sTNF-R and TNF-a) were identified for Ang2, six targets (IL-12, sTNF-R, IFN-gama, TNF-a and TF) were selected for Thrombosis, and two targets (C3b and C5b) were selected for MAC. No single perturbation displayed an impact on Phagocytosis based on our evaluation criteria. Furthermore, we found that either over-expressing tumour necrosis factor alpha (TNF-α) or blocking soluble TNF receptor (sTNF-R) could lead to a reduction of both of the organ dysfunction endpoints (Ang2 and Thrombosis). Blocking TLR4, TF or inflammatory cytokines interferon (IFN)-γ or interleukin (IL)-12 could reduce the risk of thrombosis but showed no beneficial effect on reducing angiopoietin-2. For the bacterial clearance related endpoint MAC, the over-expression of complement component C3b and C5b showed to increase its average long-term activation. This is in line with the well-established role of C5b as an essential composition of membrane attack complex (MAC) and that the cleavage of C5 to C5b requires C3b [19]. Although the interaction between the complement system and MAC is not an unexpected finding, it adds towards building confidence in the model predictions. We identified a total of six multi-target treatment strategies that showed potential benefit (Fig. 3B) in which one combination, by blocking IFN-γ and IL-10 together, could reduce both the risk of thrombosis and vessel leakage which is represented by activation of node Ang2 based on our network. Another combination shown to decrease the activation of Ang2 was blocking cytokines IL-10 and IL-12 together. Three of all the other four combinations to decrease the activation of Thrombosis included targeting TLR4, while the last one relied on the simultaneous blocking of IL-1β and IL-18. For therapy directed towards improving bacteria clearance by increasing MAC or Phagocytosis, no effective combinations were identified. When combining an immune targeting therapy with antibiotic treatment, where the antibiotic has a rapid and direct effect on bacterial clearance, the timing of initiation of treatment is of importance. The effect of clearing bacteria based on our selected endpoints differed over time and showed to be most beneficial during the early stage of infection (i.e. before 4 time steps, Fig. S2). This finding adds to the evidence of rapid initiation of antibiotic therapy improves outcomes in septic patients [20]. Although the use of antibiotics as mono-therapy showed a reduction of Thrombosis activation by more than 20%, our perturbation analysis suggests that there are still potential beneficial options of combining antibiotics with a Thrombosis focused therapy. Overall, we identified four therapeutic targets that could be beneficial to target in combination with antibiotic therapy to decrease the activation of Thrombosis (Fig. 3C), in which three of them were already identified in mono-therapy evaluation, i.e. IFN-γ, sTNF-R or TF, but blocking them could almost deactivate Thrombosis when combined with antibiotics. Another identified target, pro-inflammatory cytokine IL-1β, was not identified in mono-therapy but appeared in combined mediators specific therapies. For node Ang2, no combination showed a benefit to decrease its activation. Predictably, no increased immune regulated antibacterial effect could be identified due to the rapid bacterial eradication mediated by the antibiotics.
A novel Boolean network model was developed, which leveraged prior knowledge of immune response-related processes for the TLR4-mediated host response associated with early phase sepsis. The developed network incorporated key immune cells and mediatory molecules, as well as key clinical endpoint nodes to assess inter-individual variability and treatment interventions. By using a simulation approach, we identified several potential targets showing promise of improving bacterial clearance and/or reducing the possibility of organ dysfunction. The identified mediators might constitute potential therapeutic targets for treatment of sepsis and could be considered in further clinical studies. The long-term behaviour, i.e. attractors, of this developed network showed to be stable according to the overall single perturbation analysis, where either knocking-out or over-expression of most nodes did not trigger considerable changes on the activation of the rest nodes on attractor (Fig. S3). This stabilization could be a result of the complex interactions within the network, which might explain in part the failures of many clinical trials investing treatments against sepsis. Recently, selective or non-selective targeting of endogenous mediator molecules have been investigated as strategies to modify the systemic inflammatory response, such as blocking TNF-α and IL-1β [4, 5]. However, none of these agents showed significant improvement on septic survival rate. These results are comparable to our single node perturbations in which knocking-out TNF-α mainly lead to decreasing cell apoptosis while knocking-out IL-1β showed no big influence on other nodes. We utilized a Boolean network as a tool to screen promising treatment targets for sepsis based on endpoints related to bacteria clearance (Phagocytosis and MAC) and vessel leakage and multi-organs dysfunction (Ang2 and Thrombosis). When evaluating mono-target therapies, we found over-expressing TNF-α, instead of blocking it, was associated with a decreased activation of Ang2 and Thrombosis, which can be related to decreased organ dysfunctions. This finding is inconsistent with previous clinical studies where TNF-α was blocked but have not shown a significantly improved survival rate in sepsis patients [5]. Additionally, treatments blocking either TF or IFN-γ were identified to reduce Thrombosis in our analysis. These targets have also been studied in clinical trials, but so far no clinical effect has been identified [4]. One reason for these inconsistent results might be the differences in selected endpoints. Clinical trials for sepsis mainly use mortality as the primary endpoint, while we used four surrogate endpoints. Our simulations suggest a decrease in activation of Ang2 after over-expressing TNF-α. In contrast, a previous in vitro study suggested TNF-α can induce both angiopoietin-2 mRNA expression and protein levels in human umbilical vein endothelial cells [21] at 2 h after TNF-α exposure. Importantly, the positive interaction between angiopoietin-2 and TNF-α is in fact included in our model, with activated endothelial cells as intermediate node (Table 1). However, unlike the in vitro experiment involving a single cell type, our Boolean model also incorporates other relevant interaction events derived from other additional experiments, thereby illustrating the value of deriving expected outcomes which are the results of multiple cellular interaction events. The effect of TNF-α on thrombosis remains inconclusive. Previous studies suggested either an antithrombotic activity through the stimulation of nitric oxide [22] or a prothrombotic effect via acting on TNF-α receptor subtype 2 [23]. Recently, a in vivo study in mice showed a positive regulation of TNF-α/TNF receptor p55 singling axis in the resolution of venous thrombus [24]. In our simulations, long term over-expression of TNF-α was likely to decrease the activation of node Thrombosis, which might be a result of its beneficial role in thrombus resolution as indicated in the animal study. Worth noting are the inevitable inter-species differences when using animal models to mimic pathophysiological features in humans [25]. For multi-target treatment strategies, the combination of blocking IFN-γ and IL-10 was identified as a potential treatment to decrease the risk of organ dysfunction, via reducing activation of both Ang2 and Thrombosis. Cytokine IFN-γ functions as a positive modulator of activated platelets [26], which plays a crucial role in the process of thrombosis. Although IL-10 shows an inhibitory effect on the production of most pro-inflammatory cytokines, increased IL-10 blood levels has been associated with the development of organ failure in septic shock [27]. Nevertheless, since IFN-γ and IL-10 are negative modulators of each other, few studies have addressed the co-operative action of these combination, while Yoshiki et al. found simultaneous treatment with IL-10 and IFN-γ can significantly suppress the function of murine bone marrow-derived dendritic cells [28]. Due to the complexity of regulatory interaction between cytokines, the blockage of IFN-γ and IL-10 together could potentially reduce the risk of organ dysfunction. When treating with antibiotics in the very early phase of infection, all nodes in this network remained inactive or returned to baseline immediately (Fig. S2). This behaviour is in line with the clinical recommendation of administering antibiotics as early as possible for adults with possible septic shock or a high likelihood for sepsis [2]. A delayed start of antibiotic therapy, simulated by removing bacteria after 4 time steps, showed to be ineffective in inhibiting the initiation of the immune cascade reaction, which can be seen from the unchanged activation on attractors (Fig. S4). This phenomenon may explain the failures of clinical trial focusing on anti-endotoxin agents [5], where neither human antiserum to endotoxin nor monoclonal IgM antibodies that inactivates endotoxin could significantly improve survival in sepsis. Local thrombosis contributes to the initial defence against bacterial invasion in mammals [29]. We find that combination therapies with delayed initiation of antibiotic therapy, such as antibiotic treatment combined with IL-1β blockade, may show beneficial effects, decreasing Thrombosis node activation. These results are in line with a previous study where an increase in IL-1β mRNA expression in patients who suffered thrombotic episodes compared with healthy age-matched controls [30] was observed. Another clinical study showed the anti-inflammatory therapy targeting IL-1β pathway led to a significantly lower rate of recurrent cardiovascular events than placebo [31]. These data indicate that IL-1β might be a relevant therapeutic target, although treatment of inhibiting IL-1β alone did not show sufficient decrease of Thrombosis activation in our analysis. Interleukins have been of recent interest as potential treatment in sepsis due to their contribution to thrombosis and their potential therapeutic effect in animal models [32], including pro-inflammatory IL-6 [33] and anti-inflammatory IL-10 [34]. However, a population-based study suggested that an altered inflammatory profile of these interleukins is more likely to be associated with a result rather than an increased risk of venous thrombosis [35]. IL-12 was another identified target in our simulations. However, a previous study concluded that IL-12 can activate both coagulation and fibrinolysis in patients with renal cell carcinoma [36]. The potential of these inflammatory targets thus still need to be evaluated in well-controlled clinical studies. Antibiotic treatment was mimicked by setting the node Bacteria to 0% activation in our simulation. The dynamic pattern over time steps of other nodes varies after deactivating Bacteria (Fig. S2), in which the simulated activation of complement factors, i.e., C3, C5a and C5b, as well the complex MAC returned to baseline immediately. This consistency indicates the potential of complement factors as biomarkers for monitoring antibiotic treatment efficacy in early sepsis. Indeed, a recent prospective study evaluated complement levels in bacteremia patients, and hypothesized the measurement of C3, C4 and C9 levels may help stratify Gram-negative bacteremia patients at increased risk for mortality [37]. Activation of complement system is a key event in the pathogenesis of sepsis [38], adapting crucial complement factors as biomarkers might be of prognostic value, when their sensitivity and specificity were carefully evaluated. Although the use of a Boolean network approach can support developing understanding the behaviour of complex systems, especially in the lack of quantitative data, the approach is associated with inherent limitations. The time steps in a Boolean network are not related to real time. Thus, simulation results cannot be directly linked to time-concentration data, such as specific biomarker peak times, which further complicates model validation using clinical data. The attractors of mono perturbations on our BN were compared with previous experimental results under certain intervention, revealing some similarities between our simulations and in vivo animal studies. However, human studies with comparable endpoints are still required to validate both of the identified mono and multi therapeutic targets. The development of the Boolean network model in this study was guided by including key biological processes previously identified as key consensus mechanisms associated with TLR4-activation and early sepsis. We systematically searched the literature to identify interaction partners between involved cell types, receptors and their ligands to populate a complete network. Nonetheless, the developed Boolean network model may need further revision and additions depending on new findings and specific objectives for applying this model. With respect to (clinical) endpoint nodes we have selected biological events which may closely relate to key clinical events in the disease pathology of sepsis. Yet, it is important to recognize this model does not directly predicts clinical outcomes, which also complicates the comparison of our results to existing clinical trials. These two shortcomings could be overcome by gradually extending this network with a higher number and clinically related nodes. In conclusion, the developed Boolean network model for TLR4-mediated host immune response in early phase of sepsis exemplifies the value of using Boolean networks to increase the knowledge of complex biological systems, and constitutes a relevant strategy to deepen our understanding of systemic inflammatory diseases, analyse the influences of immune cells diversity among patient groups, and identify potential therapeutic targets for sepsis.
Below is the link to the electronic supplementary material. Supplementary material 1 (DOCX 183.5 kb)Supplementary material 2 (TIF 2038.8 kb)Fig. S1 Relative changes of four selected endpoints activation under mono and combined perturbations on mediatory molecules. Upper heatmap (A) showed the effect of knocking-out or over-expressing of identified mono therapeutic targets on four endpoints over different perturbation initiation time steps; below heatmaps (B) showed the example of effect of knocking-out or over-expressing combined therapeutic targets on four endpoints when initiating perturbations at time step 20. Colors of the heatmap represented the negative, neutral and positive relative changes of endpoints activation with blue, white and orange, respectivelySupplementary material 3 (TIFF 2204.7 kb)Fig. S2 Average activation profiles for each node under antibiotic treatment (i.e. knocking out node Bacteria) at different time step with 100 repetitions. When removing bacteria at an early phase (before time step 4), most nodes were not activated or returned back to baseline immediately; when removing bacteria at a later phase, it showed varying decline patterns for different nodes. Colors of the lines represented different perturbation initiation time stepsSupplementary material 4 (TIF 928.5 kb)Fig. S3 Overview of single node perturbation analysis of the network. The heatmaps indicated the effect of entire knock-out (A) or over-expression (B) of each node (columns) in every network node (rows). Colors of the heatmap represented the Perturbation Index (PI) with the negative (PI < 0.8), neutral (0.8 < PI < 1.25) and positive (PI > 1.25) changes being blue, white and orange, respectivelySupplementary material 5 (TIFF 756.0 kb)Fig. S4 Activation of each node on attractors under antibiotic treatment (i.e. knocking out node Bacteria) at different time steps. The activations on attractors stayed unchanged when removing bacteria at a later phase (i.e. after time step 4) |
PMC9649484 | Stephan M. Schulreich,David A. Salamanca-Díaz,Elisabeth Zieger,Andrew D. Calcino,Andreas Wanninger | A mosaic of conserved and novel modes of gene expression and morphogenesis in mesoderm and muscle formation of a larval bivalve | 07-07-2022 | Evodevo,Development,Evolution,Mollusca,Novelty,Myogenesis | The mesoderm gives rise to several key morphological features of bilaterian animals including endoskeletal elements and the musculature. A number of regulatory genes involved in mesoderm and/or muscle formation (e.g., Brachyury (Bra), even-skipped (eve), Mox, myosin II heavy chain (mhc)) have been identified chiefly from chordates and the ecdysozoans Drosophila and Caenorhabditis elegans, but data for non-model protostomes, especially those belonging to the ecdysozoan sister clade, Lophotrochozoa (e.g., flatworms, annelids, mollusks), are only beginning to emerge. Within the lophotrochozoans, Mollusca constitutes the most speciose and diverse phylum. Interestingly, however, information on the morphological and molecular underpinnings of key ontogenetic processes such as mesoderm formation and myogenesis remains scarce even for prominent molluscan sublineages such as the bivalves. Here, we investigated myogenesis and developmental expression of Bra, eve, Mox, and mhc in the quagga mussel Dreissena rostriformis, an invasive freshwater bivalve and an emerging model in invertebrate evodevo. We found that all four genes are expressed during mesoderm formation, but some show additional, individual sites of expression during ontogeny. While Mox and mhc are involved in early myogenesis, eve is also expressed in the embryonic shell field and Bra is additionally present in the foregut. Comparative analysis suggests that Mox has an ancestral role in mesoderm and possibly muscle formation in bilaterians, while Bra and eve are conserved regulators of mesoderm development of nephrozoans (protostomes and deuterostomes). The fully developed Dreissena veliger larva shows a highly complex muscular architecture, supporting a muscular ground pattern of autobranch bivalve larvae that includes at least a velum muscle ring, three or four pairs of velum retractors, one or two pairs of larval retractors, two pairs of foot retractors, a pedal plexus, possibly two pairs of mantle retractors, and the muscles of the pallial line, as well as an anterior and a posterior adductor. As is typical for their molluscan kin, remodelling and loss of prominent larval features such as the velum musculature and various retractor systems appear to be also common in bivalves. Supplementary information The online version contains supplementary material available at 10.1007/s13127-022-00569-5. | A mosaic of conserved and novel modes of gene expression and morphogenesis in mesoderm and muscle formation of a larval bivalve
The mesoderm gives rise to several key morphological features of bilaterian animals including endoskeletal elements and the musculature. A number of regulatory genes involved in mesoderm and/or muscle formation (e.g., Brachyury (Bra), even-skipped (eve), Mox, myosin II heavy chain (mhc)) have been identified chiefly from chordates and the ecdysozoans Drosophila and Caenorhabditis elegans, but data for non-model protostomes, especially those belonging to the ecdysozoan sister clade, Lophotrochozoa (e.g., flatworms, annelids, mollusks), are only beginning to emerge. Within the lophotrochozoans, Mollusca constitutes the most speciose and diverse phylum. Interestingly, however, information on the morphological and molecular underpinnings of key ontogenetic processes such as mesoderm formation and myogenesis remains scarce even for prominent molluscan sublineages such as the bivalves. Here, we investigated myogenesis and developmental expression of Bra, eve, Mox, and mhc in the quagga mussel Dreissena rostriformis, an invasive freshwater bivalve and an emerging model in invertebrate evodevo. We found that all four genes are expressed during mesoderm formation, but some show additional, individual sites of expression during ontogeny. While Mox and mhc are involved in early myogenesis, eve is also expressed in the embryonic shell field and Bra is additionally present in the foregut. Comparative analysis suggests that Mox has an ancestral role in mesoderm and possibly muscle formation in bilaterians, while Bra and eve are conserved regulators of mesoderm development of nephrozoans (protostomes and deuterostomes). The fully developed Dreissena veliger larva shows a highly complex muscular architecture, supporting a muscular ground pattern of autobranch bivalve larvae that includes at least a velum muscle ring, three or four pairs of velum retractors, one or two pairs of larval retractors, two pairs of foot retractors, a pedal plexus, possibly two pairs of mantle retractors, and the muscles of the pallial line, as well as an anterior and a posterior adductor. As is typical for their molluscan kin, remodelling and loss of prominent larval features such as the velum musculature and various retractor systems appear to be also common in bivalves.
The online version contains supplementary material available at 10.1007/s13127-022-00569-5.
Bilaterian animals have three germ layers, the ectoderm, the endoderm, and the mesoderm. The mesoderm originates during gastrulation and forms a variety of derivatives, including connective tissue and the musculature. Gene expression during mesoderm formation and/or myogenesis has been studied in most bilaterians such as acoelomorphs, deuterostomes such as chordates, hemichordates, and echinoderms, as well as in ecdysozoan and lophotrochozoan protostomes (e.g., brachiopods, ectoprocts, phoronids, and annelids; Candia & Wright, 1995; Furlong et al., 2001; Minguillón & Garcia-Fernàndez, 2002; Pocock et al., 2004; Lowe et al., 2006; Andrikou et al., 2013; Chiodin et al., 2013; Andrikou & Arnone, 2015; Passamaneck et al., 2015; Erkenbrack, 2016; Kozin et al., 2016; Martín-Durán et al., 2017; Vellutini et al., 2017; Andrikou & Hejnol, 2021). Nevertheless, a large gap of knowledge exists for one of the most morphologically diverse lophotrochozoan phyla, Mollusca, for which only few species have been investigated in some detail (e.g., the gastropod Crepidula fornicata and the bivalve Saccostrea kegaki; Kakoi et al., 2008; Perry et al., 2015). Developmental genes with a widely conserved expression during bilaterian mesoderm formation are manifold and include Brachyury (Bra), caudal (cdx), dachshund (dachs), even-skipped (eve), eyes absent (eya), forkhead A (foxA), forkhead C (foxC), forkhead D (foxD), forkhead F (foxF), gata4/5/6, myocyte enhancer factor-2 (mef2), Mox, myosin II heavy chain (mhc), myoblast determination protein 1 (myoD), neurokinin 1 (nk1), paraxis, sine oculis (six1/2), snail, tropomyosin (tm), twist (twi), and vasa (vas) (Andrikou & Hejnol, 2021; Martín-Durán et al., 2017; Passamaneck et al., 2015; Sebé-Pedrós & Ruiz-Trillo, 2017; Zhang & Bernstein, 2001). The homeobox gene Mox (a homolog of Meox, Gax, and buttonless) appears to have an additional role in myogenesis in some lophotrochozoans and chordates (Kozin et al., 2016; Passamaneck et al., 2015; Satou & Imai, 2015). Eve (a homolog of Evx, Xhox3, and vab-7) is closely related to Mox and acts as a pair-rule gene during arthropod segmentation (Copf et al., 2003; Damen et al., 2000; Janssen et al., 2011; Patel et al., 1994). It is also involved in mesoderm development and/or myogenesis in cephalochordates, vertebrates, and ecdysozoans, as well as in vertebrate limb formation (Ruiz et al., 1989; Patel et al., 1992; Ahringer, 1996; Hérault et al., 1996; Sordino et al., 1996; Ferrier et al., 2001; Fujioka et al., 2005). Bra is expressed in the mesoderm of a number of protostomes and deuterostomes including annelids, brachiopods, priapulids, and arthropods (Kozin et al., 2016; Kusch & Reuter, 1999; Martín-Durán et al., 2017; Peter & Davidson, 2011; Peterson et al., 1999; Sebé-Pedrós & Ruiz-Trillo, 2017). For mollusks, no mesodermal expression of Bra was found in the gastropod Haliotis asinina, whereas in another marine snail, Patella vulgata, Bra is transiently expressed in the 4d cell that gives rise to the future endomesoderm (Koop et al., 2007; Lartillot et al., 2002). In the gastropod Crepidula fornicata, Bra is involved in mesoderm formation, while in the bivalves Crassostrea gigas and Saccostrea kegaki, the data are somewhat inconclusive as to whether or not Bra is expressed during mesoderm formation (Kin et al., 2009; Perry et al., 2015; Tan et al., 2017). During metazoan myogenesis, a number of genes and their respective proteins are commonly expressed, including those of the myosin family (Burgess, 2005; Thompson & Langford, 2002). Of these, myosin II heavy chain (mhc) appears to have a particularly conserved role in muscle formation and is consistently expressed from the earliest stages of myogenesis onwards in a number of phyla (Kobayashi et al., 1998; Zhang & Bernstein, 2001; Renfer et al., 2010; Andrikou et al., 2013). Although larval myoanatomy has been described in several invertebrate taxa including mollusks, very few details are available on the ontogenetic sequence that gives rise to the highly intricate musculature of larval and adult bivalves, the second largest class-level molluscan taxon after the gastropods (Audino et al., 2015; Li et al., 2019; Sun et al., 2019; Wurzinger-Mayer et al., 2014). These studies showed that bivalve larvae typically exhibit a velum muscle ring as well as various retractor systems that degenerate prior to or at metamorphosis. The muscles of the pallial line, the mantle retractors, the adductor system, as well as the foot retractors together with the plexus-like foot musculature, are common features of adult bivalves that develop in the larva and are retained after metamorphosis (Audino et al., 2015; Cragg, 2016; Li et al., 2019; Sun et al., 2020; Wurzinger-Mayer et al., 2014). The invasive quagga mussel Dreissena rostriformis (Deshayes, 1838) shows an indirect lifecycle with a trochophore and a subsequent veliger larva, and is an emerging model system in evolutionary developmental biology (Calcino et al., 2019; Salamanca-Díaz et al., 2021). In order to assess whether common regulators of bilaterian mesoderm and muscle formation are also involved in bivalve ontogeny, we investigated the expression of Brachyury, even-skipped, Mox, and myosin II heavy chain during D. rostriformis development. In addition, we provide a detailed account of myogenesis in this model bivalve in order to contribute to the reconstruction of the myoanatomical ground pattern of bivalve larvae.
Adult quagga mussels were collected in the Danube River in Vienna, Austria (Georg-Danzer-Steg, 48°14ʹ45.7ʺN 16°23ʹ38.4ʺE), in May 2018. Mussels were kept in a 45 L aquarium in an incubator at 18 °C in Danube water with a weekly water change. Prior to spawning, adult mussels were cleaned with a brush under running tap water. The specimens were washed in a 100 mL beaker with 2 µm filtered Danube water (FDW) containing 0.1% sodium hypochlorite (#09,951,780, DanKlorix, Hamburg, Germany) for 5 min. To induce spawning, the mussels were placed in a fresh 100 mL beaker with FDW containing 10−3 M serotonin hydrochloride (#H9523, Sigma-Aldrich, St. Louis, MO, USA) for 20 min at room temperature (RT). After gamete release, the eggs of each female were mixed with two to three drops of concentrated sperm and transferred to a 200 mL container with fresh FDW and incubated for 15 min. This was followed by three washes in FDW to remove excess sperm. When the animals had reached the trochophore stage, the larvae of each female were transferred to a fresh container with 2 L FDW with aeration and a magnetic stirrer and were kept at 18 °C. The FDW was exchanged every 2 days, and when the veliger stage was reached, the larvae were additionally fed one to two drops of an Isochrysis concentrate after the water change (Plankton-Welt, Hamburg, Germany). Prior to fixation, crystalline cocaine was added to veliger larvae at a final concentration of 30 µg/mL (#609,020,011, Gatt-Koller, Absam, Austria) to avoid retraction into the shell. Developmental stages (gastrula, trochophore larva, early D-shaped veliger larva, late veliger larva) were fixed in 4% ice-cold paraformaldehyde (PFA) (#158,127, Sigma-Aldrich) in 0.1 M phosphate buffer saline (PBS) for 1 h. For in situ hybridization, samples were washed 2 × 10 min in 100% methanol and stored at − 20 °C. For immunofluorescence and actin staining, larvae were washed 3 × 10 min in PBS containing 0.1% NaN3 (#71,289, Sigma-Aldrich) and stored at 4 °C.
Dreissena rostriformis samples were washed 3 × 10 min in PBS, followed by decalcification for 1 h in 50 mM EGTA (#E3889, Sigma-Aldrich) in PBT (1 × PBS, 0.1% Tween 20; #9127.1, Carl Roth, Karlsruhe, Germany) and 2 × 10 min washes in PBT at RT. Unspecific binding sites were blocked for 1 h in PBT with 3% normal swine serum (#014–000-121, Jackson ImmunoResearch, West Grove, PA, USA). Subsequently, samples were incubated in the primary antibodies (dilution 1:900, anti-acetylated α-tubulin, #T6793, Sigma-Aldrich) in the block solution overnight at RT. All specimens were washed 5 × 15 min in PBT and incubated in secondary antibodies (dilution 1:900, goat anti-mouse, Alexa Fluor 633, #A21050, Invitrogen, Carlsbad, CA, USA) with DAPI (dilution 1:400, 4ʹ,6-diamidino-2-phenylindole, #D1306, Invitrogen) added to visualize cell nuclei and Alexa Fluor 488 phalloidin (dilution 1:40, #A12379, Invitrogen) for actin labelling in PBT for 24 h at 4 °C in the dark. All samples were washed 5 × 15 min in PBT, followed by two washing steps in PBS for 10 min each. Stained specimens were mounted on glass slides with Fluoromount-G (#0100–01, SouthernBiotech, Birmingham, AL, USA). The samples were stored at 4 °C in the dark for a few days prior to the analyses. Samples were analysed with a Leica SP5 II confocal laser scanning microscope with the software LAS AF (v. 2.6.3.8173) (both Leica Microsystems, Wetzlar, Germany). ImageJ2 (Rasband, W.S., ImageJ, US National Institutes of Health, Bethesda, MD, USA, https://imagej.nih.gov/ij/, 1997–2018) and Imaris × 64 (v. 7.3.1) (Bitplane, Zurich, Switzerland) were used to analyse the image stacks, and Inkscape (v. 0.92.4; https://inkscape.org/) was used to create the schematic drawings.
Most candidate orthologs of the genes of interest (myosin II heavy chain, Mox, even-skipped, and Brachyury) and corresponding outgroups were retrieved from the NCBI nr database (https://www.ncbi.nlm.nih.gov) and confirmed with reciprocal blast searches (Supplemental Tables 1, 2, and 3). Dreissena rostriformis sequences were subsequently obtained by BLASTp (v. 2.8.1 +) against the translated transcriptome using these candidate sequences as queries (Calcino et al., 2019). Additionally, a few orthologs were downloaded from the Ensembl Metazoa database (Supplemental Tables 1 and 3). Orthologs containing either the myosin head domain or the T-box domain from the bivalve Crassostrea gigas and the cnidarian Nematostella vectensis were obtained by using hmmscan (Eddy, 1995) with the corresponding PFam (v. 32.0) hmm files (PF00063.21, PF00907.22) against the respective Ensembl genomes (Howe et al., 2020; Hinxton, UK, https://metazoa.ensembl.org/index.html). All orthologs of the genes of interest of Acanthochitona fascicularis were identified by blast hits against the transcriptome (De Oliveira et al., 2016; here assigned to A. crinita) (https://zoology.univie.ac.at/research/open-data/) using hmmscan (Eddy, 1995). For the myosin II heavy chain phylogeny, myosin families that are commonly known from metazoans were included (Thompson & Langford, 2002). All selected myosin families contain a myosin head domain and because myosin I is considered to be the earliest branching family, it was used as the outgroup for the phylogeny (Foth et al., 2006). For the even-skipped and Mox phylogenies, several Hox gene families were used as outgroup (Minguillón & Garcia-Fernàndez, 2003; Ryan et al., 2007). For the Brachyury phylogeny, all metazoan-specific T-box families were included. Brachyury is an early branching family of T-box proteins and so was set as an outgroup to the remaining T-box families (Sebé-Pedrós & Ruiz-Trillo, 2017; Sebé-Pedrós et al., 2013). Multiple sequence alignments were performed using MAFFT (v. 7.427) (Katoh et al., 2002), trimming was performed with BMGE (v. 1.12) (Criscuolo & Gribaldo, 2010), visualisation was performed with AliView (v. 1.0.0.0) (Larsson, 2014), and editing was performed with Jalview (v. 2.11.0) (Waterhouse et al., 2009). Appropriate amino acid substitution models were determined using ProtTest (v. 2.1) (Abascal et al., 2005). These were LG (Le & Gascuel, 2008) for Brachyury and myosin II heavy chain and JTT (Jones et al., 1992) for even-skipped and Mox. The phylogenetic (maximum likelihood) trees were computed using PHYML (v. 3.1) (Guindon & Gascuel, 2003) with a bootstrap value of 100. Visualisation of phylogenetic trees was performed with FigTree (v. 1.4.4) (http://tree.bio.ed.ac.uk/software/figtree/). For in situ hybridization probe production, specific primers for each gene under investigation were designed manually (Supplemental Table 4) and synthesised by Microsynth Austria GmbH (Vienna, Austria). Reading frames and orientation of the transcriptomic templates were verified with the ExPASy translate tool (Artimo et al., 2012; https://web.expasy.org/translate/) and melting temperatures of the designed primers were checked with the Promega Oligo Calculator tool (Rychlik & Rhoads, 1989; https://at.promega.com/resources/tools/biomath/tm-calculator/; 500 nM primer concentration, 5 × green or colourless Go Taq Reaction Buffer). For the self-complementary check, the Northwestern biotool OligoCalc tool (Kibbe, 2007; http://biotools.nubic.northwestern.edu/OligoCalc.html) was used. The primers were diluted to yield a working concentration of 10 µM and stored at − 20 °C. The nucleotide sequences and insert length of each primer pair are listed in Supplemental Table 4. Relative gene expression values (tpm values) were retrieved for all four genes of interest using stage-specific transcriptomes of D. rostriformis (Calcino et al., 2019).
Different developmental stages (1, 2, 4, 6, 8, 12, 15, 18, 21, 24, 28, 48, 70 h post fertilisation; hpf) were transferred to RNAlater (#76,106, Qiagen, Venlo, Netherlands) and stored at 4 °C. For total RNA extraction from pooled stages, the RNeasy Mini Kit (#74,104, Qiagen) with the QIAshredder homogeniser (#79,654, Qiagen) was used according to the manufacturer’s instructions. RNA samples were diluted 1:10 with DEPC (diethylpyrocarbonate)–treated water, quantified by a spectrophotometer (Nanodrop 2000c, Thermo Fisher Scientific), and stored at −80 °C. For cDNA synthesis, total RNA was denatured for 15 min at 65 °C and placed on ice. Subsequently, the 1st Strand cDNA Synthesis Kit for RT-PCR (#11 483 188 001, Roche, Basel, Switzerland) was used with Oligo-p(dt)15 primers. The obtained cDNA was diluted 1:5 with DEPC-treated water and stored at −20 °C. PCRs (cDNA-, plasmid-, colony-PCR) were performed using Go Taq Flexi DNA Polymerase (0.025 U/µl, #M780B, Promega, Madison, WI, USA), 1 × Go Taq Flexi Buffer (#M890A, Promega), PCR nucleotide mix (0.8 mM, #C1145, Promega), 1.25 mM MgCl2 (#A351H, Promega), and nuclease-free water (#R0581, Thermo Fisher Scientific, Waltham, MA, USA). To amplify Brachyury, Mox, even-skipped, and myosin II heavy chain, gene-specific primers (Supplemental Table 4) and cDNA were added to the PCR mixture. The PCR products were checked on a 1% agarose gel (#2267.4, Carl Roth) in TAE buffer (#CL86.1, Carl Roth). Bands corresponding to the expected nucleotide sequence length were excised and the DNA was extracted using the QIAquick Gel Extraction Kit (#28,706, Qiagen). The extracted DNA (insert) was stored at −20 °C. Ligation of the insert into a plasmid and transformation of the plasmid into E. coli JM109 Competent Cells were done using the pGEM-T Easy Vector System II (#A1380, Promega) according to the manufacturer’s instructions. White-blue screening of transformed bacteria was performed on LB agar plates (35 mg/mL, #965.1, Carl Roth) with 0.1% ampicillin (#A9518, Sigma-Aldrich). Successful transformation of the desired insert was confirmed by colony PCR, using M13 primers (10 µM, Microsynth, Balgach, Switzerland). Transformed bacteria were grown in 5 mL LB medium (#X964.1, Carl Roth) containing ampicillin (100 µg/mL, #A9518, Sigma-Aldrich) overnight at 37 °C with agitation (180 RPM). Plasmids were purified using the QIAprep Spin Miniprep Kit (#27,106, Qiagen), quantified (Nanodrop 2000c, Thermo Fisher Scientific), and sequenced (Microsynth, Vienna, Austria). Inserts corresponding to genes of interest were amplified through plasmid PCR using M13 primers. PCR products were checked by gel electrophoresis and stored at 4 °C. For the synthesis of sense and anti-sense riboprobes, PCR products (100–200 ng) were incubated with RNase-free water (#R0581, Thermo Fisher Scientific), 1 × transcription buffer (#11,465,384,001, Roche), 10 µM dithiothreitol (DTT, #D9779, Sigma-Aldrich), 1 × DIG RNA Labelling Mix (#11,277,073,910, Roche), 0.1 U Protector RNase Inhibitor (#03,335,402,001, Roche), 50 U SP6 RNA polymerase (#10,810,274,001, Roche), or 50 U T7 RNA polymerase (#10,881,767,001, Roche) in a thermocycler (37 °C, lid 60 °C) for 2 h. Afterwards, 1 µL DNase I (recombinant, RNase-free, #04,716,728,001, Roche) was added and samples were incubated for another 15 min at 37 °C to remove template DNA. DIG-Probes were purified via ProbeQuant™ G-50 Micro Columns (#GE28-9034–08, GE Healthcare, Chicago, IL, USA). Riboprobes were precipitated by adding 5 µL 4 M LiCl (#L7026, Sigma-Aldrich) and 120 µL 100% EtOH (#20,821, VWR Chemicals, Radnor, PA, USA) and by incubating overnight at −20 °C. Next, riboprobes were centrifuged at 14,000 RPM for 15 min at 4 °C and the obtained pellets were washed twice with 70% EtOH. Pellets were dried for 15 min at RT and dissolved in 20 µL RNase-free water (#R0581, Thermo Fisher Scientific). All RNA probes were quantified by a spectrophotometer, checked by gel electrophoresis, and stored at −80 °C.
Prior to WMISH, the developmental transcript abundances of each target gene were checked using quantitative gene expression data (Supplemental Table 5) (Calcino et al., 2019). This was done in order to assess the relative expression levels of putative genes of interest and helped in choosing promising candidate genes as well as key developmental stages for in situ hybridization experiments. Full-length sequences of the riboprobes used for WMISH experiments are provided in Supplemental Table 6. Dreissena rostriformis samples were rehydrated stepwise from 100% methanol to 0.1 M PBS (#1058.1, Carl Roth). All samples were decalcified for 1 h in PPE (4% PFA (#158,127, Sigma-Aldrich), 0.1 M PBS, 50 mM EGTA pH 8 (#E3889, Sigma-Aldrich)) and washed 3 × 5 min in PBT. Subsequently, the larvae were incubated in 30 µg/mL proteinase-K (#03,115,879,001, Roche) in PBS for 10 min at 37 °C. Specimens were washed 3 × 5 min in PBT, post-fixed in 4% PFA in PBS for 45 min, and washed again 3 × 5 min in PBT. Subsequently, the larvae were stepped into 100% hybridization buffer (50% formamide (#47,671, Sigma-Aldrich), 5 × SSC (#10,541, Carl Roth), 50–100 µg/mL heparin (#H3149, Sigma-Aldrich), 5 mM EDTA pH 8 (#20–158, Sigma-Aldrich), 1 × Denhardt’s (#D2532, Sigma-Aldrich), 100 µg/mL yeast tRNA (#R6750, Sigma-Aldrich), 0.1% Tween 20 (#9127.1, Carl Roth), 5% dextransulfat (#D8906, Sigma-Aldrich)). Pre-hybridization was carried out overnight at a gene-specific temperature (58.5 °C for myosin II heavy chain and 55 °C for Mox, even-skipped, and Brachyury). Each sense probe (negative control) and anti-sense probe was diluted at a concentration of 2 ng/µL in hybridization buffer and denatured for 10 min at 85 °C. After adding a riboprobe to the specimens, they were allowed to hybridize for 48–60 h at the abovementioned gene-specific temperatures. The samples were washed 3 × 20 min in 4 × wash (50% formamide (#47,671, Sigma-Aldrich), 4 × SSC (#10,541, Carl Roth), 0.1% Tween 20 (#9127.1, Carl Roth)), followed by 2 × 20 min washes in 2 × wash (with 2 × instead of 4 × SSC) and another 2 × 15 min washes in 1 × wash (with 1 × SSC). Specimens were allowed to cool down to RT and washed 3 × 15 min in 1 × SSC (#10,541, Carl Roth) containing 0.1% Tween 20 (#9127.1, Carl Roth). Subsequently, all samples were stepped into 0.1 M MAB (100 mM maleic acid (#K304.1, Carl Roth), 150 mM NaCl (#6781.3, Carl Roth), 0.1% Tween 20 (#9127.1, Carl Roth)). Specimens were blocked for 3 h in blocking solution (2% blocking reagent (#11,096,176,001, Roche), 0.1 M MAB) at RT, followed by incubation in Anti-Digoxigenin-AP Fab fragments (#11,093,274,910, Roche) diluted 1:5000 in blocking solution overnight at 4 °C. Next, the samples were washed 3 × 20 min and 3 × 10 min in PBT. Prior to staining, the larvae were washed 2 × 5 min in AP buffer (1 × alkaline phosphatase, 1 M NaCl (#6781.3, Carl Roth), 200 mM Tris pH 9 (#4855.1, Carl Roth), 0.1% Tween 20 (#9127.1, Carl Roth)). For highly expressed genes (e.g., myosin II heavy chain), specimens were stained in colour reaction buffer (1 × AP buffer, 5 µL/mL NBT (nitroblue tetrazolium chloride, #11,383,213,001, Roche), 3.75 µL/mL BCIP (5-bromo-cloro-3-indolyl-phosphate, 4-toluidine salt (#11,383,221,001, Roche)) at 37 °C for 2–3 h. For lowly expressed genes, 7.5% polyvinyl alcohol (PVA) (#P1763, Sigma-Aldrich) was added to the colour reaction buffer and specimens were incubated at 37 °C for 4–13.5 h. In order to stop the reaction, the larvae were washed 2 × 5 min in PBT and post-fixed in 4% PFA for 1 h at 4 °C. Subsequently, the specimens were washed 3 × 5 min in PBT and 3 × 10 min in PBS at RT. All washing steps were done on a shaker at 130 RPM. Stained larvae were stored at 4 °C and the PBS was changed once a week. For the subsequent analyses, the samples were mounted on glass slides in 100% glycerol (#G5516, Sigma-Aldrich) and imaged using an Olympus BX53 light microscope equipped with an Olympus DP73 camera and the software cellSens Standard (v. 1.11) (Olympus Corporation, Shinjuku, Tokyo, Japan). Schematic drawings were created with Inkscape (v. 0.92.4).
All annotated genes of interest are summarized in Supplemental Table 7. For the myosin II heavy chain family, four candidates (Dro-mhc_c1, c2, c3, c4) were found in Dreissena rostriformis, which contain a specific glycine insertion (G) (Richards & Cavalier-Smith, 2005) at position 534 (Supplemental Fig. 1). The first three candidates (Dro-mhc_c1-c3) include a complete myosin N, myosin head, and myosin tail 1 domain. The fourth candidate (Dro-mhc_c4) contains a fragmented myosin head domain (Supplemental Table 7). Nine further candidates (two copies of myosin I, myosin III, myosin V, myosin VI, myosin VII, myosin IX, myosin XV, and myosin XVIII) with partially fragmented myosin head domains were found in D. rostriformis and nest within the corresponding myosin family (Supplemental Fig. 1a and Supplemental Table 7). A phylogenetic tree was constructed for the homeobox domain–containing even-skipped and Mox genes (Supplemental Fig. 2). A single D. rostriformis even-skipped (Dro-eve) ortholog was identified, which includes a proposed characteristic tyrosine (Y) at position 48 at the beginning of the homeobox domain (Supplemental Fig. 2a, b). Two D. rostriformis Mox (Dro-Mox_c1 and Dro-Mox_c2) orthologs were found, which contain a putatively specific glutamic acid (E) insertion at position 48 at the beginning of the homeobox domain (Supplemental Fig. 2a, c). The third Mox candidate nests within the Hox4 family and so is likely not a true Mox gene (Supplemental Fig. 2a). A single D. rostriformis Brachyury (Dro-Bra) ortholog nests within the Brachyury family, which includes a specific lysine (K) (Conlon et al., 2001; Sebé-Pedrós et al., 2013) at position 121 (Supplemental Fig. 3). Six further candidates (Eomes, Tbx2, Tbx3, Tbx15, twice Tbx20) with a T-box domain were found in D. rostriformis and nest within the corresponding family (Supplemental Fig. 3 and Supplemental Table 7).
At 18 °C, a free-swimming ciliated gastrula forms by 18 h post fertilisation (hpf). The developing shell field is characterised by a deep invagination on the dorsal side that is surrounded by large ectodermal cells, while the blastopore marks the ventral side (Figs. 1 and 2). By about 24 hpf, the early trochophore larva has developed. Evagination of the shell field commences and is completed by approximately 30 hpf. A ciliated two-rowed prototroch is distinct, together with an apical tuft and a posterior telotroch (Figs. 1, 2, 3, and 4). Between 30 and 40 hpf, an early (D-shaped) veliger larva has developed. It is characterised by two lateral valves that form the embryonic shell (protoconch I: often referred to prodissoconch in bivalves; see Wanninger & Wollesen, 2015) and by a ciliated velum that forms from the prototroch. In addition, Dreissena veliger larvae also exhibit a pre-anal tuft on the ventral side, a telotroch on the ventro-posterior side, and a functional digestive tract. After 4–5 days, the D-shape of the veliger larva changes and the umbo begins to form (Fig. 5). The oldest veliger larvae were over 1 month old.
Dro-Bra shows high relative expression with respect to other genes during early stages (< 18 hpf), with a considerable relative decrease in the gastrula stage (18–23 hpf) (Supplemental Table 5). Relative expression values remain low in the trochophore (23–30 hpf) and veliger stage (> 30 hpf) (Supplemental Fig. 4a and Supplemental Table 5). Dro-Bra expression is first detected in the developing mesoderm in the ventral region of the gastrula (18 hpf) (Fig. 1a, b). In the trochophore larva (30 hpf), Dro-Bra is expressed in the endoderm on either side along the invagination of the developing digestive tract (Fig. 1c, d). In addition, expression of Dro-Bra is present in the ventro-posterior mesoderm and is located posteriorly to the developing digestive tract in the region of the future hindgut (Fig. 1c, d). No expression of Dro-Bra was observed in the veliger larva. Dro-eve transcripts show high relative expression values with respect to other genes in early stages (< 18 hpf), with a considerable decrease from the gastrula stage (18–23 hpf) onwards (Supplemental Fig. 4a and Supplemental Table 5). Using in situ hybridization, Dro-eve expression was first detected in the gastrula stage (18 hpf) in three distinct domains. One domain corresponds to the dorsal ectoderm of the shell field (Fig. 2a, c), while the other two are situated ventrally in the developing mesoderm close to the Dro-Bra expression domains (Fig. 2a, b). Trochophore larvae (30 hpf) show two Dro-eve expression domains, one in the dorsal ectoderm in the median region of the shell field and one in the ventro-posterior mesoderm (Fig. 2d, e). The mesodermal expression of Dro-eve is located posteriorly to the developing digestive tract and lies adjacent to the expression of Dro-Bra, with the former extending further laterally (Fig. 2d, e). No expression domains of Dro-eve were observed in the veliger larva.
Dro-mhc candidate genes are relatively lowly expressed in early stages (< 18 hpf) with respect to other genes, with a slight relative increase in the gastrula stage (18–23 hpf). This is followed by further relative increases in the trochophore stage (23–30 hpf) and, more prominently, in the veliger stage (> 30 hpf) (Supplemental Fig. 4b and Supplemental Table 5). Two Dro-mhc_c1 expression domains are first detected in the anterior mesoderm of the early trochophore larva (24 hpf). They are spot-like and situated in the anterior region between the developing digestive tract and the shell field, close to the first F-actin positive cells that appear at ~30 hpf (Fig. 3). In the trochophore larva (30 hpf), four expression domains of Dro-mhc_c1 are present in the dorsal mesoderm. Two of them are stripe-like and extend along the anterior–posterior axis (Fig. 4e, g). These domains likely give rise to the developing dorsal velum retractors and the larval retractors (Fig. 4i, k). The other two Dro-mhc_c1 expression domains are spot-like and located in the dorso-anterior region (Fig. 4e, g, h). Their position corresponds to that of the anlagen of the anterior adductors (Fig. 4i, k, l). Additionally, four expression domains are found in the ventral mesoderm, adjacent to and posterior of the developing digestive tract as well as in the region of the ventro-posterior musculature and the Dro-Mox_c2 domain (Fig. 4). The ventral expression domains of Dro-mhc_c1 are slightly larger than those of Dro-Mox_c2 (Fig. 4b, f). In the D-shaped veliger larva (70 hpf), three expression domains of Dro-mhc_c1 are present. Two of them are located laterally on both sides of the larva’s median region, at the sites of the velum retractors and larval retractors (Fig. 5). The third expression domain is in the dorsal region between the shell plates, in the region of the developing anterior adductors (Fig. 5).
Dro-Mox_c2 shows low relative expression levels with respect to other genes and is only briefly upregulated in the trochophore and veliger stages at 26 and 36 hpf, respectively (Supplemental Fig. 4a and Supplemental Table 5). Dro-Mox_c2 expression is first (and only) detected in the ventral mesoderm of the trochophore larva (30 hpf). Expression of Dro-Mox_c2 is adjacent to and posterior of the developing digestive tract, on either side (Fig. 4a, b, d). These expression domains correspond to the region of the ventro-posterior musculature and to the ventral expression domain of Dro-mhc_c1 (Fig. 4). No Dro-Mox_c2 expression was observed in the veliger larva.
F-actin staining is first detected in the dorso-median mesoderm of the D. rostriformis trochophore larva (30 hpf). The paired domains are situated below the median region of the shell field (Figs. 3c, d and 6c). From here, the first pair of myofilaments emerges, which gives rise to the dorsal velum retractors and the developing ventral larval retractors that lie below the shell field. The developing dorsal velum retractors project into the anterior region. In contrast, the developing ventral larval retractors extend into the posterior region with a slightly ventral direction (Figs. 4i, k and 6d). The first anlagen of the anterior adductors also form in the trochophore larva, in the dorso-anterior mesoderm below the shell field, and above the developing dorsal velum retractors (Figs. 4i, k, l and 6d). In addition, a pair of transient ventro-posterior muscles emerges that lies posterior to the developing digestive tract (Figs. 4i, j, l and 6d). The first distinct muscle bundles develop in the early (D-shaped) veliger larva (40 hpf). The mantle (pallial) musculature is formed around the edges of the mantle (Figs. 5b and 6f). Two fine interconnections of the anterior adductors are visible and attach dorsally to the embryonic shell (Figs. 5b, c and 6f). A pair of ventral larval retractor muscles attaches to the embryonic shell near the hinge and extends ventrally into the region of the hindgut (Figs. 5b and 6f). The velum musculature consists of the newly formed velum muscle ring that underlies the velum (Figs. 5b and 6f). In addition, a pair of dorsal velum retractors inserts posteriorly at the embryonic shell near the hinge and at the dorsal part of the velum. The second pair of velum retractors, the ventral velum retractors, develops between the dorsal velum retractors and the ventral larval retractors and attaches in the median region of the velum and posteriorly at the embryonic shell near the hinge (Figs. 5b, d and 6f). The third pair of velum retractors, the median velum retractors, emerges later in the D-shaped veliger larva. The attachment is posterior to the embryonic shell near the hinge and at the velum between the dorsal velum retractors and the ventral velum retractors (Figs. 5d and 6g). A median branch of the median velum retractors runs towards the ventral part in the region of the hindgut. This branch shows a connection to the median velum retractor (Figs. 5c, h and 6g). In the late veliger larva (102 hpf), the fourth pair of velum retractors (accessory velum retractors), two pairs of mantle retractors, and one pair of foot retractors become visible (Figs. 5g and 6h). The accessory velum retractors are located between the anterior adductors and the dorsal velum retractors. They attach to the most dorsal region of the velum. Two pairs of mantle retractors are situated between the dorsal and the ventral velum retractors, respectively, and both connect to the mantle. The foot retractor emerges as a branch of the dorsal velum retractor and extends into the region of the developing foot (Figs. 5g and 6h). In the late veliger larva, the anterior adductors increase in size. Shortly thereafter, the foot retractors become interconnected and form a U-shape (Figs. 5f, h, i and 6i).
In Dreissena rostriformis, Bra is expressed in the developing mesoderm in the gastrula stage and in the trochophore larva, with additional expression in the developing foregut. A very similar Bra expression pattern is found in the gastrula of the Pacific oyster Crassostrea gigas (Tan et al., 2017). In the spiny oyster Saccostrea kegaki, first Bra expression is in the vegetal region of the 16-cell stage. After that, Bra is also expressed in the putative mesoderm in the ventral region, similar to C. gigas and D. rostriformis. In S. kegaki, Bra is additionally expressed in the ectoderm along the ventral midline and near the blastopore. After the evagination of the shell field, Bra is restricted to the presumptive anus region, similar to D. rostriformis. In contrast to D. rostriformis, no Bra expression was found in the foregut of S. kegaki (Kin et al., 2009). In most gastropods, Bra expression is similar to that of bivalves, as it is expressed in the mesoderm and digestive tract, as well as ectodermally near the blastopore and along the ventral midline (Fig. 7; Lartillot et al., 2002; Perry et al., 2015). Since the latter expression domain is only present in mollusks, it seems to be an apomorphy of Mollusca, while the other expression domains also occur in other taxa (Fig. 7). Bra expression has been described near and/or around the blastopore and often also in the digestive tract in a vast number of metazoans (Fig. 7; e.g., Peter & Davidson, 2011; Green & Akam, 2014; Hejnol & Martín-Durán, 2015; Martín-Durán et al., 2017; Sebé-Pedrós & Ruiz-Trillo, 2017). This indicates that Bra appears to have a conserved role in blastopore and digestive tract formation amongst bilaterian animals (Fig. 7). Mesodermal expression of Bra has been reported in most nephrozoan taxa (protostomes and deuterostomes), except for a few spiralians, e.g., ectoprocts, phoronids, and chaetognaths, where Bra expression was not detected in the mesoderm (Fig. 7; Andrikou et al., 2019; Green & Akam, 2014; Hejnol & Martín-Durán, 2015; Kusch & Reuter, 1999; Martín-Durán et al., 2012, 2017; Nishino et al., 2001; Perry et al., 2015; Peter & Davidson, 2011; Peterson et al., 1999; Satou & Imai, 2015; Takada et al., 2002; Terazawa & Satoh, 1997; Vellutini et al., 2017). Since mesodermal expression of Bra appears to be absent in the acoel Convolutriloba longifissura, expression of Bra in the mesoderm may have evolved in the lineage leading to the nephrozoans, with a possible loss of function in various spiralians and nematodes, whereby Bra is absent from the genome of Caenorhabditis elegans altogether (Fig. 7; Hejnol & Martindale, 2008a; Martín-Durán & Romero, 2011; Pocock et al., 2004; Sebé-Pedrós & Ruiz-Trillo, 2017). Accordingly, the data currently available suggest that Brachyury was expressed during blastopore and digestive tract development in the last common ancestor (LCA) of Bilateria. In addition, Brachyury was likely involved in mesoderm formation in the LCA of Nephrozoa, with a novel expression of Bra along the ventral midline in the molluscan ectoderm (Fig. 7).
In the gastrula and the trochophore larva of Dreissena rostriformis, eve is found in the developing mesoderm and in the ectoderm of the shell field. These constitute the first eve expression data for any mollusk. In a number of bilaterians and the cnidarian Nematostella vectensis, eve is expressed in the ectoderm, which is commonly associated with hindgut formation and neurogenesis (Fig. 8; Ikuta et al., 2004; Martín-Durán et al., 2017; Ryan et al., 2007; Vellutini et al., 2017). Accordingly, ectodermal expression of eve seems to be a conserved feature across pan-bilaterian taxa (Fig. 8). Expression of eve during mesoderm formation has been documented in vertebrates and the cephalochordate amphioxus, as well as in the majority of protostomes, including most annelids, an ectoproct, C. elegans, and arthropods (Fig. 8; Ruiz et al., 1989; Ferrier et al., 2001; Seebald & Szeto, 2011; Kozin et al., 2016; Martín-Durán et al., 2017; Vellutini et al., 2017). In Artemia franciscana, Drosophila, and C. elegans, eve is additionally expressed in mesoderm derivatives such as muscle and/or heart cells, and eve expression is required for limb development in the mouse and zebrafish (Ahringer, 1996; Copf et al., 2003; Fujioka et al., 2005; Hérault et al., 1996; Sordino et al., 1996). However, eve was not found to be expressed in the mesoderm in a few protostomes, including brachiopods, a nemertean, and a priapulid, as well as in some deuterostomes, e.g., a sea urchin and the ascidian Ciona intestinalis (Fig. 8; Ikuta et al., 2004; Li et al., 2014; Martín-Durán & Hejnol, 2015; Martín-Durán et al., 2015, 2017). Since eve is neither expressed in the mesoderm of the acoel C. longifissura, it appears that eve may have evolved a role in mesoderm formation only after the acoel-nephrozoan split with loss of function in multiple lineages. This notion is further supported by absence of the even-skipped gene in ctenophores, placozoans, and poriferans (Fig. 8; Hejnol & Martindale, 2008b; Leininger et al., 2014; Ryan et al., 2010; Schierwater et al., 2008).
In the trochophore larva of Dreissena rostriformis, Mox is expressed in the ventral mesoderm in the region of the ventral mhc domain and at the site of the developing ventro-posterior musculature. Mesodermal and/or muscular Mox expression is also found in other mollusks, lophotrochozoans, protostomes, chordates, and cnidarians, suggesting that Mox might have already played a role in their development in the LCA of nephrozoans and cnidarians (Fig. 9; Andrikou & Hejnol, 2021; Candia & Wright, 1995; Chiang et al., 1994; Chiori et al., 2009; Hinman & Degnan, 2002; Ikuta et al., 2004; Kozin et al., 2016; Lowe et al., 2006; Mankoo et al., 1999; Minguillón & Garcia-Fernàndez, 2002; Neyt et al., 2000; Passamaneck et al., 2015; Rallis et al., 2001; Ryan et al., 2007; Satou & Imai, 2015). The apparent lack of Mox in ctenophores, placozoans, poriferans, and C. elegans suggests that this gene family emerged at the base of the eumetazoan lineage with secondary loss in the nematode (Fig. 9; Ruvkun & Hobert, 1998; Ryan et al., 2010; Schierwater et al., 2008). In the sea urchin embryo and in Drosophila, Mox is involved in neurogenesis (Fig. 9; Chiang et al., 1994; Poustka et al., 2007). It thus appears likely that Mox expression in neural cells may have evolved independently in these lineages, but the database is as of yet too scarce to unequivocally resolve this issue.
Five distinct muscle systems are present in the veliger larva of Dreissena rostriformis, namely the velum muscle ring, four pairs of velum retractors, one pair of ventral larval retractor, one pair of foot retractor, the mantle musculature including the muscles of the pallial line, and two pairs of mantle retractors, as well as an initially paired anterior adductor (Figs. 6 and 10). A posterior adductor muscle and pedal plexus (foot musculature), as present in the adult, were not found, which most likely emerge in late larval stages or after metamorphosis. The velum muscle ring degenerates prior to or at metamorphosis and has been reported in dreissenids, teredinids, and mytilids but not in other bivalve larvae (Fig. 10a; Audino et al., 2015; Dyachuk & Odintsova, 2009; Kurita et al., 2016; Li et al., 2019; Sun et al., 2020; Wurzinger-Mayer et al., 2014). However, since the prototroch/velum muscle ring occurs in almost all class-level sublineages of mollusks with indirect development except for the scaphopods, it seems most likely that it is part of the molluscan—and thus also the bivalve—larval muscular ground pattern (Fig. 10a; Wanninger & Wollesen, 2015). The velum retractors have been documented in all veliger larvae of autobranch bivalves investigated to date and are resorbed prior to or during metamorphosis. Their number differs between species; e.g., four pairs are common in euheterodonts, except for the teredinid shipworm Lyrodus pedicellatus, where two pairs are present (Fig. 10a; Wurzinger-Mayer et al., 2014). Interestingly, the two velum retractor pairs of the shipworm were suggested to transform into the future mantle musculature. However, this condition has not been described for any other mollusk and, if true, most likely constitutes an apomorphy of this genus or species (Wurzinger-Mayer et al., 2014). In pteriomorph larvae, four pairs of velum retractors were found in pectinids, whereas three pairs are present in oysters and two to three pairs were described in mytilids (Fig. 10a; Audino et al., 2015; Cragg, 1985; Dyachuk & Odintsova, 2009; Kurita et al., 2016; Li et al., 2019; Sun et al., 2019, 2020). Accordingly, three or four pairs of velum retractors appear most likely to be a part of the myoanatomical ground pattern in autobranch bivalve larvae (Fig. 10). The larval retractors disappear prior to or at metamorphosis and are present in most autobranch bivalve lineages, even in the semi-direct (brooding) lasaeids (Altnöder & Haszprunar, 2008). However, the number of larval retractors differs amongst species; e.g., one (ventral) pair is common in imparidents, except in the lasaeids which contain three pairs, while in pteriomorphs, one to five pairs are present (Fig. 10a; Audino et al., 2015; Kurita et al., 2016; Li et al., 2019; Sun et al., 2020; Wurzinger-Mayer et al., 2014). Accordingly, one or two pairs of larval retractors appear most likely to be a part of the muscular ground pattern in autobranch bivalve larvae (Fig. 10). A dimyarian condition, i.e. the presence of an anterior and a posterior adductor muscle, is common for many adult bivalves. They are usually formed in the larva and are transiently present in pectinid and oyster larvae that as adults only have one adductor. Here, the adult monomyarian condition is achieved by loss of the anterior adductor at metamorphosis (Fig. 10a; Audino et al., 2015; Cragg, 2016; Drew, 1899, 1901; Li et al., 2019; Sun et al., 2019, 2020; Wurzinger-Mayer et al., 2014). Interestingly, a transient larval adductor is also present in the parasitic glochidium larva of unionids, but the adult anterior and posterior adductors appear to develop independently during metamorphosis (Herbers, 1913). In most bivalves, the muscles of the pallial line develop in later larval stages while the paired (adult) mantle retractors are formed after metamorphosis. Their number differs amongst species; e.g., two pairs of retractors are present in dreissenids and montacutids, while three pairs were found in the teredinids (Fig. 10a; Dyachuk & Odintsova, 2009; Li et al., 2019; Sun et al., 2020; Wurzinger-Mayer et al., 2014). This variation is also found in the (adult) foot retractors, where one pair is present in dreissenids and montacutids, while two pairs are common in most other autobranchs (Fig. 10a). Taken together, it appears that at least a velum muscle ring, three or four pairs of velum retractors, one or two pairs of larval retractors, an anterior and a posterior adductor, and two pairs of foot retractors together with the plexus-like foot musculature as well as the mantle musculature including muscles of the pallial line and possibly two pairs of mantle retractors, are part of the muscular ground pattern of autobranch bivalve larvae (Fig. 10). The two-partite condition of the anterior adductor in early development throughout Autobranchia might argue for a paired anterior adductor in the LCA of autobranchs or even Bivalvia. For further assessments concerning the ground plan of the entire Bivalvia, more data on the Protobranchia, the sister taxon to all other bivalves, are required.
The present study shows that expression of Bra, eve, and Mox in the quagga mussel Dreissena rostriformis is congruent with numerous other bilaterian taxa. The data currently available suggest that Mox had an ancestral role in bilaterian mesoderm formation, while even-skipped and Brachyury have obtained their mesodermal expression domains after the xenacoelomorph-nephrozoan split. The data on bivalve myogenesis indicate that the muscular ground pattern of autobranch—and maybe even all—bivalve larvae contains a highly complex arrangement of larval retractor muscles and heterochronically shifted, functional adult systems that undergo significant, taxon-specific remodelling and reduction events during metamorphosis.
Below is the link to the electronic supplementary material.Supplementary file1 (TIFF 1095 kb)Supplementary file2 (TIFF 1079 kb)Supplementary file3 (TIFF 949 kb)Supplementary file4 (TIFF 855 kb)Supplementary file5 (DOCX 16 kb)Supplementary file6 (DOCX 15 kb)Supplementary file7 (DOCX 16 kb)Supplementary file8 (DOCX 13 kb)Supplementary file9 (DOCX 15 kb)Supplementary file10 (DOCX 14 kb)Supplementary file11 (DOCX 14 kb) |
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PMC9649495 | Gugulothu Baloji,Sandhya Jagtap,Ashwini Talakayala,Meghana Kolli,Lali Lingfa,Mallikarjuna Garladinne,Srinivas Ankanagari | Insights from the protein sequence and structure analysis of PgHsc70 and OsHsp70 genes | 28-02-2022 | Abiotic stress,chaperones, Brassinolide,heat shock proteins,homeostasis,environmental stressors | Heat shock proteins are induced in a wide range of abiotic and biotic stresses. They are well known for cellular chaperone activities and play an important role in protecting plants through regulation of homeostasis and survival. A comprehensive characterization and comparative analysis of the Hsp70 family members within the closely related plant species helps in better interpretation of these proteins' contribution to cell function and response to specific environmental stresses. Therefore, it is of interest to glean insights from the protein sequence analysis of PgHsc 70 and OsHsp70 genes. Thus, we document data from the sequence and structure analysis of PgHsc 70 and OsHsp 70 gene a. | Insights from the protein sequence and structure analysis of PgHsc70 and OsHsp70 genes
Heat shock proteins are induced in a wide range of abiotic and biotic stresses. They are well known for cellular chaperone activities and play an important role in protecting plants through regulation of homeostasis and survival. A comprehensive characterization and comparative analysis of the Hsp70 family members within the closely related plant species helps in better interpretation of these proteins' contribution to cell function and response to specific environmental stresses. Therefore, it is of interest to glean insights from the protein sequence analysis of PgHsc 70 and OsHsp70 genes. Thus, we document data from the sequence and structure analysis of PgHsc 70 and OsHsp 70 gene a.
Plant defense mechanisms are induced rapidly and plants adapt at morphological, molecular and physiological levels [1,2,3]. The sensing of abiotic stresses initiates complex signaling pathways controlling the stress and tolerance responses. The signal transmission of stress and subsequent induction of stress receptive pathways involves expression of genes and proteins related to tolerance that have been studied extensively at the molecular level [4,5]. The molecular mechanisms have identified a large number of genes induced with abiotic stress factors and characterized using approaches such as subtractive cDNA libraries [6], microarrays [7] and NGS based RNA sequencing [8]. In the cell, protein aggregation due to environmental stresses is a major effect resulting in their dysfunction. Understanding the protective mechanisms to abiotic stresses is indispensable for developing crops with increased stress tolerance [9]. In the environmental stress conditions, the cell survival and sustenance is dependent upon protein native conformation and preventing protein aggregation. In the event of environment stress conditions, chaperone proteins, assist to fold cellular proteins into three-dimensional conformation and avoid abnormal folding and aggregation [10,11]. Heat shock proteins (HSPs) are the central chaperone proteins, involved in the maintenance of homeostasis, nascent protein folding, denatured proteins refolding, aggregation prevention and aiding protein transport across the membranes [12,13]. Besides these processes, HSP gene family members also protects the cells from the damage caused in the events of extreme temperatures, salinity, dehydration, oxidative stress, heavy metal toxicity, high intense irradiation and wounding [14,7]. In the cells under higher temperature environments, the HSPs are immediately synthesized and expressed, and on the other hand, most of other proteins' synthesis is detained. Thus, heat shock proteins are performing key role in protecting plants through cellular homeostasis regulation to stress conditions [15,16]. Furthermore, based on stress signal, research has shown that high HSPs expression and accumulation is involved in different stress signaling pathways. In plants, the HSP induction, synthesis and increase in thermo tolerance are well documented [17,18,19]. Based on the molecular weights, five families of heat shock proteins are identified. The major HSPs families are chaperonin (Hsp60/GroEL), 70-kDa Hsp (Hsp70/DnaK), Hsp90, Hsp100/ClpB, and the small heat shock proteins (sHsp) [20]. Of these, the HSP70 family is of greatest interest. It is evolutionarily conserved, present in archaebacteria, plants and humans [21]. The four Hsp70 gene subgroup family members are localized in the sub-cellular compartments: plastids, mitochondria, endoplasmic reticulum and cytosol [22]. Furthermore, Hsp70 family includes genes that are constitutive (housekeeping) and predominantly associated with physiological functioning such as heat shock cognate (hsc) 70 gene, or stress-induced such as hsp70. In general, the newly synthesized proteins are folded by constitutive expressed members whereas protein translocation into the organelles, involve stress-induced members that re-fold and degrade mis-folded proteins in adverse environmental stress conditions [23,24]. Both these proteins have modular structure playing role in cell growth with conserved ATPase domain and hydrophobic pocket with lid-like structure of substrate-binding domain (SBD) at N-terminal end and variable C-terminal domain but conserved [23]. Because of diverse subcellular localizations, Hsp70 plays critical role in development or specific protein communications [25]. The activity of Hsp70 is also modulated by post-translational modifications and by interaction with other co-chaperones [26]. The yield improvement of crops in the unfavorable abiotic conditions is a challenge. In particular the role played by Poaceae crops in food demand is well known, contributing high amount of calories in the human diet [27,28]. Wheat, rice, maize, millet, sorghum, barley and rye starchy grains serving as important food sources for the world's majority population [27,28]. The Hsp70 family members comprehensive characterization in plant species is needed to know how these members contribute towards cell function and protect in adverse abiotic stresses 24]. Therefore, it is of interest to document the protein sequence analysis data of PgHsc 70 and OsHsp 70 genes to glean useful information.
The complete nucleotide sequence and the CDS of Pennisetum glaucum heat shock cognate 70 kDa protein (PgHSC70) and Oryza sativa hsp70 gene for heat shock protein 70 (OsHSP70) registered in GenBank and their protein sequences are obtained in FASTA format from National Center for Biotechnology information (NCBI). The gene and protein structural and functional analysis of sequences was done using in-silico tools.
The gene structure of PgHSC70 and OsHSP70 were predicted based on the genome and coding sequences using the Gene Structure Display Server. This server analyses exon/intron organization of PgHSC70 and OsHsp70 genes. In the gene structure analysis exons/introns and intronic phase distribution (phase 0, 1, 2) were identified and marked. Based on position relative to reading frame three intron phases exist: insertion between two codons (phase 0), insertion after first base codon (phase 1) or after second base codon (phase 2).
Using MEME suite sequence motifs were scanned over the nucleotide sequences in PgHSC70 and OsHSP70 genes. Input of cDNA sequences of PgHSC70 and OsHSP70 genes were given to the MEME suite. In a default setting, it can help in predicting up to three motifs and selected for finding distribution of motifs with three different parameters. Parameters were optimized in MEME suite set to 10 as maximum number and 1 as minimum occurrence of motif site per sequence. All the other parameters were kept in default value. Predicting the width and the occurrence number of motifs, in order to minimize the E-value, was automatically done with MEME suite.
Using Plant CARE database cis-acting regulatory elements (CREs) were scanned in PgHSC70 and OsHsp70 genes. The PgHSC70 and OsHsp70 genes cDNA sequences were uploaded and evaluated for cis-regulatory response elements presence in promoter regions to predict computationally the regulatory elements. For the response elements shown, a matrix value of ≥5 was considered for acceptance on the sense strand. The obtained cis-elements were compared with each other.
Plant small RNA-targeted gene prediction was performed on sequences PgHSC70 and OsHsp70 genes using psRNATarget server. The miRNA target sites were analyzed using default parameters.
For identification of similarity of PgHSC70 and OsHsp70, the homology search of each protein was performed by BLAST using blastp algorithm respectively. Using multiple sequence alignment (MSA) tool ClustalW2, the protein sequences of all the identified homologues of PgHSC70 and OsHsp70 were aligned. Using the BLOSUM 62 substitution matrix evolutionary alignment was inferred with progressive method. The phylogenetic tree was constructed for the identified PgHSC70 and OsHsp70 proteins using ClastalW2.
Physicochemical characterization of the target protein sequences of PgHSC70 and OsHsp70 such as mol. wt, aa composition, isoelectric point (pI), instability index (II), the total negative and positive residues, extinction coefficient (EC), grand average of hydropathicity (GRAVY) and aliphatic index (AI) were analyzed using Expasy's ProtParam prediction server.
SOPMA tool was used to predict PgHSC70 and OsHsp70 proteins secondary structure for assigning positional possibility of various regions of α-helix, β-strands, turns as well as random coils likely to fold. The method makes use of predicting consensus from multiple alignments of the relative frequencies of each amino acid anchored in the X-ray crystallographic solved protein templates.
PgHSC70 and OsHsp70 subcellular localization was predicted using CELLO v.2.5 which is a multiclass support vector machine classification system. SignalP was used to verify the presence of signal peptide cleavage sites and their locations in both proteins, which works on the basis of a combination of several neural networks, namely artificial neural network (ANN) and Hidden Markov Model (HMM).
Using STRING (Search Tool for Retrieval of Interacting Genes) v 9.1 protein-protein interactions (PPIs) analysis was done. The STRING repository consisted of PPIs concerning stable protein complexes, functional and regulatory interactions. The PPIs of PgHSC70 and OsHsp70 were searched individually by submitting a protein query sequence in the search box of STRING. Determining the protein–protein interaction network would empower study of signaling pathways.
In protein folding and formation a functional and stable confirmation is determined by disulfide bonds among its cysteine residues. To predict cysteine bonds (disulfide bonds) presence and absence and their bonding patterns CYS_REC tool was used.
The targets in PgHSC70 and OsHsp70 were predicted for putative acetylation, methylation, phosphorylation, ubiquitination, and N-glycosylation sites. phosphorylation at serine, threonine and tyrosine residues in the PgHSC70 and OsHsp70 proteins were predicted using NetPhos 3.1 was used. To complete this task it ensembles neural networks and residues having scores 0.5 threshold as phosphorylated. The N-glycosylation sites of the target proteins were predicted with NetNglyc 1.0 server, with threshold value of >0.5. By default the predictions are done only on the Asn-Xaa-Ser/Thr sequons.
The estimation of intrinsic disordered regions (IDRs) of PgHSC70 and OsHsp70 was made by DisEMBL tool.
The protein structures of PgHSC70 and OsHsp70 was modeled using the bovine HSC70 (PDB ID: 1YUW) as template sequence exhibiting the highest similarity identified by BLAST against the PDB database. DS was used to design a homology model of both proteins and the each protein model with less geometric function was selected and energy minimized in CHARMm force field using DS minimization algorithms. For structural validation, the obtained final models are further subjected to PROCHECK for Psi/Phi Ramachandran plots analysis. Protein binding/catalytic sites are identified using DS Analyze Binding Site tool.
The homology models of PgHSC70 and OsHSP70 were analyzed for docking with brassinolide. Using DS LibDock docking simulation was performed. The obtained confirmations were then summarized and analyzed for interactions. The interactions showing highest scores and docking energy were considered best for protein-ligand complex structure.
Comparative alignment of genomic and cDNA nucleotide sequences of PgHsc70 and OsHSP70 genes is shown in Figure 1(see PDF). The intron phases consisted of phase 0, 1 and 2. Exon count and intronic phase distribution was similar in both the genes. Two exons were present in both the genes and their distribution was found at phase 0 (exon 1) and phase 2 (exon 2). Phase 1 intronic phase distribution was found in both the genes. The intron length varied with OsHSP70 intron of 1935 bp while PgHSC70 intron is 141 bp. The MEME tool identified 10 significant conserved motifs as shown in Figure 2(see PDF). The length of conserved motifs varied from 21 to 50 amino acids. The consensus sequence motifs identified are given in Table 1(see PDF). In silico analysis of cis-regulatory elements (CREs) in the CDS sequence of PgHSC70 and OsHsp70 genes revealed different elements in the upstream region (Table 2-see PDF). In PgHSC70 a total of 33 CAREs whereas, in OsHSP70, 29 CAREs were identified and a few were uniquely present in each gene. Cis-elements were found responsive to light, meristem-specific activation, abscisic acid, methyl jasmonate, gibberellins, low temperature, seed-specific regulation, root-specific expression, anoxia, and circadian regulation. The CCGTCC motif, ABRE, W box, CCAAT-box, GC motif,, G-Box, MYB recognition site STRE, MYB, TGA-element, plant_AP-2-like, WRE3, and unnamed-1 are the common CREs found in both the genes. All of them were present on sense strand with matrix value ≥ 5. The plant small RNA target analysis server (psRNATarget) was used to predict miRNA target sites. The miRNAs comprising target sites in PgHSC70 and OsHSP70 genes were identified with expectation score lower than 4.0 (Table 3 -see PDF)). MSA using ClustalW was constructed by aligning seven PgHSC70 protein sequences along with twelve OsHsp70 protein sequences. Phylogenetic tree based on MSA is shown in Figure 3(see PDF). Comparative phylogenetic analysis of PgHSC70 and OsHsp70 revealed major groups of HSP70 genes with paralogous as well as orthologous genes. Each group contained both PgHSC70 and OsHsp70. The Expasy's ProtParam tool determined the length (649 and 648 aa), mol. wt. (71105.48 and 70997.56 Da), isoelectric point (pI=5.10 and 5.16), the total negative and positive charged residues of PgHSC70 and OsHsp70 proteins (100 and 98 and 82 and 82, respectively). Proteins are found be acidic (pI<7). The amino acid composition analysis revealed that Ala (58) was the most abundant amino acid in both the sequences, while Trp (3) was the least abundant (Table 4-see PDF). PgHsc 70 had the atomic composition C2777H4999N861O995S24, while OsHsp70 had the atomic composition C3108H5007N861O987S24. The calculated extinction coefficient (EC) of PgHsc70 and OsHsp70 proteins was found to correlate with the cysteine, tryptophan and tyrosine content at 280 nm as 37735 and 39225 (assuming all pairs of cysteine residues form cysteines) and 37360 and 38850 (assuming all cysteine residues are reduced) M−1cm−1, respectively. The instability index (II) values were 34.52(PgHsc70) and 33.69(OsHsp70) classifying both proteins as stable. Similarly, the aliphatic index (AI) had high value of 81.79 (PgHsc70) and 83.56 (OsHsp70), indicating proteins are thermo stable. The estimated half-lives for both proteins were 30 h in mammalian reticulocytes (in vitro), >20 h in yeast (in vivo) and >10 h in Escherichia coli (in vivo). The GRAVY was -0.427 (PgHsc70) and -0.399 (OsHsp70), respectively. Both proteins are highly water soluble. Table 5(see PDF) shows the predicted physicochemical properties of the PgHSC70 and OsHsp70. In Figures 4 and 5(see PDF), the secondary structure of protein sequences of PgHSC70 and OsHsp70, predicted using SOPMA server are shown. The evaluated percentage α-helices, β-turn, extended strand, and random coils with output width 70 is given in Table 6(see PDF). From the computed percentage of each conformation, α-helix predominated, followed by extended strand random coil and random coil in both the proteins. The high percentage of random coils indicates protein flexibility and more interactions. Also, high coiled structural content might be because of flexible glycine and proline amino acids in the proteins. The sub cellular localization of proteins PgHsc70 and OsHsp70 predicted by CELLO was found to be cytosolic in nature. The SignalP analysis revealed that none of the proteins have any of signal peptide. Using CYS_REC tool the cysteine residues in the proteins determined revealed that the protein PgHsc70 contain cysteine residues in the positions 20, 273, 319, 326, 366, 483 and 609. But all are not involved in disulfide bonding; Cys326 is probably SS-bond with a score of 1.9. On the other hand, OsHsp70 revealed that cysteine residues were present in the position 20, 272, 318, 325, 365, 482 and 608 and probably Cys326 is SS-bonded with a score of 1.9. At the molecular level the disulfide bridges presence is a positive factor for stability. Table 7(see PDF) shows the results obtained using CYS_REC tool. NetPhos 3.1 & NetNglyc servers identify post-translational modification sites. NetPhos retrieved the information related to kinases PKC, Unsp, CKI, cdk5, CKII, PKA, RSK, DNAPK, CKI, PKG, EGFR , SRC and cdc2 involved in the phosphorylation of both proteins and an extra one kinase INSR unique to OsHsp70. In both the proteins high score of 0.691 predicted for site having threonine and the percentage amino acids composition obtained is shown in Figures 6a and 6b(see PDF). Using NetNglyc server, the N-glycosylation sites (38 NRTT, 155 NDSQ, 423 NTTI and 493 NVSA) of the PgHsc70 protein were predicted with a score of 0.66, 0.57, 0.58 and 0.74 respectively. The N-glycosylation sites of OsHsp70 protein predicted were (38 NRTT, 154 NDSQ, 422 NTTI and 492 NVSA) with scores of 0.6378, 0.5774, 0.5874 and 0.7403. The plot of predicted N-glycosylation sites is shown in Figures 7a and 7b(see PDF). Using String, the interacting partners predicted in both PgHsc70 and OsHsp70 is shown in the Figure 8(see PDF). From the analysis, the functional partners observed in the string network of PgHsc70 protein were HSFA2, HSF1, HSP101, HSP90.1, Hsp81.4, Hop3, HSP81-3, LOS1, J3 and Hop1. The functional partners observed in the string network of OsHsp70 are OsJ-17347, OS11T0703900-01, OsJ_11911, HSP81-2, OsJ_12871, DJA6, and OS04T0107900-02. These interactions give some insights into understanding the functioning of these proteins in response to heat stress and tolerance. Using DisEMBL the predicted intrinsic disorder regions (IDRs) of PgHSC70 and OsHsp70 is shown in Figure 9(see PDF). Homology modeling was done to predict the 3-D structures of PgHSC70 and OsHsp70 based on the template structure HSC70 (PDB ID: 1YUW) from bovine, at a resolution of 2.6 Å deposited in PDB. The template protein had identity of 81% (PgHSC70) and 80.18 % (OsHsp70). The initial models of PgHSC70 and OsHsp70 proteins were built using the crystal coordinates information of the template 1YUW. The models generated by DS, were scored with discrete optimized protein energy (DOPE) geometric function, and the model with the lowest DOPE score was taken as final model as shown in Figure 10(see PDF). After the proteins were energy minimized, the final models were validated using PROCHECK. Corresponding to core regions most favorable Psi/Phi value combinations are present in the darkest areas in Ramachandran plot. Each of the protein models displayed 90% accuracy. Overall, the homology model of the PgHSC70 have 94.6% of the residues occurring in most favored region, 4.4 % in allowed regions, and only 1.1 % of the residues in disallowed regions. In comparison, the OsHsp70 homology model have 94.6% residues in favored region, 4.9 % residues in allowed region and 0.5 % residues in outlier region. Molecular docking of PgHSC70 and OsHsp70 with brassinolide was studied in order to identify the critical interactions and their variation. Using LibDock the docking results for brassinolide on PgHSC70, showed high binding affinity with score of 115.231 and binding energy of 0.00119 kcal/mol, in comparison target OsHsp70 showed a LibDock score of 146.59 and binding energy of -26.586 kcal/mol. In the PgHSC70-Brassinolide complex, the electrostatic and −87.3 and -835.7 kcal/mol of van der Waals energies respectively and for OsHsp70-Brassinolide complex -18.332 and 4.139 kcal/mol were found to be higher. Docking analysis revealed both H-bonds and close interactions within the docked site of PgHSC70 and OsHsp70 (Figure 11-see PDF). The PgHSC70-Brassinolide complex formed five hydrogen bonds, 3 with residue THR271, each one with LYS59 and LYS277, and the closest interactions are also found with the amino acid residue GLY236. Whereas the OsHsp70-Brassinolide complex formed two hydrogen bonds with two residues ASP35 and LYS129 and found to interact with the amino acid residue ILE133. The docking studies clearly indicated that the ligand and receptor were bound together closely to stabilize complex structure in OsHsp70 and PgHsc 70 as done in our previous study [29].
We have documented the characterization of Hsp70 gene family members, PgHSC70 and OSHsp70 genes, and their proteins sequences in pearl millet and rice respectively. The results indicated conserved relationships and distinct functions of PgHSC70 and OSHsp70 highlighting the wide participation of these family members in environmental adaptation. Data from docking analysis of the homology models with brassinolide is also reported. |
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PMC9649517 | Luyun Xu,Yan Ye,Yuqin Sun,Wenting Zhong,Liangjie Chi,Youyu Lin,Hongxia Liu,ShengZhao Li,Hui Chen,Chengcheng Li,Yuxuan Lin,Qingshui Wang,Fangqin Xue,Yao Lin | Low FNDC5/Irisin expression is associated with aggressive phenotypes in gastric cancer 10.3389/fphar.2022.981201 | 28-10-2022 | gastric cancer,FNDC5,irisin,KLF9,methylation,immune | Background: FNDC5 belongs to the family of proteins called fibronectin type III domain-containing which carry out a variety of functions. The expression of FNDC5 is associated with the occurrence and development of tumors. However, the role of FNDC5 in gastric cancer remains relatively unknown. Methods: In the research, the expression of FNDC5 and its value for the prognosis of gastric cancer patients were observed with the TCGA database and GEO datasets of gastric cancer patients. The role of FNDC5 in the regulation of gastric cancer cells proliferation, invasion, and migration was determined. WGCNA and Enrichment analysis was performed on genes co-expressed with FNDC5 to identify potential FNDC5-related signaling pathways. Meanwhile, the LASSO Cox regression analysis based on FNDC5-related genes develops a risk score to predict the survival of gastric cancer patients. Results: The expression of FNDC5 was decreased in gastric cancer tissues compared to normal gastric tissues. However, survival analysis indicated that lower FNDC5 mRNA levels were associated with better overall survival and disease-free survival in gastric cancer patients. Meanwhile, a significant negative correlation was found between FNDC5 and the abundance of CD4+ memory T cells in gastric cancer. In vitro overexpression of FNDC5 inhibits the migration and invasion of gastric cancer cells, without affecting proliferation. Finally, A two-gene risk score module based on FNDC5 co-expressed gene was built to predict the overall clinical ending of patients. Conclusion: FNDC5 is low expressed in gastric cancer and low FNDC5 predicts a better prognosis. The better prognosis of low FNDC5 expression may be attributed to the increased number of CD4+ memory activated T-cell infiltration in tumors, but the exact mechanism of the effect needs to be further explored. Overexpressing FNDC5 inhibits the invasion and migration of gastric cancer but does not affect proliferation. At last, we constructed a clinical risk score model composed of two FNDC5-related genes, and this model may help lay the foundation for further in-depth research on the individualized treatment of gastric cancer patients. | Low FNDC5/Irisin expression is associated with aggressive phenotypes in gastric cancer 10.3389/fphar.2022.981201
Background: FNDC5 belongs to the family of proteins called fibronectin type III domain-containing which carry out a variety of functions. The expression of FNDC5 is associated with the occurrence and development of tumors. However, the role of FNDC5 in gastric cancer remains relatively unknown. Methods: In the research, the expression of FNDC5 and its value for the prognosis of gastric cancer patients were observed with the TCGA database and GEO datasets of gastric cancer patients. The role of FNDC5 in the regulation of gastric cancer cells proliferation, invasion, and migration was determined. WGCNA and Enrichment analysis was performed on genes co-expressed with FNDC5 to identify potential FNDC5-related signaling pathways. Meanwhile, the LASSO Cox regression analysis based on FNDC5-related genes develops a risk score to predict the survival of gastric cancer patients. Results: The expression of FNDC5 was decreased in gastric cancer tissues compared to normal gastric tissues. However, survival analysis indicated that lower FNDC5 mRNA levels were associated with better overall survival and disease-free survival in gastric cancer patients. Meanwhile, a significant negative correlation was found between FNDC5 and the abundance of CD4+ memory T cells in gastric cancer. In vitro overexpression of FNDC5 inhibits the migration and invasion of gastric cancer cells, without affecting proliferation. Finally, A two-gene risk score module based on FNDC5 co-expressed gene was built to predict the overall clinical ending of patients. Conclusion: FNDC5 is low expressed in gastric cancer and low FNDC5 predicts a better prognosis. The better prognosis of low FNDC5 expression may be attributed to the increased number of CD4+ memory activated T-cell infiltration in tumors, but the exact mechanism of the effect needs to be further explored. Overexpressing FNDC5 inhibits the invasion and migration of gastric cancer but does not affect proliferation. At last, we constructed a clinical risk score model composed of two FNDC5-related genes, and this model may help lay the foundation for further in-depth research on the individualized treatment of gastric cancer patients.
Gastric cancer (GC) is a malignant tumor of the digestive tract with the second-highest mortality rate and the incidence is increasing annually (Maak et al., 2021). Multifactorial etiology of gastric cancer means that both environmental and genetic factors contribute to the development of the disease. Studies have shown that inflammatory gastrointestinal disease, dietary habits, smoking and alcohol consumption, and genetics are risk factors for GC (Rabiee et al., 2020). Although the main interventions for gastric cancer are surgery, chemotherapy, and drugs, their prognosis is poor due to the lack of specific symptoms and diagnostic markers for early GC (Joshi and Badgwell, 2021), Therefore, further research on gastric carcinogenesis and progression is particularly important for early diagnosis and improving prognosis and survival of patients with advanced gastric cancer (Thrift and El-Serag, 2020). The FNDC protein is characterized by at least one fibronectin type III domain (FN3) (Nie et al., 2020). They are necessary for various functions including tissue development and cell adhesion, migration and proliferation, and apoptosis. To date, FNDC5 is the most extensively studied FNDC, mainly because it is a launch vehicle for the peptide hormone irisin that was proposed to promote the conversion of white adipose tissue to beige adipose tissue (Waseem et al., 2022). Irisin has been shown to affect the proliferation of some cancer cells and the chemosensitivity of anticancer drugs like doxorubicin. Numerous studies have demonstrated that FNDC5 exerts differential effects on tumor cell proliferation and apoptosis in breast, lung, and liver cancer through multiple mechanisms (Kuloglu et al., 2016; Zhang et al., 2018; Nowinska et al., 2019; Pazgan-Simon et al., 2020; Cebulski et al., 2022; Taken et al., 2022). FNDC5 plays an important role in the occurrence, development, and metastasis of different tumors, suggesting that FNDC5 may serve as a potential target for tumor diagnosis and therapy (Pinkowska et al., 2021). Therefore, the expression and function of FNDC5 in tumors may be of great significance for the prevention and treatment of tumors. The expression of FNDC5 and its value for the prognosis of gastric cancer patients were investigated. In addition, enrichment analysis was performed on genes co-expressed with FNDC5, and correlated regulatory genes were screened. A risk score and clinical prognosis prediction model for predicting OS in gastric cancer patients was constructed to evaluate the prognostic value of patients.
Clinical data of gastric cancer patients were collected from The Cancer Genome Atlas (TCGA) database (https://www.cancer.gov/tcga) and the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Five microarray gene expression datasets (GSE13195, GSE27342, GSE63069, GSE62254, and GSE65801) of gastric cancer patients were obtained from the GEO database. For the TCGA dataset, RNA sequencing data (FPKM values) were normalized into log2 (FPKM + 1). The method for extracting microarray gene expression values was based on our previous study.
pcDNA3.1-HA-FNDC5 (Jingkairui, Wuhan, China) was constructed to express full-length FNDC5 with an HA tag at the N-terminal end. pcDNA3.1-HA-FNDC5 was constructed by restriction enzyme sites BamH I and EcoR I site.
SNU-1 and AGS cells were obtained from ATCC. both cells were cultured in RPM1 1640 medium (Biological Industries (BI), Israel) supplemented with 10% fetal bovine serum (FBS, BI), and cells were treated with 100 μg/ml penicillin and 100 μg/ml streptomycin (1% Pen/Strep) (BBI Life Sciences, Shanghai, China) at 37°C in a humid environment with 5% CO2. Cells were transfected using the VitalGENE-II In Vitro DNA Transfection Kit (biocanaan) according to the manufacturer’s instructions.
Lysis of cells in Radioimmunoprecipitation Assay (RIPA) lysis buffer (Roche Ltd, Basel, Switzerland) containing protease inhibitors. Protein concentration in the lysates was measured by Micro BCA Protein Assay Kit (Pierce Biotechnology). Samples were separated on 10% SDS-PAGE and subsequently transferred to Amersham Protran nitrocellulose membranes (GE Healthcare Life Sciences, Fairfield, United States). The nitrocellulose membranes were then incubated with primary antibodies for the target proteins FNDC5 and GAPDH (Abmart) (Proteintech, Wuhan, China) at a dilution of 1:1,000 for 2 h. IRDye® 800CW Goat Anti-Rabbit IgG or IRDye® 680RD Goat anti-Mouse IgG (The proteins were detected and quantified using the Odyssey® CLx Infrared Imaging System (LI-COR Biosciences).
Cells were plated in 96-well plates at 2 × 106 cells per well, for a total of 4 96-well plates. The first 96-well plate measured was set to 0 h, and then one 96-well plate was taken out every 24 h 10 μl of CCK8 solution was mixed with 100 μl of 1640 medium and added to each test sample before measurement, and the 96-well plate was incubated at 37°C and 5% CO2 for 1.5 h. Measure the OD of each well with a microplate reader, set to 450 nm, and collect and analyze the data. Four gradient times were measured: 0 h, 24 h, 48 h, and 72 h. The experiment was performed at least 3 times.
When the cells were fully adhered to the cells and covered well (at least 95% confluence), a 10 μl pipette was used to gently scratch each well. After scratching, rinse the cells several times with PBS until any dead cells or debris floating in the culture wells are washed away. Replace the cultured cells with 2 ml of serum-free antibiotic-free medium. Six-well plates were photographed with an inverted phase contrast microscope at a time point we call “0 h”. The six-well plates were incubated at 5% CO2 and 37°C. After 48 h of incubation, the plates were rinsed with PBS and photographed again. Multiple views of each well were measured and three independent experiments were performed, which were repeated at least three times.
500 μl of complete medium was added to the 24-well plates, and Transwell chambers with matrix gel (Corning, New York, United States) were placed in the 24-well plates. The cell density was diluted to 1 × 10 4 cells/mL by incubation in a 37°C incubator for 15 min 0.5 ml of cell dilution was added to each well of the Transwell chambers, and three replicate controls were added to each group. The medium was discarded and the cells in the inner basement membrane were removed with a cotton swab after 48 h (note that the lower outer layer of the basement membrane should not be touched to avoid wiping off the cells that had invaded). After fixation of the cells, approximately 300 μl of gentian violet dye solution was added to the Transwell for 10 min. After staining, the staining solution was discarded and excess dye was removed from the upper layer of the substrate membrane and the surrounding area with a cotton swab. The Transwell chamber was photographed with an inverted phase contrast microscope (Carl Zeiss).
The abundance of 22 types of immune cells was calculated by using CIBERSORT through sangerbox (http://sangerbox.com/Tool). And the correlation between FNDC5 expression and 22 types of immune cells in gastric cancer was also analyzed.
The Cistrome DB Toolkit database (http://dbtoolkit.cistrome.org) is a website that allows users to query TFs that might regulate the expression of genes. In the study, Cistrome DB Toolkit was performed to predict which TFs are most likely to decrease FNDC5 expression in gastric cancer.
The methylation site and beta values of the FNDC5 promoter in gastric cancer were obtained from UCSC Xena Browser (http://xena.ucsc.edu/). At each CpG site, methylation is quantified by beta value to evaluate the degree of methylation.
In the study, WGCNA was performed through sangerbox (http://sangerbox.com/Tool). The WGCNA hypotheses that the co-expression gene network follows the scale less distribution firstly, defines the adjoining function of the gene co-expression correlation matrix and gene network formation, calculates the different coefficients of different nodes, and constructs a hierarchical cluster tree accordingly finally.
To further analyze the potential functions of genes positively correlated with FNDC5 expression, the DAVID (http://david.abcc.ncifcrf.gov/) database was used. p < 0.05 was set as the cut-off criterion.
Irisin (Cat No.ADI-908-307-0010) was purchased from Enzo Life Sciences (Farmingdale, NY, United States).
The statistical correlation was calculated using the t-test in this study. Overall survival (OS) and disease-free survival (DFS) were calculated by the Kaplan-Meier method, and differences between groups were tested by a log-rank test. All p-values < 0.05 were considered statistically significant.
To investigate the FNDC5 expression model for gastric cancer, four related GEO datasets (GSE13195, GSE27342, GSE63069, and GSE65801) and TCGA datasets containing gastric cancer and adjacent normal gastric tissues were used. Analysis of these datasets showed that the expression of FNDC5 was decreased in gastric cancer tissues compared with adjacent normal gastric tissues (Figure 1; Supplementary Table S1). Gastric cancer is classified into two major histological subtypes, diffuse-type and intestinal-type adenocarcinoma. Based on TCGA dataset, we found that the expression of FNDC5 was decreased in intestinal-type adenocarcinoma compared with diffuse-type adenocarcinoma (Supplementary Figure S1). To identify whether the expression of FNDC5 is associated with overall survival (OS) and disease-free survival (DFS), 300 gastric cancer patients with both OS and DFS information in GSE62254 was analyzed. To our surprise, we observed that patients with low FNDC5 expression had significantly poorer OS and DFS (Figure 2; Supplementary Table S2).
Next, we evaluated whether FNDC5 affected the tumor microenvironment and the effect on immune cells infiltrating inside the tumor. We estimated the correlation of FNDC5 expression with 22 immune cells based on signature expression data from gastric cancer patients and found a significant negative correlation between FNDC5 and CD4+ memory-activated cells by using the CIBERSORT algorithm (Figure 3; Supplementary Table S3).
To investigate the impact of FNDC5 on gastric cancer cell proliferation, migration, and invasion, we transfected SNU-1 and AGS cells with HA-FNDC5. Then, we examined FNDC5 expression using RT-PCR and western blot. FNDC5 mRNA and protein were markedly expressed in SNU-1 and AGS cells as reflected by RT-PCR (Figures 4A,B) and western blot analysis (Figures 4C,D). The functional experiments showed that FNDC5 overexpression did not affect the proliferation of SNU-1 and AGS cells (Figures 4E,F), but inhibited the migration (Figures 4G,H) and invasion (Figures 4I,J) of SNU-1 and AGS cells. In addition, we explored the role of irisin in gastric cancer by the addition of exogenous irisin. The results suggest that irisin can suppress the migration and invasion of gastric cancer cells (Figure 5).
To identify members of a putative molecular network that regulates the expression of FNDC5, we investigated transcription factors (TFs) that can affect the transcription of the FNDC5 gene. First, the top 20 regulatory transcription factor TFs in human cancers were identified using the Cistrome DB toolkit (Figure 6A), and KLF9 was found to have the greatest regulatory potential (Figure 6B). Additionally, KLF9 expression is significantly positively correlated with FNDC5 in the GSE62254 database (Figure 6C). Next, the expression of KLF9 in gastric cancer tissues and adjacent tissues was analyzed by GEO dataset (GSE13195, GSE27342, GSE63069, and GSE65801) and TCGA database (Figure 6D). The results of the analyses revealed that KLF9 expression was also decreased in gastric cancer tissues compared with normal gastric tissues. A significant positive correlation was also observed between KLF9 expression and FNDC5 expression in TCGA database (Figure 6E). We also found that the low expression of KLF9 had a better prognostic value (Figure 6F), which was the same as the expression and prognostic analysis of FNDC5. Therefore, KLF9 may play a vital role in regulating the expression of FNDC5 in gastric cancer. Methylation is one of the major mechanisms regulating gene expression and has been demonstrated to control transcription. Studies have demonstrated that increased expression of multiple genes is associated with promoter hypomethylation. The association between FNDC5 and promoter methylation in gastric cancer patients was detected by UCSC Xena. Figure 6G shows the chip quantification of the association between methylation of eight FNDC5 CpG sites and FNDC5 expression (Supplementary Table S4). Only the Pearson correlation coefficient between the methylation site (cg00668227) and FNDC5 was −0.35 (p < 0.05), which suggested that the methylation site (cg00668227) had an adverse significant correlation with FNDC5 expression (Figure 6H). Prognostic analysis showed that the overall survival time of gastric cancer patients was prolonged when a methylation site (cg00668227) was more highly methylated (Figure 6I; Supplementary Table S5). Generally, these results suggest that TFs and DNA methylation modifications potential have an important effect on the occurrence and development of gastric cancer by regulating FNDC5 expression.
To reveal the highly correlated gene and co-expression network of the FNDC5 gene in gastric cancer patients, WGCNA analysis was performed. Gastric cancer samples were obtained from the GSE62254 database for constructing the WGCNA network. We calculated the network topology for soft threshold power from 1 to 30 to choose the best threshold. One of the most critical parameters in the construction of WGCNA networks is the power value, which affects the average connectivity and independence of each co-expression module. A power value of 3 is the minimum power for a scale-free topology (Figures 7A,B). The co-expression similarity matrix was transformed into an adjacency matrix, and the topological overlap matrix (TOM) was calculated. The dynamic tree-cut analysis produced different colored modules, among which 2657 genes co-expressed with FNDC5 belonged to blue modules (Figure 7C; Supplementary Table S6). The results of correlation analysis showed that 1156 genes out of 2567 genes were positively correlated with FNDC5 expression. The top ten genes, including TCEAL2, CASQ2, PDZRN4, BVES, CAND2, RBPMS2, SYNM, THSD7B, C2orf40, and RNF150, most associated with FNDC5 expression are shown in Figure 7D (Supplementary Table S7). Meanwhile, to further characterize the functions of the 1156 genes, we analyzed the GO and KEGG pathways respectively through the DAVID database. KEGG pathway enrichment network analysis showing FNDC5-related 1156 genes enriched in vascular smooth muscle contraction, adrenergic signaling in cardiomyocytes, cGMP-PKG signaling pathway, oxytocin signaling pathway, cardiac muscle contraction, insulin secretion, salivary secretion, tight junction, axon guidance, gastric acid secretion. (Figure 7E; Supplementary Table S8). GO-BP enrichment network analysis showing FNDC5-related 1156 genes enriched in muscle system process, muscle contraction, regulation of system process, circulatory system process, heart process, regulation of heart contraction, regulation of blood circulation, smooth muscle contraction, regulation of muscle contraction (Figure 7F; Supplementary Table S9).
We next performed a univariate Cox survival analysis on the top 10 genes most associated with FNDC5 expression. And the results indicated that these 10 FNDC5-related genes were associated with the prognosis of gastric cancer patients. The high level of these 10 FNDC5-related genes was significantly correlated with shorter OS for gastric cancer patients (Figure 8A). Then these 10 FNDC5-related genes were used to construct a prognostic model using Lasso-Cox proportional hazards regression, and the resulting best prognostic signature for predicting overall survival consisted of two FNDC5 co-express genes including RBPMS2 and CASQ2. Risk Score = (1.759 * RBPMS2 expression) + (−0.433 * CASQ2 expression) (Figures 8B,C). A risk score was assigned to each gastric cancer patient according to a risk scoring formula and divided into a low-risk score group and a high-risk score group. The distribution of risk scores, survival status, and mRNA expression levels of gastric cancer patients in the GSE62254 database is shown (Figure 8D). Kaplan-Meier curve analysis showed that the overall survival of the low-risk group was distinctly longer than that of the high-risk group (Figure 8E; Supplementary Table S10). To further assess the robustness of the risk score model, we stratified the gastric cancer patients based on gender, age, and tumor stage in GSE62254 database. Survival analyses indicated that gastric cancer patients with high-risk scores were found to have worse outcomes in different stratification (Figure 9; Supplementary Table S11). These results further confirmed the relatively good stratification ability of the prognostic model. To further verify the validity and stability of the prognostic model, TCGA and GSE84437 databases were used. Each patient was brought into the previous prognostic model to calculate the risk score. Patients were divided into high-risk and low-risk groups. Kaplan-Meier curve analysis showed that gastric cancer patients with low-risk scores had a better OS than those in the high-risk-score group in TCGA (Figure 10A; Supplementary Table S12) and GSE84437 databases (Figure 10B; Supplementary Table S13), indicating good accuracy.
Based on four variables including age, sex, tumor grade, and risk score, survival nomograms were created to accurately calculate 1-, 3-, and 5-year survival probabilities (Figure 11A; Supplementary Table S11). The overall C-index of the model is 0.75, which showed excellent calibration of the nomogram (Figure 11B). ROC curve was used to verify the diagnostic effect and AUC was found to be greater than 0.8 regardless of 1-year (AUC, 0.82; 95% CI, 0.76–0.88), 3-year (AUC, 0.81; 95% CI, 0.76–0.86) and 5-year (AUC, 0.80; 95% CI, 0.75–0.85) (Figure 10C), suggesting that this nomogram was reliable and robust. We believe that nomograms may have good accuracy for survival prediction in gastric cancer patients.
FNDC5 consists of 209 amino acids and can be divided into four domains including signal peptide (SP), fibronectin III domain (FND), hydrophobic domain (H), and C-terminal domain (C) (Waseem et al., 2022). FNDC5 is proteolytically hydrolyzed at amino acid position 30 and position 142 under the action of peroxisome proliferator-activated receptor gamma coactivator 1-α (PGC1-α) to produce irisin. FNDC5 protein range in mass from 20 to 32 kDa, and this difference is related to post-translational modifications (Boström et al., 2012). Numerous studies have shown that FNDC5 plays an important role in cancer diseases, including many types of malignancies, namely breast cancer, lung cancer, reproductive tract cancer, and bone cancer (Zhang et al., 2018; Cheng et al., 2020; Kim et al., 2021; Pinkowska et al., 2021; Cebulski et al., 2022; Zhu et al., 2022). In lung cancer, FNDC5 inhibits EMT and reduces the migration and invasion abilities of lung cancer cells by mediating the PI3K/AKT/Snail signaling pathway (Shao et al., 2017). FNDC5 was shown to inhibit EMT through the modulation of STAT3/Snail pathway in osteosarcoma (Kong et al., 2017). In pancreatic cancer, FNDC5 inhibited the growth of pancreatic cancer cells through the AMPK-mTOR pathway, and inhibited the migration and invasion of pancreatic cancer cells through the inhibition of EMT (Liu et al., 2018). Moreover, Zhang Z. et al. found that serum irisin is decreased in breast cancer patients with spinal metastasis compared to non-metastatic patients (Zhang et al., 2018). FNDC5/Irisin has been shown to aid the diagnosis for several cancers. Increased levels of FNDC5 are associated with a reduced risk of breast cancer, and FNDC5 can be used for differential diagnosis and prognosis of breast cancer (Zhang et al., 2018).The serum irisin level in renal cancer patients was significantly higher than that in healthy controls suggesting that serum irisin could be used as a biomarker for the diagnosis of renal cancer (Thomas et al., 2017). Serum irisin protein was increased in gastric cancer and increased FNDC5 expression may have a cachexia effect in cancer-induced mice (Us Altay et al., 2016). Serum irisin level was significantly lower in the bladder cancer patients compared to the control group, demonstrating that serum irisin levels can be used for the diagnosis of bladder cancer (Taken et al., 2022). The level of serum irisin in prostate cancer patients is considerably reduced and irisin may be used as a biomarker for prostate cancer patients (Aslan et al., 2020). There are many advantages for irisin in serum to be promising biomarkers. First, Serum samples are more easily accessible compared with tumor tissue samples. Second, sufficient irisin in serum makes it easy to be determined by simple and fast methods. Gastric cancer is the second leading cause of cancer death worldwide, mainly due to the poor prognosis, with an average 5-year survival rate of less than 20% and asymptomatic onset in the early stage (Chia and Tan, 2016). The expression analysis from gastric cancer-related GEO databases (GSE13195, GSE27342, GSE63089, and GSE65801) and TCGA database showed that the expression of FNDC5 was significantly downregulated in gastric cancer tissues compared with matched adjacent normal gastric tissues. Based on TCGA dataset, we found that the expression of FNDC5 was decreased in intestinal-type adenocarcinoma compared with diffuse-type adenocarcinoma. However, it confused us when Kaplan-Meier analysis showed that gastric cancer patients with high expression of FNDC5 had shorter overall survival than patients with low levels. Then, we investigated the impact of FNDC5 in proliferation, migration and invasiveness of gastric cancer cells. These results indicated that overexpressed FNDC5 had no effect on the proliferation of gastric cancer cells but inhibited the migration and invasion of gastric cancer cells in vitro. Numerous studies have shown its impact on the proliferation of cancer cells (Waseem et al., 2022). It was demonstrated that irisin can activate the AMPK pathway and downregulates the mTOR pathway, thereby suppressing pancreatic cancer cell growth (Liu et al., 2018). The number of malignant breast tumor cells decreased significantly upon exposure to irisin (Gannon et al., 2015). The increased level of irisin leads to decreased proliferation in a lung cancer cell by inhibiting the PI3K/Akt pathway (Shao et al., 2017). These studies suggested that FNDC5 may have different functions in different tumor cells. Tumor progression is a process of continuous proliferation, infiltration, and migration of cancer cells in the complex tumor microenvironment, which contains a variety of different immune cells. Changes in the status of tumor-reactive immune cells are associated with clinical changes in patient survival as well as immune tolerance response (Moreau et al., 2022). We investigated the association of FNDC5 with various immune cells in the tumor microenvironment and found a significant negative correlation between FNDC5 and CD4+ memory T cells, implying an increase in the number of CD4+ memory T cells when FNDC5 was lowly expressed. After induction of the immune response, antigen-reactive T cell persist in the memory pool and provide systemic immune surveillance in lymphoid organs. Increasing CD4+ memory cell density in gastric cancer has been reported to be a predictor of prolonged patient survival, while further studies have shown that CD4+ T cells are more infiltrated in gastrointestinal tumors compared to normal tissue (Ge et al., 2019). The results of immune cell clustering and clinicopathological characterization show that high levels of activated CD4+ memory T cells are significantly associated with a better prognosis (Ning et al., 2020). Therefore, the better prognosis of low FNDC5 expression can be attributed to the increased number of CD4+ memory activated T-cell infiltration in tumors, but the exact mechanism of the effect needs to be further explored. Meanwhile, we identify the regulatory transcription factors and methylation sites of FNDC5. We used multiple databases to carry out enrichment analysis of FNDC5-expressed genes and found that the expression level of KLF9 in gastric cancer patients was favorably correlated with FNDC5. The TCGA database was referenced simultaneously to determine whether FNDC5 levels were manipulated by modulating DNA methylation in gastric cancer. The results showed that FNDC5 expression was significantly negatively correlated with the level of methylation site (cg00668227). These results suggest that the low expression of FNDC5 in gastric cancer may be related to the expression of transcription factor KLF9 and the methylation of cg00668227 locus. Next, we analyzed the genes highly associated with FNDC5 in gastric cancer patients by WGCNA, and found that FNDC5-related genes were enriched in muscle system process, muscle contraction, and so on. Considering irisin, an adipocytokine secreted by FNDC5, is a hormone-like myokine produced in abundance by skeletal muscle in response to exercise, both in mice and humans (Us Altay et al., 2016). Using Cox univariate analysis and Lasso regression, we constructed a prognostic risk model for gastric cancer patients including 2 FNDC5-related genes. Meanwhile, we conducted external validation, and subgroup analysis to assess the reliability of the risk prognostic model. The AUC values of the ROC curves of 1-, 3-, and 5-year survival of the model were all greater than 0.8, which indicated that the signature composed of 2 FNDC5-related genes had good performance in predicting the prognosis of gastric cancer patients. There are some limitations in the current study which need to be acknowledged. First, there is a contradiction between in vitro results and clinical data. The change of tumor microenvironment could be one of the reasons for the contradiction between in vitro results and clinical data. However, it does not indicate immune cells are the major cause of the contradictory clinical results. There are many other possibilities besides it, for example, the efficiency of the T cells, the differentiation of immune cells, or even gastric microbial community. To answer the question, conditional knock mice will be required. For example, using APC mice that have specific knockout of FNDC5 in T lymphocytes or gastric epithelial cells. Second, the hypothesis that KLF9 might be the transcription factor of FNDC5 should been further confirm by immunofluorescence, chromatin immunoprecipitation and dual-luciferase assay.
Overall, we found that low levels of FNDC5 companying increasing CD4+ memory activated cells were a good prognostic factor in patients. Our experimental results suggest that FNDC5 inhibits the invasion and migration of gastric cancer cells. Meanwhile, we found that FNDC5 expression correlates with DNA methylation and the TF gene KLF9. Furthermore, the risk score model including FNDC5-related genes can be used to predict the prognosis of gastric cancer patients, leading to improved monitoring of the present patient population. |
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PMC9649551 | Clarissa Campo Dall’Orto,Rubens Pierry Ferreira Lopes,Mariana Torres Cancela,Ciria de Sales Padilha,Gilvan Vilella Pinto Filho,Marcos Raphael da Silva | Extensive right coronary artery thrombosis in a patient with COVID-19: A case report | 06-11-2022 | Acute coronary syndrome,Coronary angiography,COVID-19, Intravascular ultrasound,Thrombosis,Case report | BACKGROUND Occurring in approximately 30% of hospitalized patients, cardiovascular complications that take place during the course of coronavirus disease 2019 (COVID-19) have been shown to cause morbidity and mortality. This case is the first report of extensive right coronary artery (RCA) thrombosis that was evaluated by intracoronary imaging and intracoronary invasive physiology in a patient with COVID-19. CASE SUMMARY A 62-year-old woman presented with flu-like symptoms; ten days later, she presented with inferior ST-segment elevations, chest pain, dyspnea, nausea and vomiting. The patient was diagnosed with COVID-19 following a positive test result. Emergency angiography of the RCA and its branches indicated intraluminal filling defects, suggesting a thrombus. Intravascular ultrasound confirmed a subacute thrombus in the RCA, the right posterior descending branch and the right posterior ventricular (RPV) branch. There was also an acute thrombus in the RPV branch and atherosclerosis in the RCA. Dual antiplatelet/ anticoagulation therapy was administered. After 7 d, angiography revealed complete disappearance of the thrombi. Optical coherence tomography confirmed this with the exception of a small thrombus in the RPV branch and atherosclerotic plaque in the RCA. The atherosclerotic RCA was measured using the resting full-cycle ratio, indicating no impairment to coronary physiology. The patient was discharged on the 11th day of hospitalization and remained asymptomatic through the 6-mo follow-up. CONCLUSION This was the first report of RCA thrombosis in a patient with COVID-19. Dual antiplatelet/anticoagulation therapy was successful. | Extensive right coronary artery thrombosis in a patient with COVID-19: A case report
Occurring in approximately 30% of hospitalized patients, cardiovascular complications that take place during the course of coronavirus disease 2019 (COVID-19) have been shown to cause morbidity and mortality. This case is the first report of extensive right coronary artery (RCA) thrombosis that was evaluated by intracoronary imaging and intracoronary invasive physiology in a patient with COVID-19.
A 62-year-old woman presented with flu-like symptoms; ten days later, she presented with inferior ST-segment elevations, chest pain, dyspnea, nausea and vomiting. The patient was diagnosed with COVID-19 following a positive test result. Emergency angiography of the RCA and its branches indicated intraluminal filling defects, suggesting a thrombus. Intravascular ultrasound confirmed a subacute thrombus in the RCA, the right posterior descending branch and the right posterior ventricular (RPV) branch. There was also an acute thrombus in the RPV branch and atherosclerosis in the RCA. Dual antiplatelet/ anticoagulation therapy was administered. After 7 d, angiography revealed complete disappearance of the thrombi. Optical coherence tomography confirmed this with the exception of a small thrombus in the RPV branch and atherosclerotic plaque in the RCA. The atherosclerotic RCA was measured using the resting full-cycle ratio, indicating no impairment to coronary physiology. The patient was discharged on the 11th day of hospitalization and remained asymptomatic through the 6-mo follow-up.
This was the first report of RCA thrombosis in a patient with COVID-19. Dual antiplatelet/anticoagulation therapy was successful.
Core Tip: Cardiovascular complications occurring during the course of coronavirus disease 2019 (COVID-19) cause morbidity and mortality. We report the case of a 62-year-old woman with COVID-19 and ST-elevation myocardial infarction. Angiography of the right coronary artery suggested a thrombus, and findings were confirmed via intravascular ultrasound and optimal coherence tomography. Dual antiplatelet therapy and anticoagulation with enoxaparin therapy was administered for 7 d, followed by disappearance of the thrombi. Resting full-cycle ratio was performed without damage to coronary physiology. There is no consensus on the ideal management approach for acute coronary syndrome in this scenario; however, in this case the thrombi disappeared after dual antiplatelet and anticoagulation therapy.
Cardiovascular complications occurring in the course of coronavirus disease 2019 (COVID-19) cause morbidity and mortality affecting 30% of hospitalized patients[1-3]. One possible explanation for the damage caused to the myocardium by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) involves hypoxia following respiratory failure along with excessive inflammation, excess cytokine production, angiotensin-converting enzyme 2 receptor expression downregulation, platelet activation, coagulation cascade, endothelial cell injury, rupture of previously existing plaques [type 1 acute myocardial infarction (AMI)] and direct myocyte infiltration by the virus[4-6].
A 62-year-old woman with COVID-19 who presented with chest pain, dyspnea, nausea, and vomiting.
The patient initially presented with flu-like symptoms and was diagnosed with COVID-19 following a positive reading on a polymerase chain reaction (PCR) test. Ten days later, the patient presented with chest pain, dyspnea, nausea and vomiting.
The patient’s medical history included dyslipidemia and incipient atherosclerosis in the carotid and aortic territories, continuous use of nortriptyline for migraines and 9 years of tiboline [Libiam 1.25 mg once a day (Libbs-São Paulo–SP/BR)] use as menopausal hormone therapy. The patient had received three doses of a vaccine for SARS-CoV-2; two chimpanzee adenovirus vector vaccines (ChAdOx1 nCoV-19 AZD1222; Oxford/AstraZeneca/Fiocruz, Rio de Janeiro, Brazil) on April 12, 2021 and July 13, 2021, respectively, and one BNT162b2 mRNA COVID-19 vaccine (BioNTech/Pfizer, New York City, NY, United States) on December 13, 2021. Timeline is showed in Figure 1.
Upon physical examination, the vital signs were as follows: Body temperature 36.5 °C; blood pressure 122/78 mmHg; heart rate 82 beats per min; and respiratory rate 22 breaths per min. The patient’s clinical presentation was compatible with Killip-Kimball grade I classification.
Laboratory examination results were as follows: Blood cardiac biomarkers included a creatine kinase level of 6105 IU/L, a creatine kinase myocardial band fraction of 300 IU/L, a cardiac troponin I level of 25000 pg/mL, a C-reactive protein level of 75.8 mg/dL, a lactic dehydrogenase level of 1.510 U/L and a D-dimer level of 2.540 mcg/L FEU. In addition, transthoracic Doppler echocardiography revealed akinesis in the inferior mid-basal and apical infero-basal portions of the left ventricle.
Emergency coronary angiography revealed that the anterior descending coronary artery and its diagonal branches and circumflex artery and its marginal branches were free of obstructive atherosclerotic lesions (Figure 2). However, images of the right coronary artery (RCA) and its right posterior descending (RPD) and right posterior ventricular (RPV) branches indicated defects in intraluminal filling, suggesting a thrombus (Figure 3).
Electrocardiography showed sinus rhythm, inferior ST-segment elevations and reciprocal changes in the anterolateral leads (Figure 4). The patient was referred for emergency angiography.
We evaluated the RCA by intracoronary ultrasound. Intravascular ultrasound (IVUS) pullbacks were performed using a 40 MHz IVUS OPTICROSS catheter (Boston Scientific, Natick, MA, United States) at 0.5 mm/s. The images suggested a subacute, homogeneous, echolucent thrombus in a large extension of the RCA, RPD branch, and RPV branch. Additionally, they showed an acute thrombus with a bright aspect, clear outline and no signal attenuation in the RPV branch (Figure 5)[7]. We believe that the presence of thrombi on IVUS in the acute and subacute stages was due to the fact that at the time the patient was studied, the process had already been evolving for over 24 h and the thrombotic process (when it does not culminate with vessel occlusion) is a continuum of thrombus stages. We also identified mild to moderate atherosclerosis in the middle third of the RCA (Figure 5).
The patient was diagnosed with AMI with inferior ST-segment elevations, Killip grade I heart failure and COVID-19.
On angiography, the patient was pain-free and had thrombolysis in myocardial infarction grade 3 flow, despite extensive thrombotic burden in the RCA. Therefore, we did not perform primary angioplasty and, instead, opted for dual antiplatelet therapy with ticagrelor and aspirin and anticoagulation therapy with enoxaparin 1 mg/kg twice a day. Additionally, we administered the pharmacology recommended by current guidelines for patients with AMI with ST-segment elevation, including statins, a beta-blocker, an angiotensin-converting enzyme or angiotensin II receptor blocker and a mineralocorticoid receptor antagonist[8-10].
On angiography and IVUS, we were unable to identify any culprit lesions. Therefore, we used optical coherence tomography (OCT) for confirmation to look for signs of erosion or plaque rupture, which could have explained the condition and guided treatment; for example, if there was a need for mechanical passivation of the plate with a stent. However, we did not find plaque erosion or rupture on OCT. After 7 d, repeat coronary angiography showed complete disappearance of the thrombi located in the RCA and its branches (Figure 6). Therefore, we performed intravascular OCT for confirmation, using the ILUMIENTM OPISTM, OPTIS Integrated, and OPTIS Mobile systems (Abbott Vascular, Santa Clara, CA, United States) with a rapid exchange catheter (DragonflyTM DUO, DragonflyTM OPISTM, and Dragonfly OpStarTM Imaging Catheter; Abbott Vascular, Santa Clara, CA, United States) with a 75 mm/2.1 s (36 mm/s) pullback and 180 frames/s. The OCT exam confirmed that the thrombi had disappeared with the exception of a small residual thrombus in the RPV branch (Figure 7). It also identified a plaque in the middle third of the RCA. Severity of coronary stenosis was measured using the resting full-cycle ratio, a non-hyperemic index based on unbiased detection of the lowest existing relationship between distal coronary pressure and aortic pressure (Pd/Pa), independent of the electrocardiogram, landmark identifications and time within the heart cycle. We evaluated this plaque as 40% mild in the RCA, causing 40% arterial lumen obstruction. We used PressureWire X (Abbott Vascular, Santa Clara, CA, United States), which reported a value of 0.99, indicating the absence of impairment to coronary physiology. The patient recovered without further event and was discharged on the 11th day of hospitalization. After showing good tolerance to medications with no adverse effects, she was prescribed ticagrelor, aspirin, statins, beta-blockers and angiotensin-converting enzyme inhibitors. The patient has been under follow-up until the present day by our team, with a 6 mo follow-up completed thus far, and she remains asymptomatic without any other clinical conditions since then.
In the present case, the diagnosis of AMI with ST-segment elevation was confirmed by clinical findings, electrocardiogram, laboratory blood tests and coronary angiography. The latter showed extensive failure of intraluminal filling suggesting a thrombus, which was then confirmed by IVUS and OCT findings. The plaque in the middle third of the RCA, which we believe was not responsible for the event, did not cause any hemodynamic repercussions, a fact confirmed by the assessment of invasive physiology. The diagnosis that proved to be feasible in this case was AMI with ST-segment elevation type 2; that is, AMI not related to the instability of atherosclerotic plaque in the coronary artery. Faced with this diagnosis, it is necessary to reflect on the possible causes to guide us towards the most appropriate treatment. A possible cause suggested in this patient could be hormone replacement therapy for menopause. The patient was chronically using tibolone, which is a synthetic steroid whose metabolites have estrogenic, androgenic and progestogenic properties. In the Long-term Intervention on Fracture with Tibolone study, tibolone in postmenopausal women was linked to an excessive risk of stroke in women receiving tibolone compared with placebo. However, there were no significant differences in the risk of coronary heart disease or venous thromboembolism between the two groups[11]. The hypothesis of a post-vaccination reaction to the vaccine for COVID-19 could also be raised. However, we did not emphasize the possibility that the patient's clinical condition was due to an adverse effect of the vaccine because the event occurred more than 30 d after the third dose; additionally, she had a positive PCR test result indicating active infection. In view of the abovementioned findings, with a positive PCR test result for SARS-CoV-2, in addition to inflammatory markers, with very high serum (lactic dehydrogenase, C-reactive protein and D-dimer) levels corroborating active infection, the most plausible cause we found for the etiology of AMI with ST-segment elevation type 2 in this case was COVID-19. The common occurrence of extra-respiratory involvement in SARS-CoV-2 infections has become more evident over time. AMI with ST-segment elevation is observed with a pattern on angiography, and extensive thrombosis can affect one or more coronary arteries and different vascular territories simultaneously, not caused by rupture of atherosclerotic plaques. These occurrences present new challenges to treating and managing this viral infection[10,12]. The increased incidence of stent thrombosis may be associated with these phenomena, and severe inflammation with consequent hypercoagulation is another primary pathology associated with SARS-CoV-2[13]. There is no specific finding that confirms the relationship between COVID-19 infection and AMI other than the known extensive thrombotic burden, which is not a specific finding. The high thrombotic load in patients with AMI and COVID is known. In addition to the findings in other studies in the literature[14,15], there are also other important reports addressing this issue, such as a statement from the American College of Cardiology in which the authors concluded that patients with ST-segment elevation myocardial infarction and concurrent COVID-19 experienced a higher thrombotic burden than those without concurrent COVID-19[13]. AMI without plaque rupture (type 2) can indeed occur in addition to COVID-19; however, we did not identify any other causes of type 2 AMI, such as coronary dissection, vasospasm, emboli, microvascular dysfunction or increases in demand with or without underlying coronary artery disease. The difference between this case and those previously published is that we were able to document the absence of plaque rupture or erosion in a patient with coronary artery disease and to demonstrate that the plaque had no hemokinetic repercussions, shown via invasive physiology. We believe that our case corroborates a body of evidence that has been building toward an understanding of COVID and AMI. While the importance of differentiating between type I and type II AMI and myocarditis in patients with COVID-19 presenting with acute coronary syndrome (ACS) is established, there is no consensus on the ideal management approach for ACS in this scenario. In patients with known or suspected COVID-19, treatment of ST-segment elevation myocardial infarction is similar to that for patients without COVID-19, using aspirin, nitrate, beta-blockers, anticoagulation, antiplatelet aggregation with a P2Y12 agent, statins, and reperfusion therapy with fibrinolytics or primary angioplasty. Percutaneous coronary intervention, aspiration and antiplatelet thrombectomy are options, with the latter being the most generally agreed upon for treating these patients[16]. However, in this study, the thrombi disappeared after dual antiplatelet therapy, anticoagulation therapy and traditional post-myocardial infarction pharmaceutical interventions were administered.
This was the first report of extensive RCA thrombosis in a patient with COVID-19 evaluated by intracoronary imaging and intracoronary invasive physiology. |
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PMC9649558 | Ya-Ya Qin,Song Feng,Xiao-Dong Zhang,Bin Peng | Screening of traditional Chinese medicine monomers as ribonucleotide reductase M2 inhibitors for tumor treatment | 06-11-2022 | Tumor,Ribonucleotide reductase M2 inhibitor,Traditional Chinese medicine,Monomer,Molecular docking,Literature mining | BACKGROUND Ribonucleotide reductase (RR) is a key enzyme in tumor proliferation, especially its subunit-RRM2. Although there are multiple therapeutics for tumors, they all have certain limitations. Given their advantages, traditional Chinese medicine (TCM) monomers have become an important source of anti-tumor drugs. Therefore, screening and analysis of TCM monomers with RRM2 inhibition can provide a reference for further anti-tumor drug development. AIM To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2. METHODS The Gene Expression Profiling Interactive Analysis database was used to analyze the level of RRM2 gene expression in normal and tumor tissues as well as RRM2's effect on the overall survival rate of tumor patients. TCM monomers that potentially act on RRM2 were screened via literature mining. Using AutoDock software, the screened monomers were docked with the RRM2 protein. RESULTS The expression of RRM2 mRNA in multiple tumor tissues was significantly higher than that in normal tissues, and it was negatively correlated with the overall survival rate of patients with the majority of tumor types. Through literature mining, we discovered that berberine, ursolic acid, gambogic acid, cinobufagin, quercetin, daphnetin, and osalmide have inhibitory effects on RRM2. The results of molecular docking identified that the above TCM monomers have a strong binding capacity with RRM2 protein, which mainly interacted through hydrogen bonds and hydrophobic force. The main binding sites were Arg330, Tyr323, Ser263, and Met350. CONCLUSION RRM2 is an important tumor therapeutic target. The TCM monomers screened have a good binding capacity with the RRM2 protein. | Screening of traditional Chinese medicine monomers as ribonucleotide reductase M2 inhibitors for tumor treatment
Ribonucleotide reductase (RR) is a key enzyme in tumor proliferation, especially its subunit-RRM2. Although there are multiple therapeutics for tumors, they all have certain limitations. Given their advantages, traditional Chinese medicine (TCM) monomers have become an important source of anti-tumor drugs. Therefore, screening and analysis of TCM monomers with RRM2 inhibition can provide a reference for further anti-tumor drug development.
To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2.
The Gene Expression Profiling Interactive Analysis database was used to analyze the level of RRM2 gene expression in normal and tumor tissues as well as RRM2's effect on the overall survival rate of tumor patients. TCM monomers that potentially act on RRM2 were screened via literature mining. Using AutoDock software, the screened monomers were docked with the RRM2 protein.
The expression of RRM2 mRNA in multiple tumor tissues was significantly higher than that in normal tissues, and it was negatively correlated with the overall survival rate of patients with the majority of tumor types. Through literature mining, we discovered that berberine, ursolic acid, gambogic acid, cinobufagin, quercetin, daphnetin, and osalmide have inhibitory effects on RRM2. The results of molecular docking identified that the above TCM monomers have a strong binding capacity with RRM2 protein, which mainly interacted through hydrogen bonds and hydrophobic force. The main binding sites were Arg330, Tyr323, Ser263, and Met350.
RRM2 is an important tumor therapeutic target. The TCM monomers screened have a good binding capacity with the RRM2 protein.
Core Tip: Tumors seriously threaten human life and health. In our work, we found that ribonucleotide reductase M2 (RRM2) is highly expressed in most tumor tissues, and is related to poor prognosis. Seven traditional Chinese medicine monomers with good binding ability to RRM2 were identified, and their binding sites were summarized and analyzed. Those will provide ideas for the development of anti-tumor drugs with RRM2 inhibition in the future.
The tumor is a major contributor to endangering human health. In terms of disability-adjusted life years, it is only second to cardiovascular disease. The World Health Organization predicts that there will be a global increase in new tumor cases of more than 50%, from 18 million in 2018 to 27 million in 2040[1]. In addition to conventional surgical resection, drug adjuvant therapy still occupies a considerable part in the treatment of tumors. Although numerous chemotherapy medications have been developed, the majority of them have more or less side effects and some are rather pricey. Since natural chemicals are safer, cheaper, and more effective than synthetic ones, there has been an increasing interest in finding medications to prevent and cure tumors from natural compounds[2]. Ribonucleotide reductase (RR), the only multi-subunit enzyme existing in all biological cells that can catalyze the reduction of ribonucleotides to corresponding deoxyribonucleotides, is the rate-limiting enzyme of DNA synthesis. By regulating and balancing the content of different deoxyribonucleic acids (dNTPs) in the cell cycle, RR is mainly involved in DNA replication and repair, which is crucial for controlling cell proliferation and preserving genomic stability[3,4]. Human RR is composed of two large subunits M1 and two small subunits M2 (RRM2)[5]. Since RRM2 has the ability to regulate and catalyze substrates, the enzymatic activity of RR is primarily controlled by RRM2[6]. The tumor is a highly invasive disease, the tumor cell proliferation requires the participation of a large number of dNTPs[3]. Studies have found that most tumor cells express more RR than normal cells do. The overexpression of RRM2 is related to tumor malignancy, invasion, metastasis, drug resistance, and autophagy[7-9]. Inhibiting or reducing the expression of RRM2 may improve tumor patients' disease progression and prognosis, and lengthen their survival[10]. According to the target and mechanism of action, RRM2 inhibitors are roughly divided into gene expression regulators and protein inactivators. The gene expression regulators include R2 antisense inhibitors and siRNA inhibitors, whereas free radical scavengers, iron chelators, and iron mimics fall under the category of protein inactivators[11]. Due to a wide range of pharmacological properties, some traditional Chinese medicine (TCM) monomers have also been used as RRM2 inhibitors for research. Through literature mining, we found multiple TCM monomers that have inhibitory effects against RRM2 in tumors. However, there are few studies on their interaction sites. This paper aims to elucidate the relationship between RRM2 and malignant tumors and the prognosis of tumor patients, and then to screen out potential anti-tumor TCM monomers with good binding ability to RRM2. Through the analysis of their main binding sites, some thoughts for the development of new anti-tumor drugs with RRM2 inhibition are provided.
Through the Expression Profiling Interactive Analysis database (GEPIA) (http://gepia.cancer-pku.cn), we analyzed and obtained the mRNA level of the RRM2 gene in normal tissues and tumor tissues, as well as its effect on the overall survival rate of tumor patients. All tumor abbreviations were listed in Table 1.
PubMed database (http://www.ncbi.nlm.nih.gov/pubmed/) and the China National Knowledge Infrastructure database (CNKI, https://www.cnki.net) were used to retrieve and download the articles related to TCM monomers acting on RRM2 targets. Subsequently, the application of TCM monomers in tumors was summarized and analyzed one by one.
According to the small molecule CAS number from the PubChem database, we downloaded the 3D structure of TCM monomers with small molecule SDF format, then imported them into chembio3d ultra 14.0 for energy minimization respectively. The three-dimensional structure of the RRM2 protein was obtained from the PDB (http://www.rcsb.org). AutoDock vina1.1.2 was used to complete the molecular docking between RRM2 protein and TCM monomers. The relevant parameters of RRM2 protein were set to center_ x = -4.715, center_ y = -3.6and 33, center_ Z = 15.668, the size of the grid box was set to 50 × 50 × 50 (the spacing of each grid point is 0.375 Å), and the other parameters were the default settings. Finally, analyzation of the interaction mode of the docking results was performed by Pymol 2.3.0 and ligplot V2.1.
In the GEPIA database, we found 31 types of tumor tissues with RRM2 differential expression and their paired normal samples. The findings revealed that, except for LAML, the mRNA expression of RRM2 in 30 types of tumor tissues was considerably higher than that in normal tissues (Figure 1). The investigation of the overall survival rate of 33 types of tumor patients revealed that the RRM2 gene expression of 23 types was negatively correlated with the overall survival rate (Figure 2), while it was positively correlated in 10 types (Figure 3). Among them, the reason for the few positive correlation results observed may be other issues exist that affect the overall survival rate.
Through the literature search in the PubMed database and CNKI database, we found seven TCM monomers that may be used as RRM2 inhibitors in tumors (Table 2). They all will be described in subsequent sections separately. Berberine: Berberine is a quaternary ammonium alkaloid extracted from medicinal plants such as Coptis chinensis, Berberis aristata, Hydrastis canadensis, and Coptis japonica[12]. Berberine and its derivatives have been identified to have pharmacological properties against multiple diseases, including digestive diseases, metabolic diseases, cardiovascular diseases, and neurological diseases[13]. Recent studies have discovered that berberine can also inhibit the invasion and metastasis of many kinds of tumors, such as oral squamous cell carcinoma, lung cancer, liver cancer, glioblastoma, breast cancer, and so on[12]. Through binding to P53, NF-κB, matrix metalloproteinase (MMP), Bcl-2, and receptors e.g. estrogen receptor, berberine could promote the cell cycle arrest and death of tumor cell lines, and induce the expression of pro-apoptotic factors[14-16]. In addition, some other information indicates that RRM2 may also be a potential target of berberine in the treatment of tumors. A bioinformatical analysis showed that RRM2 is the hub-gene for berberine to act on breast cancer[17]. After berberine treatment in vitro, the expression level of the RRM2 gene and protein in non-small cell lung cancer cell lines (A549, H1299, and H1975) was significantly reduced[18]. Ursolic acid: Ursolic acid, a natural pentacyclic triterpene compound, is widely found in fruits and vegetables. It has been demonstrated to have multiple biological functions, including anti-inflammatory, antioxidant, anti-apoptotic, and anti-allergic activities[19]. At present, ursolic acid has also been reported to have anti-tumor pharmacological properties, acting as an active therapeutic agent for several malignancies such as breast cancer, colon cancer, pancreatic cancer, and liver cancer[20]. By regulating a variety of enzymes (ATPase, GST, COX-2), transcription factors (AP-1, NF-κB, STAT-3), growth factors (EGF, PDGF, HGF), receptors (EGFR, ER-a, HER-2, EAR), as well as inflammatory factors (MAP-K, PKA, PTK, IL-6, IL-1, IL-8, MIP), it could inhibit tumor proliferation, metastasis, and angiogenesis[21,22]. Recently, a network pharmacology analysis detected that RRM2 maybe also the potential target of ursolic acid in tumors[23], but still needs to be further confirmed in clinical and experimental studies. Gambogic acid: Gambogic acid, a kind of caged xanthone extracted from dry resin secreted by Garcinia hanburyi tree, has the functions of promoting blood circulation, anti-tumor, detoxification, and hemostasis[24]. According to numerous studies, multiple carcinomas, including breast cancer, lung cancer, liver cancer, colon cancer, and pancreatic cancer, were inhibited by gambogic acid[25]. Through the combination of several major targets such as VEGF, Bcl-2, MDM2, MMP-9, MMP-2, EGFR, and P53, gambogic acid promotes tumor cell apoptosis, autophagy, and arrests cell cycle, thereby inhibiting tumor invasion, metastasis, and angiogenesis[26]. An investigation in pancreatic cancer demonstrated that following treatment with gambogic acid in vivo and in vitro, the expression of RRM2 protein and mRNA was significantly decreased[6], suggesting that RRM2 may also be the target of gambogic acid in tumor treatment. Cinobufagin: Bufadienolide cinobufagin, which is extracted from the Asiatic toad Bufo gargarizans, has analgesic, detoxifying, and detumescent properties[27]. Some investigations conducted recently have revealed that it has potent anti-tumor effects as well. In non-small cell lung cancer, cinobufagin could suppress proliferation, migration, and invasion of cancer cells by inhibiting the expression of G9a[27]. By interfering with the cell cycle, cinobufagin also inhibits the survival of cancer cells and promotes apoptosis[28]. Moreover, many other anti-tumor pathways are involved, such as the Notch signaling pathway[29], AURKA/mTOR/eIF4E axis[30], c-Myc pathway[31], and ROS/JNK/p38 signaling pathway[32]. After cinobufagin treatment, the expression of RRM2 in endometrial carcinoma (Ishikawa cell line) decreased significantly at gene and protein levels, inhibiting cell proliferation and reducing invasiveness[33]. In vivo studies likewise produced the same results[34]. Thus, cinobufagin is expected to be an RRM2 inhibitor with multiple anti-tumor effects. Quercetin: Quercetin, a flavonol compound widely existing in many plants, has been reported to have multiple pharmacological effects on preventing osteoporosis, cardiovascular disease, aging, and tumors[35]. In terms of anti-tumor properties, the main mechanisms are to regulate the viability, apoptosis, and autophagy of tumor cells through PI3K/Akt/mTOR, Wnt/β-Catenin, and MAPK/ERK1/2 pathways[36], and then exhibits inhibitory activities against a variety of tumors, such as colon cancer (Caco-2 cell line), lung cancer (NCI-H446, A549 cell line), and gastric cancer (MGC-803, SGC-7901 cell line)[37]. A comprehensive analysis based on differential genes and drug targets found that quercetin was closely related to RRM2[23]. After treatment with quercetin, the activity of Leishmania donovani was inhibited by targeting RR[38]. Therefore, we speculate that the anti-tumor effect of quercetin may be partially attributed to the inhibition of RRM2. Daphnetin: Daphnetin is a coumarin derivative with rich pharmacological activity, extracted from Daphne odora. It is often used in the treatment and research of neurological diseases, malaria, parasites, and arthritis[39]. Currently, some studies suggest that daphnetin also has an inhibitory effect on tumor growth, with the mechanisms of action including downregulating Cyclin D1 expression in breast cancer (MCF-7 cell line), inducing G2/M and S phase arrest in hepatoma cells (SMMC-7721 cell line), suppressing the Akt/NF-κB signaling pathway in lung adenocarcinoma (A549 cell line), and inhibiting the AMPK/Akt/mTOR pathway in ovarian cancer (A2780 cell line)[40]. In addition, a study on malaria found that daphnetin could also inhibit the expression and activity of RR by binding to the iron-containing group (RRM2)[41]. However, as an indispensable key enzyme for tumor growth, whether daphnetin can inhibit RRM2 in human tumor cells needs further research to confirm. Osalmid: In clinical practice, osalmid has been used to treat biliary tract inflammation, cholecystitis, and post cholecystectomy syndrome. By decreasing RRM2 activity and activating P53, it was found that osalmid also inhibits the progression of human hepatocellular carcinoma[42]. The expression of RRM2 in esophageal cancer was similarly inhibited by osalmid. In addition to promoting apoptosis, blocking cell cycle and DNA damage, and inhibiting the proliferation and migration of tumor cells, the radiosensitivity was enhanced[43]. Due to their powerful anti-tumor activities, osalmid and its derivatives have been used in numerous investigations as new RRM2 inhibitors[44-46].
Molecular docking presented interaction between the aforementioned seven TCM monomers and RRM2 protein, and the results showed that they all had a strong binding capacity. The specific results are described in detail in the following sections (Table 3). Berberine bonded to the RRM2 protein with a binding energy of -7.3 kcal/mol, mostly made up of one hydrogen bond and eight hydrophobic bonds (Figure 4A). The hydrogen bond was mainly localized at Ser263(A) of RRM2, with a length of 3.73 Å, and the hydrophobic sites were situated at Glu260(A), Arg264(A), Tyr323(A), Arg330(A), Gly233(A), Val327(A), Ser237(A) and Gly267(A) of RRM2. Ursolic acid and RRM2 protein had binding energy of -8.6 kcal/mol (Figure 4B). There was just hydrophobic force between them. Arg264(A), Gly267(A), Cys270(A), Gly233(A), Arg330(A), Val327(A), Ser263(A), Phe244(A), Phe349(A), and Met350(A) of RRM2 were the primary hydrophobic action sites. Gambogic acid and RRM2 protein bonded with a -8.6 kcal/mol binding energy (Figure 4C). Their interaction was achieved through the formation of hydrogen bonds and hydrophobic forces. The hydrogen bonds in RRM2 were at positions Arg330(A) and Tyr323(A), with lengths of 3.24 Å and 2.74 Å, respectively. The hydrophobic action sites in RRM2 were found at Gly267(A), Leu268(A), Ser100(A), Lys96(A), Arg264(A), Met350(A), and Ser263(A). The binding energy between cinobufagin and RRM2 protein was -7.6 kcal/mol (Figure 4D). They interacted with each other through the formation of hydrogen bonds and hydrophobic force. The hydrogen bond lengths were 3.88 Å and 2.82 Å, respectively, which were located at Asp271(A) and Arg330(A) of RRM2. The hydrophobic effect was generated on Glu334(A), Leu331(A), Cys270(A), Gly267(A), Glu266(A), Ser263(A), Phe244(A), and Met350(A) of RRM2 and cinobufagin. Quercetin and RRM2 protein had binding energy of -7.4 kcal/mol (Figure 5A). Quercetin mainly forms three hydrogen bonds and nine hydrophobic forces with RRM2. The hydrogen bond lengths were 3.16 Å, 3.05 Å, and 2.77 Å, respectively, which were mainly formed in Arg330(A) and Tyr323(A) of RRM2. The hydrophobic sites were found in the following positions in RRM2: Met350(A), Arg264(A), Glu260(A), Phe244(A), Ser263(A), Glu232(A), Gly233(A), Phe240(A) and Val327(A). The binding energy between daphnetin and RRM2 protein was -6.7 kcal/mol (Figure 5B). Tyr323 (A) and Arg330 (A) of RRM2 made four hydrogen bonds with daphnetin, whereas Val327(A), Ser263(A), Phe240(A), Met350(A), Gly267(A), Gly233(A), and Gys270(A) of RRM2 formed seven hydrophobic forces with daphnetin. Whose hydrogen bonds had lengths of 2.87 Å, 3.19 Å, 2.80 Å, and 3.24 Å, respectively. Osalmide and RRM2 protein had a -6.8 kcal/mol binding energy (Figure 5C). They only interacted hydrophobically, and their hydrophobic interaction sites were found in Phe244(A), Arg264(A), Tyr323(A), Phe240(A), Ser237(A), Met350(A), Gly233(A), and Ser263(A) of RRM2.
Nowadays, the acknowledged tumor treatment strategies include surgical resection, chemotherapy, and radiotherapy, as well as biotherapy, immunotherapy, and targeted therapy developed in recent decades. Due to some limitations and defects, monotherapy does not seem to be able to fully achieve the ideal effect[47]. Therefore, combination therapy and adjuvant therapy are often required. As a natural medicine, some active ingredients of TCM have been proven to have excellent anti-tumor activity. TCM can not only inhibit the proliferation of tumor cells through multiple targets, improve the cancer microenvironment, and strengthen the function of anti-tumor immunity, but also enhance the efficacy of chemotherapy, radiotherapy, targeted therapy, and immunotherapy, and reduce the damage caused by these therapies, to prolong the survival time of tumor patients and improve the quality of life to a certain extent[48]. Because of their advantages of broad spectrum, high efficacy, low toxicity, and strong specificity, TCMs and extracts are widely used as adjuvant therapy for tumors in clinics[49]. Paclitaxel, vinblastine, and hydroxycamptothecin are three examples of commonly used clinical chemotherapeutic medicines[50-52]. Compared with traditional synthetic medications, the anti-tumor mechanisms of TCMs are more complex and extensive. They involve multiple signaling pathways and biological targets related to cancer. Despite the long history of TCM study, part of the mechanism of action and molecular targets are not completely clear[53]. TCM monomers, as the active compound of TCM, including their functions still need to be further explored and studied. Deoxyribonucleotide triphosphate (dNTP), the building block for DNA synthesis, is in high demand in tumors. As the key enzyme of DNA synthesis, RR not only participates in DNA synthesis and repair via producing dNTP but is also involved in cell cycle regulation[5,54]. RRM2 is an important subunit of RR, which also play a regulatory role in multiple biological processes, including the survival, proliferation, apoptosis, and chemoresistance of various cancer cells[7]. According to GEPIA database analysis, we found that RRM2 is highly expressed in more than 30 types of tumor tissues, and negatively correlated with the overall survival rate of patients with the majority of tumor types. A study in prostate cancer has found that RRM2 is a driver of aggressive subtypes, and elevated RRM2 contributes to tumor cell immune escape[55]. The overexpression of RRM2 in breast cancer cells activated NF-κB and MMP-9 to alter the tumor microenvironment, thereby enhancing the migratory abilities of tumor cells[56]. Increased RRM2 expression is also associated with tamoxifen resistance, inhibition of RRM2 not only reduced migration and invasion characteristics of cancer cells in vitro but also reversed tamoxifen resistance of breast cancer cells, which may be mediated by NF-κB, HIF-1α, and MAPK/JNK pathways[57]. GW8510 acts as an RRM2 inhibitor, improving acquired tamoxifen resistance in breast cancer cells by autophagy induction, a similar effect was seen in lung squamous cell carcinoma cells[58,59]. Besides, knockdown of RRM2 enhanced the drug sensitivity of chronic myeloid leukemia to imatinib treatment by activating the Bcl-2/caspase apoptosis pathway and inhibiting the Akt cell signaling pathway[60]. These results indicate that RRM2 is an independent predictor of poor prognosis in a variety of tumors and could be a good target for tumor therapy. RRM2 has two important drug binding targets: tyrosine free radical and divalent iron radical, most of the currently developed RRM2 inhibitors act on these two targets[61]. Hydroxyurea is a common anti-tumor chemotherapy drug as well as an RRM2 inhibitor, which can inhibit RRM2 activity by scavenging tyrosine free radicals, and then inhibit DNA synthesis[62]. Gallium, an iron analog, has chemical characteristics similar to iron. Though interacting with iron-binding protein, gallium interferes with cellular iron uptake and damages iron homeostasis in cells, resulting in the inhibition of RRM2 function[63]. Triapine also inhibits RRM2 activity by forming iron chelates with iron groups[64]. However, some RRM2 inhibitors may lead to different degrees of side effects such as blood and lymphatic system metabolic disorders, liver and kidney dysfunction, gastrointestinal reactions, and reproductive toxicity[65,66]. Therefore, it is urgent to develop or find new RRM2 inhibitors that are safer, more effective, and more specific. Through literature mining, we retrieved seven TCM monomers with an inhibitory effect on RRM2 in tumors. They all have good binding capacities with RRM2, according to molecular docking analysis, with binding energies ranging from -8.6 to -6.8 kcal/mol. The hydrogen bonds and/or hydrophobic forces are the main contributors to these binding energies, their major active sites are Arg330, Tyr323, Ser263, and Met350 of RRM2. Among them, Arg330 is the site where the most hydrogen bonds are formed between TCM monomer and RRM2, followed by Tyr323. The locations with the highest frequency of hydrophobic action are Ser263 and Met350, the next two are Gly267 and Arg264. These findings imply that Arg330, Tyr323, Ser263, and Met350 may be important binding sites of RRM2 inhibitors with RRM2, which will provide some thoughts for the development of new anti-tumor drugs with RRM2 inhibition based on these sites.
RRM2 is a crucial tumor therapeutic target. It is highly expressed in almost all tumors and negatively correlated with the overall survival rate of patients with the majority of tumor types. The seven screened TCM monomers have a good binding capacity to RRM2, and their binding sites are mainly concentrated in Arg330, Tyr323, Ser263, and Met350 of RRM2. This will provide theoretical support and a point for the development of anti-tumor medications with RRM2 inhibition based on these binding sites. Meanwhile, natural drugs with abundant structures are an important source for the development of anti-tumor drugs, it is anticipated that more effective RRM2 inhibitors will be developed through in-depth research.
The tumor is a major contributor to endangering human health, traditional Chinese medicine (TCM) monomer is an important source of anti-tumor drugs. Ribonucleotide reductase (RR) is a key enzyme in tumor proliferation, especially its subunit-RRM2. Screening and analysis of TCM monomers with RRM2 inhibition can provide a reference for further anti-tumor drug development.
To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2, and provide some thoughts for the development of anti-tumor drugs with RRM2 inhibition in the future.
To clarify the relationship between RRM2 and malignant tumors. To clarify the relationship between RRM2 and the prognosis of tumor patients. To screen and analyze potential anti-tumor TCM monomers with a good binding capacity to RRM2, and provide some thoughts for the development of anti-tumor drugs with RRM2 inhibition in the future.
The GEPIA database was used to analyze the level of RRM2 gene expression in normal and tumor tissues as well as RRM2's effect on the overall survival rate of tumor patients. TCM monomers that potentially act on RRM2 were screened via literature mining. Using AutoDock software, the screened monomers were docked with the RRM2 protein.
The expression of RRM2 mRNA in multiple tumor tissues was significantly higher than that in normal tissues, and RRM2 was negatively correlated with the overall survival rate of patients with the majority of tumor types. Berberine, ursolic acid, gambogic acid, cinobufagin, quercetin, daphnetin, and osalmide have inhibitory effects on RRM2. The screened TCM monomers had a strong binding capacity with RRM2 protein.
RRM2 is an important tumor therapeutic target. The screened TCM monomers have a good binding ability with the RRM2.
Their main binding sites could provide new thoughts for the development of anti-tumor drugs with RRM2 inhibition. |
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PMC9649571 | Fabrício Freire de Melo,Hanna Santos Marques,Fabian Fellipe Bueno Lemos,Marcel Silva Luz,Samuel Luca Rocha Pinheiro,Lorena Sousa de Carvalho,Cláudio Lima Souza,Márcio Vasconcelos Oliveira | Role of nickel-regulated small RNA in modulation of Helicobacter pylori virulence factors | 06-11-2022 | Helicobacter pylori,Small regulatory RNAs,Nickel-regulated small RNA,Virulence factors,Cytotoxin associated antigen A,Gastric cancer | Helicobacter pylori (H. pylori) is a Gram-negative bacterium that infects about half of the world's population. H. pylori infection prevails by several mechanisms of adaptation of the bacteria and by its virulence factors including the cytotoxin associated antigen A (CagA). CagA is an oncoprotein that is the protagonist of gastric carcinogenesis associated with prolonged H. pylori infection. In this sense, small regulatory RNAs (sRNAs) are important macromolecules capable of inhibiting and activating gene expression. This function allows sRNAs to act in adjusting to unstable environmental conditions and in responding to cellular stresses in bacterial infections. Recent discoveries have shown that nickel-regulated small RNA (NikS) is a post-transcriptional regulator of virulence properties of H. pylori, including the oncoprotein CagA. Notably, high concentrations of nickel cause the reduction of NikS expression and consequently this increases the levels of CagA. In addition, NikS expression appears to be lower in clinical isolates from patients with gastric cancer when compared to patients without. With that in mind, this minireview approaches, in an accessible way, the most important and current aspects about the role of NikS in the control of virulence factors of H. pylori and the potential clinical repercussions of this modulation. | Role of nickel-regulated small RNA in modulation of Helicobacter pylori virulence factors
Helicobacter pylori (H. pylori) is a Gram-negative bacterium that infects about half of the world's population. H. pylori infection prevails by several mechanisms of adaptation of the bacteria and by its virulence factors including the cytotoxin associated antigen A (CagA). CagA is an oncoprotein that is the protagonist of gastric carcinogenesis associated with prolonged H. pylori infection. In this sense, small regulatory RNAs (sRNAs) are important macromolecules capable of inhibiting and activating gene expression. This function allows sRNAs to act in adjusting to unstable environmental conditions and in responding to cellular stresses in bacterial infections. Recent discoveries have shown that nickel-regulated small RNA (NikS) is a post-transcriptional regulator of virulence properties of H. pylori, including the oncoprotein CagA. Notably, high concentrations of nickel cause the reduction of NikS expression and consequently this increases the levels of CagA. In addition, NikS expression appears to be lower in clinical isolates from patients with gastric cancer when compared to patients without. With that in mind, this minireview approaches, in an accessible way, the most important and current aspects about the role of NikS in the control of virulence factors of H. pylori and the potential clinical repercussions of this modulation.
Core Tip: This paper aims to review current information about the role of nickel-regulated small RNA (NikS) in the modulation of main Helicobacter pylori virulence factors, specially cytotoxin associated antigen A (CagA), which is crucial to gastric cancer development. Here we explore what is the most important about the epigenetic processes involved in the interaction between nickel levels, NikS, and CagA and their potential clinical repercussions.
Helicobacter pylori (H. pylori) is a microaerophilic, Gram-negative, helical-shaped bacterium that inhabits the gastric environment of 60.3% of the world’s population[1,2]. The infection is associated with the development of chronic gastritis, gastric and duodenal peptic ulcer, gastric adenocarcinoma, and gastric mucosa-associated lymphoid tissue (MALT) lymphoma[3]. In order to achieve a successful colonization, H. pylori must take advantage of some pathogenicity mechanisms, such as motility, adherence, manipulation of the gastric microenvironment, and virulence factors, of which we highlight cytotoxin associated antigen A (CagA), vacuolating cytotoxin A (VacA), and outer membrane proteins (OMPs). In this sense, the classification of this bacterium as a class I carcinogen is mostly due to the pro-oncogenic role of these virulence factors, especially CagA[4]. This oncoprotein is capable of inducing genetic, epigenetic, and morphological changes in gastric cells, including alterations of cell polarity and cytoskeleton, leading to "hummingbird" phenotype and promotion of genomic instability, which favor carcinogenesis[5-8]. In this regard, it has been recently discovered that nickel-regulated small RNA (NikS) plays a key role in gene expression during H. pylori infection, given that, through base pairing, it is able to repress CagA and VacA at the post-transcriptional level[9,10]. Notably, the expression of this sRNA is modulated by the nickel-responsive transcriptional regulator (NikR), consequently rendering H. pylori virulence factor expression dependent on nickel levels[10]. Therefore, considering that these virulence factors are associated with the onset of a carcinogenic process, the possible correlation between NikS expression and the development of gastric diseases secondary to H. pylori infection, including gastric carcinoma and MALT lymphoma, is indisputable. The present paper is a minireview that aims to gather, through an accessible perspective, important and current information regarding the role of a small regulatory RNA (sRNA), NikS, in the control of virulence factors of H. pylori, addressing the epigenetic processes involved and the potential clinical repercussions of this modulation.
sRNAs are effective regulatory macromolecules that are able to modulate protein expression and function in response to environmental factors, such as pH, temperature, and metabolite concentration[11]. These post-transcriptional regulators of gene expression play a pivotal role in successful bacterial colonization and stress response, given that they enable metabolic adaptation to the host microenvironment and regulate the expression of virulence factors[12]. The three main classes of sRNAs comprise: (1) Cis-encoded antisense sRNAs; (2) Trans-encoded sRNAs; and (3) sRNAs that modify protein activity (Table 1)[13]. Cis-encoded antisense sRNAs are synthesized from the complementary strand of the mRNA that they modulate. Indeed, these regulators have been strongly associated with the repression of bacterial toxic proteins, through inhibition of primer maturation, transcriptional attenuation, and translational repression or promotion of RNA degradation[14,15]. In contrast, trans-encoded sRNAs are transcribed from a promoter somewhere else on the bacterial chromosome and are only partly complementary to their target mRNAs[16]. In general, this class of sRNAs mainly interfere with translational initiation and/or elongation, e.g., by pairing to ribosome binding sites or translational enhancers. The translation impairment frequently leads to degradation of the mRNA, since it can be more easily targeted by ribonucleases (RNases)[17]. Lastly, sRNAs that modify protein activity are known to modulate protein activity by a mimicking mechanism and thus compete with RNA and DNA targets[13]. These mechanisms are described to utilize several auxiliary proteins, including RNases and ribosome-binding proteins. The Hfq RNA chaperon protein, for example, is strongly associated with the base-pairing between trans-encoded RNAs and their target mRNAs, hence acting in the regulation of virulence factors in Gram-negative bacteria[18]. Thus, as mentioned above, post-transcriptional regulatory macromolecules known as sRNAs can stimulate or inhibit gene expression, playing a key role in bacterial infection through its three distinct groups, ranging from preventing ribosomal binding to modifying protein activities.
Hosts have evolved refined techniques to sense and react against pathogens, such as recognition of pathogen-associated molecular patterns that promotes activation of Toll-like receptors[19]. In this sense, the decisive pathogen’s actions for the infection's success are a faster response and efficient adjustment to a continuously changing hostile environment. Those responses are regulated by sRNAs, due to their flexibility to target a plethora of genes or transcription factors, influencing many ambits of expression and responses to environmental stress[20]. Besides this, sRNAs do not require translation, which means a lower energy consumption for the pathogen[21]. As mentioned above, when entering the host, the bacterium faces diverse innate immunity barriers including: Temperature, pH, changes in nutrient availability, and physical barriers. It is during these circumstances when the varied toolkit of activities of sRNAs perform their roles for pathogen’s survival[22]. These functions can be grouped in two main related fields: Management of biological processes, such as temperature response, biofilm formation, quorum sensing and virulence, and regulation of responses vs host barriers to infection, e.g., acidic pH, inflammation, and nutritional immunity[21]. Regarding the temperature response, it is known that pathogens have to evade the hyperthermia feedback during inflammation[23]. According to studies, an intense involvement of sRNAs in temperature adaptation has been noticed, helping the bacteria to regulate faster their physiology facing environmental thermal disorders[6]. For example, in analysis of Borrelia burgdorferi, responsible for Lyme disease, it was observed that a large set of sRNAs were entangled in regulation of genes involved in adaptation to pyrexia and identification of the molecular scheme to trigger according to environment[24]. Concerning biofilm formation, it is established that it requires coordination of quorum sensing mechanisms to succeed. In P. aeruginosa, researchers found a group of sRNAs, specially RhlS, that bind to the 5’ untranslated region (UTR) of rhlI mRNA and stabilizes it, which is Hfq dependent, resulting in the activation of biofilm genes according to the state of infection and offering additional protection against the host immune system[25]. The role of sRNAs in pathogen’s virulence is also well-represented in P. aeruginosa. The gene RpoS commands a diverse number of virulence related genes, and its translation has been observed to be regulated by the sRNA ReaL, also a Hfq dependent base pairing apparatus, refining the bacterial virulence factors[26]. In the second category group, one of the first barriers to infection is the acidic pH. To overcome the acidic environment of the human stomach and to reach out host cells, for example, it involves several colonization factors like motility and chemotaxis[14]. In this context, H. pylori has sRNAs like RepG and 5’ureB that regulate expression of chemotaxis receptors contributing to stomach colonization[27,28] and linking urease production to surrounding pH[29]. A recent study reported that extreme conditions related to the stress caused by the host inflammatory response during oxidative burst, induces a heavy expression of RsaC, a sRNA of Staphylococcus aureus, avoiding the synthesis of an ineffective enzyme (sodA)[30]. The RsaC attaches to the start codon of the sodA mRNA, committed in protection against reactive oxygen species, leading to repression of this enzyme and allowing the transcription of a second enzyme, sodM, that uses iron as cofactor instead of manganese, recovering the oxidative protection[21]. Therefore, it is firmly established that sRNAs are key players in the adjustment to unstable environmental conditions and response to distinct cellular stresses.
Recently, it was reported that the post-transcriptional regulation of H. pylori virulence factors depends on NikS. NikS has been described to act through base pairing in the 5′ UTR or coding sequence (CDS) of target mRNAs to repress gene expression, including the CagA oncoprotein[31]. In the past, NikS was believed to act as a cis-acting sRNA, however, Eisenbart et al[10] analyzed nucleotides upstream of transcriptional start sites of putative sRNAs and antisense RNAs and observed that NikS expression changed according to the length of a stretch of thymines (T) in the promoter region and these findings contrasted with the premise that NikS acted as a cis-acting sRNA[32]. Once it has been clarified that H. pylori also has trans sRNAs, it is important to highlight that they usually form a base pairing in the 5' UTR or RNA encoding target mRNAs modulating gene expression at the post-transcriptional level[18]. Eisenbart et al[10] also demonstrated in their NikS study that the thymine stretch of the NikS-10 box varies in different strains of H. pylori and this in turn has the potential to alter the spacing between box-10 and other promoter elements. Subsequently, the authors employed Northern blot analysis in the study which revealed differences in NikS expression from 16 to 7 Ts with the lowest expression at 12 Ts. This finding further corroborated the idea that NikS transcription suffers effects from the length variation of hypermutable single sequence repeats[10]. In this sense, Eisenbart et al[10] demonstrated that NikS represses the expression of the main virulence factors produced by H. pylori (CagA and VacA) and three additional factors (HofC, HorF, and HPG27_1238) related to the pathogenicity of the G27 strain, through interactions of base pairing[6]. Completely, Kinoshita-Daitoku et al[32] were responsible for one of the main current studies on NikS. They identified eight factors downregulated by NikS including CagA, HofC, HELPY_1262, HP0410, HorB, OMP14, HopE, and HP1227 and noted that the impact on the regulation of CagA expression stood out among the other factors[32]. Since the regulatory process performed by NikS acts on target mRNAs repressing or activating post-transcriptional gene expression, it is important to say that H. pylori resorts to endoribonucleases such as RNase III so that the sRNAs degrade the target mRNA leading to translation inhibition[18]. In this aspect, Kinoshita-Daitoku et al[32] also reported that NikS regulates the oncoprotein CagA by binding to multiple binding sequences present in its CDS region causing mRNA degradation by RNase III. Furthermore, the authors observed that NikS binding to CagA mRNA regulated the amount of interleukin-8 (IL-8) secreted in H. pylori infection, indicating that NikS acts in the functional control of CagA[32]. Moreover, it is known that VacA is a multifunctional toxin, which stands out mainly for cell vacuolation. In this sense, the repression of this virulence factor can impact the persistence of H. pylori infection[33]. The expression of OMPs in H. pylori strains, in turn, also contributes to bacterial pathogenicity through different mechanisms, such as adhesion, penetration of the defense barrier, and evasion of the immune system. In this sense, by repressing the biosynthesis of OMPs, such as HofC and HorF, the adhesion and colonization processes can be compromised[34]. Finally, it is important to mention that the integration between nickel availability and NikS expression is performed through the NikR[35]. When cytoplasmic nickel concentrations reach a certain threshold, the NikR protein represses nickel import mechanisms in order to control the availability of the metal and achieve the necessary homeostasis[36]. However, NikR also regulates the expression of other genes associated with nickel homeostasis by binding to NikR operators in the promoter or upstream regions[37]. For example, NikR has been shown to bind directly to the NikS promoter, being a key player in controlling NikS expression. In addition, researchers analyzed how strains with varying sizes of T stretch in the promoter region responded to changes in nickel concentration or NikR deletion. Their results showed that the addition of nickel caused a 2- to 10-fold decrease in NikS expression while the deletion of NikR led to a 2-fold increase in NikS levels[6]. In this way, NikS is transcriptionally repressed by nickel via NikR since NikR is able to ration nickel availability and reduced concentrations of this metal imply higher levels of NikS, thereby inhibiting the expression of H. pylori virulence factors (e.g. CagA) (Figure 1). Furthermore, NikS expression changed in nickel-added strains according to different T stretch lengths, but there was no direct correlation between these two factors[6].
CagA is a translocated effector protein that induces morphofunctional modifications in gastric epithelial cells and an inflammatory response, which lead, respectively, to increased bacterial adhesion and nutrient uptake[38,39] (Figure 2). This oncoprotein is encoded by the CagA gene, which is a marker of the cag PAI, a 40 kb DNA fragment that contains about 31 genes and is present in more virulent strains of H. pylori. Some genes on this mobile region of the chromosome encode proteins that form a type IV secretion system, which is responsible for translocating the CagA protein into the cytoplasm of host cells[40-44]. The C-terminal region of CagA has a variable number of Glu-Pro-Ile-Tyr-Ala (EPIYA) motifs, which serve as tyrosine phosphorylation sites. Once it reaches the host cell cytosol, the EPIYA sites of the effector protein are phosphorylated by Src family kinases such as s-Src, Fyn, Lyn, and Yes or by Abl kinases[45,46]. Afterward, CagA acts as a promiscuous scaffold protein that simultaneously disturbs multiple intracellular signaling cascades, involved in regulation of a large range of cellular processes, including proliferation, differentiation, and apoptosis[47]. Phosphorylated CagA is able to stimulate cell proliferation through the activation of promitogenic signaling pathways. Among these, we highlight the activation of the ERK-MAPK pathway through binding to the Src-homology domain 2 and consequent activation of SHP-2[48]. This process also leads to alterations in the cytoskeleton, which induces host cell elongation and change to the recognized "hummingbird" phenotype[7,8,49]. In addition, CagA causes disruption of cell polarity by interaction with the serine-threonine kinase Par-1b and disturbs cell junction-mediated functions[8,47]. This virulence factor is also able to reduce apoptosis in gastric epithelial cells, through the inhibition of tumor suppressor factors such as p53 and RUNX3[50-53]. These direct effects of CagA on epithelial cells could be related to the development of precancerous lesions, since carcinoma development has been observed in animal models even in the absence of inflammation[54-56]. Nevertheless, this effector protein was reported to be able to induce the transcription factor NF-κB and IL-8, which are crucial determinants of chronic inflammation and thus of the pathogenesis of peptic ulcer and gastric cancer[43,57]. At last, CagA also induces genetic and epigenetic alterations in the host cells that lead to a pro-carcinogenic environment[7]. In this regard, some authors suggest that the modulation of CagA expression via post-transcriptional control by NikS favors a more delicate equilibrium between induction of morphofunctional changes and inflammatory response with its regulation, so as to establish a balance between eradication and nutrient uptake[54]. Using in vitro infection studies, Eisenbart et al[10] demonstrated that possibly due to increased CagA expression, G27 strains deficient in NikS show higher numbers of intracellular bacteria, greater “hummingbird” phenotype induction in host cells, as well as increased epithelial barrier disruption. From these findings, it is possible to infer that higher expression of NikS and, consequently, lower synthesis and translocation of the oncoprotein, would reduce the CagA-induced morphofunctional alterations in the host cell, such as apoptosis of epithelial cells, loss of cell polarity, and chronic NF-κB-dependent inflammatory response, along with carcinogenesis. Interestingly, it was further reported by Kinoshita-Daitoku et al[32] that NikS expression is lower in clinical isolates from gastric cancer patients than in isolates derived from non-cancer patients, while the expression of NikS-targeted virulence factors, including CagA, is higher in isolates from gastric cancer patients. Therefore, it is possible to suggest a possible correlation between NikS expression and the onset of peptic ulcer and gastric malignancies, such as gastric carcinoma and MALT lymphoma secondary to H. pylori infection.
Considering that the regulatory role of NikS on H. pylori virulence factors is a recent discovery, there are still few studies on the subject. However, the broad action of NikS on these virulence factors may be strongly related to the risk of diseases derived from H. pylori infection. In this sense, one of the aims of our group is to evaluate whether the variation of the number of Ts in the promoter region of the NikS gene is associated with the risk of duodenal ulcer or gastric carcinoma in adults. However, further studies are still required for better understanding the role of NikS in the pathogenesis of H. pylori, as well as its possible relationship with other genes.
In summary, recent findings on sRNA-mediated regulation of H. pylori infection revealed that increased nickel concentrations lead to reduced NikS expression and this in turn up-regulates CagA levels. There is still much to be clarified about the regulatory properties involved in H. pylori infection. However, it is notable that CagA is the protagonist of gastric carcinogenesis and a deeper understanding of the interaction between this virulence factor and sRNAs such as the nickel-dependent NikS is of utmost importance for a broader understanding of the mechanisms involved in the control mediated by RNAs in H. pylori and their association with gastric malignancies and other clinical conditions. Finally, given the potential for heterogeneity of the bacterium, evolution of its strains, its pathogenicity, and the emergence of therapeutic resistance of this pathogen, it is essential to periodically reassess the molecular issues of the infection to achieve advances in the diagnosis and treatment of the disease. |
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PMC9649582 | Luca Mollica,Francesca Anna Cupaioli,Grazisa Rossetti,Federica Chiappori | An overview of structural approaches to study therapeutic RNAs 10.3389/fmolb.2022.1044126 | 28-10-2022 | therapeutic RNAs,RNA structure,RNA binding,RNA dynamics and flexibility,RNA selectivity and specificity | RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity. | An overview of structural approaches to study therapeutic RNAs 10.3389/fmolb.2022.1044126
RNAs provide considerable opportunities as therapeutic agent to expand the plethora of classical therapeutic targets, from extracellular and surface proteins to intracellular nucleic acids and its regulators, in a wide range of diseases. RNA versatility can be exploited to recognize cell types, perform cell therapy, and develop new vaccine classes. Therapeutic RNAs (aptamers, antisense nucleotides, siRNA, miRNA, mRNA and CRISPR-Cas9) can modulate or induce protein expression, inhibit molecular interactions, achieve genome editing as well as exon-skipping. A common RNA thread, which makes it very promising for therapeutic applications, is its structure, flexibility, and binding specificity. Moreover, RNA displays peculiar structural plasticity compared to proteins as well as to DNA. Here we summarize the recent advances and applications of therapeutic RNAs, and the experimental and computational methods to analyze their structure, by biophysical techniques (liquid-state NMR, scattering, reactivity, and computational simulations), with a focus on dynamic and flexibility aspects and to binding analysis. This will provide insights on the currently available RNA therapeutic applications and on the best techniques to evaluate its dynamics and reactivity.
Modern molecular biology research redefined the central dogma that explains the flow of genetic information, from DNA to RNA and from RNA to protein (Crick, 1970) and shed the light on the multiple roles of non-coding RNA (ncRNA). The most familiar form of RNA is the protein coding RNA (mRNA), but only a small fraction of RNA molecules in cells (about 5%) belong to this class and the remaining are ncRNAs. They are strategic in cell biology: ncRNAs regulate gene expression and protein functions, catalyze chemical reactions, slice and dice genetic materials (including other RNAs), and take part in building proteins by transporting amino acids and linking them together (Mollocana-Lara et al., 2021). The large use of next generation sequencing, as well as bulk and single-cell gene-expression analysis, revealed new genome-based therapeutic targets (Paunovska et al., 2022). Besides, only 1.5% of the human genome encodes for proteins, and among them, less than the 15% displays a binding site targetable by small molecules (Hopkins and Groom, 2002; Ezkurdia et al., 2014). In this context, ncRNAs, have inspired scientists about how to harness RNA as medical treatment. The progress in RNA biology, bioinformatics, and nanotechnologies, mainly for the delivery vehicles, fostered the development of RNA-based therapies toward their translation in clinical practice. Expectations were met when US Food & Drug Administration (FDA) approved Patisiran, the first RNA interference (RNAi)-based treatment for hereditary transthyretin amyloidosis, and Givosiran, RNAi-drug for acute intermittent porphyria in 2018 and 2019 (Brown and Wobst, 2021). The potentiality of RNA-based therapeutic modalities has been recognized globally with the successful outcomes of SARS-CoV-2 virus (COVID-19) mRNA vaccine. To date, the majority of approved RNA-based therapeutics are antisense oligonucleotides (ASO), followed by siRNAs and mRNAs (Zhu et al., 2022). RNA-based therapies are currently suitable for pathologies with established genetic targets, such as cancers, immune diseases and Mendelian disorders and infectious diseases. Therapeutic RNAs can modulate transcript level, inhibit RNA modulator activity, encode therapeutic protein, or induce alternative splicing, and also bind target proteins (Feng R. et al., 2021). Advantages of RNA-based therapies includes (I) acting on targets “undruggable” for a small molecules or proteins; (II) targeting a wide variety of cellular component including genes, transcripts, regulators and proteins at every level of cellular organization; (III) the high specificity, due to the base complementarity, unattainable with small molecules; (IV) the high purity of RNA construct; (V) rapid and lower cost effective development, by comparison to “traditional” small molecules or recombinant proteins based therapies; (VI) the possibility to personalize treatments, rapidly edit ad hoc the RNA construct sequence or to adapt to a new therapeutic request, such as pathogen variants. Despite the advantages of RNA-based therapies, the development of RNA therapeutics meets challenges. RNAs are rapidly catabolized by ubiquitous RNases (Houseley and Tollervey, 2009) and exogenous RNAs induce acute immune response that cause cell toxicity. Furthermore, RNA therapeutics delivery represents another hurdle. RNA drugs cannot be orally administrated, their delivery system depends on the type of RNA-based therapeutics and targeting, and whether transient or stable expression is desired. RNAs are negatively charged and cannot cross cellular membranes or the blood brain barrier. The delivery of RNA therapeutics should guarantee RNA stability and give the possibility to reach target tissue, cell type or subcellular organelles at therapeutic concentration. The delivery method should help to solve these challenges, it must be biocompatible, reduce the immune activation and have higher delivery efficiency (Cupaioli et al., 2014). Development of nanoparticle addressed many of these needs as RNA therapeutics delivery system as outlined in (Paunovska et al., 2022). RNA molecules fold, based on the Watson–Crick base pairing, into secondary structures and specific sub-structures, such as characteristic RNA loops, like hairpin loops, internal and external loops, base-pair stackings, multi-branch loops, bulge loops, junctions, pseudoknots, kissing hairpins, and so forth. Functions of ncRNAs as well as their effectiveness as therapeutic agents are deeply related to their folding because these structures are recognized by proteins, other RNAs and other parts of the same RNA. Their thermodynamic stability is crucial, and it is determined by conditions that occur and change when interacting with proteins or other ligands (Nowakowski and Tinoco, 1997). The prediction of stable optimal RNA secondary structures based on thermodynamic models, such as Turner’s nearest-neighbor model (Turner and Mathews, 2010), has some strategic significance to the development of RNA-based therapies, along with bioinformatics tools that help to predict RNA secondary structure by free energy minimization model as well as RNA modifications. The implementation of therapeutics drug design and bioinformatics platforms, structural modeling and machine learning play a key role in this new RNA therapeutics field. Small molecule drug development pipelines that target enzymes, and protein–protein interactions cannot be applied to nucleic acids. The progresses in experimental resolution and computational modelling of RNA structures have enabled clinical translation of RNA-based therapy. In this review we will discuss the characteristic of RNA therapeutics and recent advances in this field, summarizing from the structural viewpoint, the available strategies, and the computational and experimental analysis methods.
RNA-based therapies rely on both coding and non-coding RNAs, and mainly targets nucleic acids (ether DNA or RNA) and proteins. These therapies can control gene function by silencing or activation, splice modulation, transcript degradation, translational activation, or antigen synthesis, up to protein encoding and function, as also decrease or block protein production (Table 1). Aptamers are used to target proteins (Zhou and Rossi, 2016), while single-stranded antisense oligonucleotides (ASOs) and double-stranded molecules target nucleic acids by RNA interference (siRNA) (Watts and Corey, 2012). The mRNA can be administrated as therapy to promote transient protein expression and have recently evolved into mRNA vaccines and protein replacement therapies (Yu et al., 2020). RNAs are also used in genome editing for biological and therapeutics approaches, such as CRISPR-Cas gene editing (Zhang et al., 2021).
Aptamers are short (20–100 nt) single strand nucleic acids that fold into specific tertiary structures (Figure 1). Aptamers binding specificity and affinity is the outcome of their tertiary structure rather than their sequence, like antibodies. Therefore, aptamers have potentially unlimited therapeutic targets, they can bind to a variety of extracellular, circulating, and intracellular targets: proteins, peptides beyond carbohydrates and small molecules (Mollocana-Lara et al., 2021) also allowing the recognition of specific cells and tissues. Aptamers can act as agonists, thus functionally activating their target molecules (Damase et al., 2021), and as antagonists thus blocking the interaction of molecules in pathways associated with disease development (Damase et al., 2021). Furthermore, aptamers can act as bispecific agents that recognize simultaneously two or more proteins, such as cell surface receptors, improving cell type targeting specificity (Zogg et al., 2022). Specificity can be improved by relatively small changes, as shown by the presence of a single methyl group that makes aptamer more sensitive by 10k-fold to caffeine than theophylline, which sterically prevent the formation of an H-bond with the RNA molecule (Jenison et al., 1994), and in cyclic adenine/guanine aptamer recognition where sensitivity goes as low as single base paring (Knappenberger et al., 2018). In 1990 two different groups (Ellington and Szostak, 1990; Tuerk and Gold, 1990) developed the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology to design and select aptamers with high affinity and specificity (Section 6.3). Although aptamers display a similar function to antibodies, RNA aptamers have essential advantages: a smaller size, an efficient design method and chemical synthesis, performed entirely in vitro, with a very low variability among different batches, and lower production cost (Mollocana-Lara et al., 2021; Talap et al., 2021). The main issues are nucleases sensitivity, and the rapid excretion (Damase et al., 2021). As for other therapeutic RNAs, nucleic acid post-transcriptional and chemical modifications are useful to increase the structural stability, prevent RNase digestion and improve their functionality (Gehrig et al., 2012) Introducing afterwards chemical modification, though, can influence the aptamer tertiary structure, with consequent effects on target binding. Therapeutic aptamers are chemically modified to contain methyl groups at the 2′-position of their sugar moieties, rendering them resistant to degradation by serum nucleases and reducing the inherent immunogenicity of unmodified RNA (Odeh et al., 2019). Structural studies and computational tools able to determine and predict aptamer structure and thermodynamic properties, before and after the target binding, are necessary to elucidate how small RNAs can fold into highly complex three-dimensional structures (Duchardt-Ferner et al., 2020). Moreover, the binding to the target highly influences aptamer final conformation, frequently inducing a transition toward a well-ordered structure (Hermann and Patel, 2000). Also, the identification of key interaction residues and structural motifs is helpful for aptamer design modification (Sharma et al., 2017). Actually, few in silico example are reported that determine the three-dimensional conformation of aptamers and the binding to their target (Oliveira et al., 2022).
ASOs are short (10–30 nt) single strand DNA or RNA molecules (Figure 1). Overall, ASOs activities can be ascribed to Watson-Crick base-pairing with their target sequences. ASOs prevent the mRNA translation into protein by pairing or steric hindrance (Baker et al., 1997), or alter its splicing (Hua et al., 2007), by blocking splicing cis-elements or affecting mRNA structure (Singh et al., 2018; Lim et al., 2020); few examples of enhanced mRNA translation upon ASO binding to upstream open reading frames (uORFs), are also available (Liang et al., 2016, 2017). Based on their mode of action, ASOs are classified into three categories: gapmers, steric blocking and exon-skipping. Gapmers are DNA stretches that bind the target RNA forming a hybrid double strand (DNA-RNA), that is recognized and hydrolyzed by endogenous RNase H (Wu et al., 2004; Lee et al., 2022). About 10 central nucleotides are necessary for the target recognition, flanked by shorter modified nucleotides. Sugar modification, not limited to gapmers, increases digestion resistance and binding affinity, and reduces immune activation, while phosphorothioate backbone promotes the transport into the nucleus (Eckstein, 2014; Shen et al., 2019) but triggers innate immune response (Fusco et al., 2019). Gapmers are efficient in downregulating nuclear targets, useful to treat diseases caused by an overexpression of toxic proteins, or mutated variants of the wild-type protein (Zogg et al., 2022). In addition, ASOs target single nucleotide polymorphisms (SNPs) associated with a mutant allele, maintaining wild-type allele function (Rinaldi and Wood, 2017). Steric blocking ASOs are RNase H-independent and can inhibit or increase protein translation by binding to different targets. Inhibition is obtained by binding directly to mRNA and pre-mRNA or to trans-acting factors, like small nuclear RNAs, miRNA, long non-coding RNAs or RNA binding proteins (Quemener et al., 2020; Lee et al., 2022). This interaction prevents the binding to the AUG start codon, blocking the association of ribosomal subunits (Paunovska et al., 2022) or blocking polyadenylation sites, leading to shorter and destabilized transcripts (Vickers et al., 2001). Binding to the regulatory regions, uORF, instead, increases the translation of the main ORF by preventing the binding of translational suppressors (Zogg et al., 2022) up to 30–150% in a dose dependent manner (Rinaldi and Wood, 2017). MiRNAs that promote carcinogenesis and metastasis have also been targeted by ASOs (Shah et al., 2016). Exon-skipping ASOs are single strand splice-switching oligonucleotides (SSOs) that recognize pre-mRNA altering the splicing process (Lee et al., 2022; Paunovska et al., 2022). SSOs are able to restore the correct function of aberrant splicing mutations causing disease, like Duchenne muscular dystrophy (Lee et al., 2022). The nucleotide modifications mentioned above for gapmers, influence also ASOs solubility necessary for their delivery and cellular and nuclear membrane penetration, which remains the main challenge for these therapeutic molecules. Several methods can be employed to select an optimal candidate, including empirical testing of large numbers of mRNA complementary sequences, combinatorial arrays, computational RNA folding tools, and in silico prescreening using statistical modelling (Rinaldi and Wood, 2017; Fusco et al., 2019). The correct prediction of mRNA secondary structure allows to identify regions accessible to hybridization, necessary to design highly efficient ASOs (Chan et al., 2006). Recently targeted augmentation of nuclear gene output (TANGO) (Lee et al., 2022) has been proposed to prevent non-productive alternative splicing events, increasing the full-length functional transcript. TANGO strategy has been applied in the treatment of Dravet syndrome (Lee et al., 2022). Moreover, Li and colleagues employed three-dimensional structure in ASO design for targeting SARS-CoV-2 RNA frameshifting stimulation element (FSE), involved in the viral protein synthesis pathway, and transcriptional regulatory sequence (TRS) regions Li et al. (2021). The first one disrupts the FSE pseudoknot, an RNA secondary structural element, preventing the RNA-dependent RNA polymerase encoding. While the second one inhibits viral replication through TRS region; the ASO folds into a hairpin suggesting that the design of a highly specific ASO depends on its secondary structure.
Small interfering RNAs (siRNAs) are short (20–25 nt) double stranded non-coding RNAs (Gavrilov and Saltzman, 2012), obtained from a precursor after a maturation process (Figure 1). The precursor of about 30–100 bp is processed by endogenous Dicer enzyme into 20–30 bp long siRNA; this includes two bases’ overhangs at the 3′ end single base paring in cyclic adenine/guanine aptamer recognition and interacting with RNA-induced silencing complex (RISC) to obtain mature interfering RNA (Damase et al., 2021). The RISC loaded antisense strand guides the complex to its target mRNA, where endonuclease argonaute 2 (AGO2) digest its phosphodiester backbone degrading the transcript (Feng R. et al., 2021; Mollocana-Lara et al., 2021). siRNAs recognized by AGO2 are fully complementary to their targets, while other AGO enzymes (AGO1, AGO3 and AGO4) catalyze endonuclease-mediated nonspecific mRNA degradation (Paunovska et al., 2022). Double stranded siRNAs are more effective than single stranded ASOs in downregulating mRNA targets (Zogg et al., 2022); RISC complex is localized in the cytoplasm rather than RNase H that is predominantly nuclear, thus target mRNAs at different stages of the transcriptional process (Lennox and Behlke, 2016). To obtain the desired siRNA, and not its antisense strand, the correct strand must be loaded into RISC; this can be achieved using asymmetric duplex, that will favor loading into RISC the strand with less stable hybridized end (Walton et al., 2010). Like other therapeutic RNAs, nucleotide chemical modification in the sugar backbone prevents nuclease degradation, nucleoside modification to lower immunogenicity, increases safety, efficiency, and specificity (Layzer et al., 2004; Khvorova and Watts, 2017; Yu et al., 2020; Feng R. et al., 2021). The choice of the target site is critical to obtain an effective siRNA, which is usually localized closer to the start codon. Also, the C/G content influences the specificity and the stability of the siRNA, setting this content at least at 50%; while siRNA enriched in U-content have an immune-stimulatory effect (Yu et al., 2020). Moreover, a sequence motive AA_N(19)_TT was suggested to be advantageous (Kurreck, 2006). A blast search to avoid possible unwanted pairing with other target genes is required. The thermodynamic stability of the siRNA duplex is a useful parameter to evaluate its functionality, concerning nucleotide position, principally for strand dissociation process, the beginning and the central portion of the siRNA should display a low stability, while the end should be thermodynamically more stable (Amarzguioui and Prydz, 2004; Reynolds et al., 2004; Ui-Tei et al., 2004). As for ASO, the target sequence on the mRNA must be identified in accessible regions to obtain efficient interference. Several bioinformatics tools are available to calculate thermodynamic and target secondary structure [Section 4.1 molecular dynamics, Section 6.5 docking paragraph and (Shabalina et al., 2006) for details] to help in siRNA design.
Synthetic microRNAs (miRNAs) are short (22 nt) double strand non-coding RNAs (Figure 1), that regulate the expression of several mRNAs by blocking translation or promoting degradation of the target by binding to the 3′ untranslated region (UTR). Mature miRNA results from the processing of longer pre-miRNA molecules by Drosha and Dicer ribonucleases. Mature miRNA is loaded into RISC complex and miRISC recognizes the 3′UTR of target mRNA through 2 to7 bases match at miRNA 5′ seed region, thus inducing translational repression or mRNA degradation (Huntzinger and Izaurralde, 2011; Damase et al., 2021). MiRNA-based therapeutic can mimic endogenous miRNAs or inhibit them (antimiRs): mimic miRNAs, administered as pre-miRNA, are double-stranded, match the endogenous miRNA and display the same activity restoring the lost miRNA expression in disease (Mollocana-Lara et al., 2021); miRNA inhibitors, administered as single-stranded oligonucleotides pair to endogenous miRNA blocking its activity (Zogg et al., 2022). AntimiRs with a 2′-O-methoxyethyl modification are also called antagomiRs (Rupaimoole and Slack, 2017). MiRNAs are dysregulated into a wide variety of disease, therefore there is a clear interest for the use of miRNAs as therapeutic agents or targets (Yu et al., 2020; Chakraborty et al., 2021). Moreover, endogenous miRNAs target several genes simultaneously, frequently belonging to common pathway or cellular function. Indeed, miRNA-target complementarity rules allow for multiple mismatches targeting multiple mRNAs. The ability to act on multiple targets is, on the one hand, and an advantage, on the other hand, it reduces therapeutic miRNA specificity, making miRNA not suitable for personalized medicine. Moreover, miRNA effects are transient and require repeated administrations. However, comparing miRNA to other synthetic RNAs, therapeutic miRNAs display a reduced immune response (Zogg et al., 2022).
CRISPR/Cas system belongs to the bacterial adaptive immune system that evolved to protect against phage infection by incorporating short repeats of the viral genome into the bacterial one. This system has been modified and used for genome editing accelerating scientific breakthroughs toward human gene therapy. CRISPR/Cas9 is used in mammalian cells and contains two components: a CRISPR-associated protein (Cas), a DNA endonuclease, and a single guide RNA (sgRNA) (Figure 1). In sgRNA the specificity-determining CRISPR RNA (crRNA) and an auxiliary trans-activating RNA (tracrRNA) are fused. The first ∼20 nucleotides of the crRNA include the seed sequence and a non-seed sequence. The seed sequence determines Cas specificity and is composed by the 10–12 bases complementary to the target DNA sequence, followed by a sequence called protospacer adjacent motif (PAM), a short (2–6 bp) in the genomic DNA. The PAM is essential for cleavage by Cas nuclease. The crRNA binds to the Cas9 through its hairpin scaffold and the sgRNA recognize with high specificity the PAM complementary sequence. Perfect complementarity between seed sequence included in crRNA and the target DNA is required. This direct Cas nuclease to the target sequence, where tracrRNA portion of sgRNA forms an R-loop due to the base paring with Cas-DNA. Upon RNA-DNA heteroduplex formation the nuclease introduces site-specific breaks into the 20-nucleotide DNA target sequence. The damaged DNA is repaired via non-homologous end joining (NHEJ) or homology-directed repair (HDR), thereby resulting in gene disruptions and inactivation of the targeted gene (Zhang et al., 2015; Jiang and Doudna, 2017). NHEJ is exploited also for linear DNA fragments tag-guided insertion (Pickar-Oliver and Gersbach, 2019) (Figure 2). Cas nuclease specificity relies on the distance from PAM sequence to the cleavage site and on the capacity to perform double- or single-stranded breaks. For example, the most employed Cas9 performs double breaks three bases upstream PAM. CRISPR-Cas is at the cutting edge of genetic engineering: CRISPR-Cas9, has been modified to correct disease-causing mutations, inactivate oncogenes or activate oncosuppressors and to act as transcriptional or epigenetic regulator (Larson et al., 2013; Qi et al., 2013). Clinical trials based on CRISPR gene editing are ongoing to treat genetic disorders such as cystic fibrosis, Duchenne muscular dystrophy, viral infections, immunological disorders, and cardiovascular diseases (Feng R. et al., 2021). The design of sgRNA is crucial to minimize off target cutting and therefore off-target mutations and cleavage; several bioinformatic tools are available for the rationale design of highly active and specific sgRNAs (Leonova and Gainetdinov, 2020). The main challenge of this technology is the delivery to target cells because both the Cas protein and the single-guide RNA (sgRNA) must be present at sufficient concentrations to form intracellular ribonucleoproteins (Paunovska et al., 2022); and differently from the previously described RNA therapies, a DNA-target “drug” must penetrate both, cytoplasmic and nuclear membranes (Damase et al., 2021). As for previously presented RNA therapies, nucleotide chemical modifications can be useful to protect against RNase digestion and avoid immunogenicity; moreover, chemically modified gRNAs have been shown to enhance genome editing efficiency and target specificity in mammalian cells (Yu et al., 2020). The fine-knowledge of this large complex, obtained by biochemical and structural studies on Cas9 at different stages of DNA target surveillance, provides key hints for its customization and application in therapies (Jiang and Doudna, 2017). Recently molecular dynamics (see paragraph Section 5.1 for method details) (Palermo et al., 2017), also coupled with FRET (Palermo et al., 2018) or solution NMR experiments (see paragraph Section 4.1 for method details) (Nierzwicki et al., 2021), have been applied to evaluate the conformational dynamics of the complex, necessary for catalytic activity, as well as the binding features of the single-guide RNA and of DNA-target (Mitchell et al., 2020). Studies at atomic details contribute to rational design of Cas9 proteins and to understand the sensibility of this “machine”, allowing a more effective design of guide-RNAs, thus obtaining a more efficient CRISP-Cas9 genome-editing tool.
The use of exogenous mRNA to mimic cellular mRNA to produce desired proteins is an established technology since many years (Damase et al., 2021) (Figure 1). Therapeutic mRNAs are much bigger in size than other therapeutic small RNAs as they maintain the same structure than the natural one: a single strand molecule, with a central coding sequence (CDs), flanked by a 5′ cap for nuclear export and translation promotion, a 5′UTR for ribosome recruitment, a 3′UTR for post-transcriptional control and a 3′ poli (A) tail for mRNA stability. mRNA can be employed for several therapeutic purposes: 1) protein replacement therapy, where mRNA is administered to compensate for a defective protein, or to supply therapeutic proteins; 2) vaccination, where mRNA encoding specific antigen(s) is administered to elicit protective immunity 3) cell therapy, where mRNA is transfected into the cells ex vivo to guide differentiation or alter cell phenotype, and then these cells are injected back to the patient (Chabanovska et al., 2021; Damase et al., 2021). Therapeutic mRNAs have a short half-life; once reached the cytoplasm of target cells, they are digested in few hours (Damase et al., 2021). SARS-CoV-2 mRNA-based vaccines have promoted a further and fast in the development of the technology (Corbett et al., 2020; Patel et al., 2022). These mRNA vaccines encode the prefusion stabilized full-length spike protein (Baden et al., 2021) with relevant results in disease control. As previously described therapeutic RNAs, also mRNA solubility, efficacy, and immunotolerance, can be improved by chemically nucleotide modification, as well as by “converting” mRNA into circular RNA (circRNAs) (Mollocana-Lara et al., 2021).
A strategic role in the development of RNA-based therapies is played by physical-chemistry, structural biology, and computational modelling of biomacromolecules. These disciplines ensure the fine tuning and/or prediction of the intramolecular interactions that drive molecular recognition. Hence, mastering the RNA chemistry is critical in this respect. Despite the apparent chemical similarity between DNA and RNA nucleotide monomers sidechains, the subtle difference between the two backbone chemical moieties enormously influences both the reactivity and the dynamics of the two polymers. Each nucleotide consists of a planar aromatic base attached to a furanose ring with a 5′-phosphate group. Phosphodiester linkages between successive sugar residues groups (linking the 3′-carbon of each sugar to the 5′-carbon of the next one) drives the polymerization of RNA chains, leaving a free 5′-position at one end of the chain and a free 3′-position at the other. If this process is basically the same for DNA, the 2′-OH group of the RNA ribose (2′-H in DNA) moiety makes RNA less chemically stable than DNA by facilitating self-cleavage reactions, thus making RNA more flexible and prone to adaptation in cells. Moreover, 2′-OH group is a versatile hydrogen bond donor and acceptor, allowing the formation of unique macromolecular spatial arrangements with a functional role. This is reflected by the necessity of describing RNA molecules hierarchically by analyzing both their secondary structure, i.e., the interaction between nucleic acids regardless their three-dimensional orientation and the overall folding, and their tertiary structures, i.e., the spatial organization of atoms belonging to the RNA polymer. Moreover, post-translational modification of RNA has an impact on topological and spatial arrangement of RNA molecules (Nachtergaele and He, 2017). Secondary (2D) structure is defined by the pattern of Watson−Crick canonical base pairs (A-U, C-G base pairs). Canonical double helices alternate with unpaired regions, namely that do not form canonical base pairs. RNA topology is determined by the backbone, which depends on the position and direction of the backbone segments attached to the bases. This links a local property (base pair geometry) with the global topology, which determines the molecule’s biological function. Building on the basic architecture defined by the 2D structure, RNA 3D structure is determined by some canonical and many noncanonical base pair interactions that often involve H-bonding through the 2′-OH group. RNA folding kinetics is hierarchical and sequential secondary (2D) structure motifs display an overall higher stability compared to 3D structures, they fold autonomously, and usually (Brion and Westhof, 2003) before and independently of 3D structure. The description of the RNA formal structures is completed and complicated by pseudoknots, i.e., an extended canonical base pairing between a hairpin loop a 2D element and a single distal complementary strand region. A single RNA molecule, with a well-defined sequence, often has multiple accessible 2D and 3D structures that lie within a narrow range of folding free energies. These are sampled because of thermal fluctuations or proteins and other cofactors interactions induce or select specific RNA conformations. The time scales over which these alternate structures form and disappear can range from microseconds for simple base pairing changes to seconds (or even longer) for complex 2D refolding events (Al-Hashimi and Walter, 2008). Because RNA molecules often play multiple roles in biological processes, their native states can be anything from structured folded architectures to intrinsically disordered dynamical single-stranded ensembles.
After at least three decades of investigations led by physico-chemical techniques, the structure-function paradigm is currently integrated in almost all the studies that address how biomacromolecules mechanistically exert their function. More recently the role of the internal motions has been elucidated and has revealed to be crucial in molecular recognition between partners. Macromolecular structures obtained by X-ray crystallography, high-resolution NMR spectroscopy and from cryoelectron microscopy constitute an essential technique for characterizing the lowest (i.e., the most probable conformation) energy state of a molecular system and understanding molecular recognition. Nowadays, structural plasticity has become a stable asset to understand macromolecular functionality. The combination of complementary physico-chemical techniques (either experimental or a combination of experimental and computational techniques) allows to extensive investigate structural equilibrium of molecules, also to obtain truthful models. It is of key importance also the experimental characterization of the less populated states [e.g., base-pairing partner switches for RNA (Dethoff et al., 2012)], that are in thermal equilibrium with the ground state of any macromolecular system and can be now assessed by the new methodologies (Figure 3; Table 2). In this section we will discuss biophysical methods that are now routinely used for the characterization of the interaction between biomolecules and their partners, either other macromolecules or small molecules, with a focus on their application to RNA. In this respect, due also to the relatively recent role of RNA molecules as drugs, these techniques will be reviewed regardless the oligonucleotides function as ligand or as target, with the aim of highlighting the general principles behind the characterization of their interaction with any partner. Moreover, in this respect a purpose of this review is the suggestion of the application of these techniques, once they have not been intended in this way, to the world of RNA-based drug discovery.
Nuclear magnetic resonance (NMR) spectroscopy is the most advanced experimental technique that can efficiently provide details ranging from atomic positions to inner or global dynamics (Hodgkinson and Emsley, 2000; Lotsch et al., 2007). The inner dynamics and global hydrodynamics of any molecule influence the NMR observables, allowing the determination of phase changes, conformational and configurational alterations, solubility, and diffusion potential (Levitt, 2008). If this is an asset of protein-bases systems, from the 80s, the widespread application of NMR spectroscopy for the characterization of nucleic acids under solution condition is more recent. Higher magnetic fields and cryogenic technologies partially overcome the limitations deriving from low chemical heterogeneity of RNA building blocks (only four nucleotides, compared to 20 amino acids), leading to low chemical shifts dispersion of nuclei of interest (i.e., closeness of resonances, hence low distinguishability).
This technique monitors RNA dynamics in real-time (i.e., a series of spectra recorded at regular intervals), and is the most intuitive NMR-based method, as the changes that occur in a biological system can be directly associated with disappearance and appearance of signals. In a classical drug discovery context (i.e., small-molecules based) kinetic properties of non-equilibrium ligand-dependent RNA can be followed using time-resolved NMR (Buck et al., 2009; Lee et al., 2010). Real time NMR is used to investigate RNA conformational transitions, such as RNA (re-)folding, the catalytic reactions of ribozymes or purine riboswitches started by addition of the ligand (Buck et al., 2009; Steinert et al., 2017) or in some cases also RNA binding affinity for cations (Müller et al., 2021). Recently, successive NMR measuring is used to investigate the dynamic adaptation of RNA modifications in response to environmental changes. In Barraud et al. (2019), the authors describe the maturation of yeast tRNAPhe in cell culture extracts using labelled isotype in a continuous and time-resolved fashion. A general and extensive overview of real-time NMR spectroscopy methods, with an interesting focus on folding-inducing ligands, is reported in Pintér et al. (2021).
The proper understanding of biomolecular recognition mechanisms that take place in drug targeting is of paramount importance to improve the efficiency of drug discovery and development. The flexible nature of RNA influences the molecular recognition mechanisms and its characterization plays a key role in a drug discovery context, i.e., allowing the identification of multiple conformers involved in the process. NMR spectroscopy offers a wide range of techniques that can be informative in this context also including time resolution of structural interconversion. Most of current NMR techniques for the investigation of biomolecules structure and dynamics is based on isotope labelling, i.e., the enrichment of NMR-sensitive isotopes (13C, 15N) (Barton et al., 2013). As extensively reviewed in Lu et al. (2010), the three main methods for isotope labelling of nucleic acids are chemical synthesis, enzymatic synthesis using T7 RNA polymerase and segmental labelling. Although modified NTPs can readily be incorporated into specific positions, low coupling efficiencies limit chemical synthesis to relatively small RNAs. RNAs for NMR-based structure determination are now commonly prepared by in vitro transcription using the T7 RNA polymerase (RNAP) enzyme (Belin, 1997). Recently, segmental labeling approaches have been developed for studying sub-domain structure in the context of intact, functional RNAs (Lu et al., 2010), leading to an efficient method for overcoming signal overlap problems associated with larger RNAs. Labelling strategies are the crucial step for assigning the resonances of atomic species within a macromolecule according to their chemical nature (chemical shifts) (Lu et al., 2010), as well for performing dynamics-related measurements such as spin relaxation and residual dipolar couplings (RDCs). Spin relaxation, that can be seen as a measurement of the decay of the excited magnetization perpendicular to an externally applied magnetic field, has been widely used in the last two decades for the characterization of internal protein motions. The theoretical framework for its interpretation correlates the macromolecule overall shape, reference structure and hydrodynamics to its relaxation properties. While this provides a proper description of the relaxation-related observables for globular proteins, its application to flexible objects like intrinsically disordered proteins (Salvi et al., 2017) or RNA has been more difficult. For this reason, protein-RNA binding has been often studied by means of spin relaxation experiments that could detect the conformational recognition of RNA on the surface of proteins (Wurm et al., 2016; Lixa et al., 2018). Relaxation dispersion is suitable for investigating transition/transient states in structures, often reported as invisible states. Relaxation dispersion can be observed in systems that interconvert between two distinct states at a rate that is comparable to the difference between the frequencies detected for the two states: the exchange causes significant line-broadening of signals from both states. This technique is suitable for detecting the RNA molecules dynamic states as well as the effect of small ligands on them, allowing the distinction between induced fit and conformational selection mechanism (Vogt and Cera, 2012). In Orlovsky et al. (2020) authors highlighted conformational penalties as major determinants of RNA-ligand binding affinity as well as a source of binding cooperativity, with important implications for a predictive understanding of how RNA is recognized and for RNA-targeted drug discovery. The idea of preformed binding site for a ligand is also suggested by another recent study (Ding et al., 2019) demonstrating (using relaxation data and free electron laser X-ray diffraction experiments) that the local timescales and entity of motions in the binding site for riboswitch aptamer is comparable in the free and complexed form of RNA. The same approach has been adopted to RNA considered as a ligand and applied to Fsu preQ1 class I aptamer for the determination of the binding kinetics of the ligand preQ1 (Moschen et al., 2015). Interestingly, the study revealed aside the kinetic parameters of binding that the RNA molecule adopted a pseudoknot structure prior to binding and in slow conformational exchange with the unknotted form, revealing how in this class of molecules the approaches used for revealing the overall shape and structure is mandatory for a complete characterization of binding. Chemical bonds and structural motifs orientation have an influence on dipolar couplings (DCs), i.e., the spectroscopic observable that reflects the interaction between individual nuclear spins energy levels as mediated by their distance, electronic environment, and relative orientation. Residual dipolar couplings (RDCs) are variations of complete motional averaging of DCs due to a partial molecular alignment to the external magnetic field. RDCs can also provide information about dynamics on micro-milliseconds timescale. An application of RDCs for the determination of RNA binding modes to proteins has been reported in Borkar et al. (2016) once the structure of the complex formed by the HIV-1 protein transactivator of transcription (Tat) and its cognate transactivation response element (TAR) RNA transactivates viral transcription, a paradigm for the widespread occurrence of conformational rearrangements in protein-RNA recognition. The free energy landscape (FEL) of the complex has been determined using NMR residual dipolar couplings in replica-averaged metadynamics simulations (see further), showing the coexistence of three low energy states, comprising the absolute minimum and two intermediates (the one with the highest energy corresponding to the transition state of the entire system). A campaign of high-throughput screening against ∼100,000 drug-like small molecules (Ganser et al., 2018) has been performed on this target, revealing again the superiority of RDC-mediated ensembles that enrich libraries with true hits. Chang et al. (2020) resolved with the help of RDCs the structure of a microRNA (miRNA) that contains the tandem UU:GA base pair mismatch and used it for a drug-discovery purpose. This mismatch motif occurs in the helices of large rRNA subunits of several organisms, including human pathogens like HIV (Bilbille et al., 2009), making it an optimal target for pharmaceutical purposes. Due to the presence of the mismatch, the structure is very flexible, and it has been resolved with an extensive usage of molecular dynamics (MD) (see further) simulations and further refined with residual dipolar coupling constants to generate an ensemble of structures against which a virtual screening of 64,480 small molecules was performed to identify candidate compounds specifically bound by the motif. This allowed the identification of a single compound (2-amino-1,3-benzothiazole-6-carboxamide, ZN423), which binds the miRNA with moderate affinity but includes the flanking A-U and G-C base pairs in the interaction. These base pairs contribute to the structural and/or dynamic features necessary to ZN423 binding. Moreover, the finding that specificity for the UU:GA mismatch is dependent on the flanking sequence demonstrates the importance of context effect and increases the possible number of small non-canonical features that can be specifically targeted.
Small Angle X-rays scattering (SAXS) provides an accurate and diverse set of parameters that describe biomolecules, including global information about macromolecular size and shape, intermolecular association, domain motion and linker flexibility (Table 2). Recently, SAXS emerged as a prominent technology to facilitate research and quality control at different steps of drug development, i.e., providing a fingerprint of complexes formation, as well as of drug delivery systems. Being sensitive to the complex size, SAXS is particularly suited for the characterization of large drug molecules, like RNA, allowing the analysis of single components signals as weighted shape/structure. During SAXS experiments, X-rays are scattered at the electrons of the inner atomic shells. Upon interaction a spherical wave is emitted with conserved energy and wavelength and its intensity is proportional to the electronic density at nuclear position within a structure. Such intensity can be interpreted via readily available computational tools to model structures from SAXS data (Chen and Pollack, 2016); more recently developed computational methods can be applied to model the complex solution environment around RNA (Nguyen et al., 2014; Yang, 2014). Similar principles are at the basis of small angle nuclear scattering (SANS), in this technique neutrons are scattered by nuclei and substantially exhibit a wave-like behavior (Lapinaite et al., 2020). Isotopes of the same element can have very different neutron scattering properties, allowing contrast experiments, i.e., the replacement of H2O with D2O (deuterated water). Contrast variation methods for neutron scattering are perhaps more effective to implement as they involve replacing H2O with D2O and enable “blanking” of either the protein or nucleic acid components of the sample. Contrast variation SAXS presents unique opportunities for studying the dynamics of RNA–protein complexes, for example adding sucrose to the buffer to increase its electron density to equal that of the protein, ensuring significant scattering of the RNA even above this electron dense background. The contrast variation method has recently been applied to study DNA–protein complexes, where the use of high intensity X-ray sources enables time resolved studies (Chen et al., 2014). SAXS can be used to investigate dynamics of nucleic acids, except for the experimental characterization of complex formation and hydrodynamics. Dynamics of biological relevant nucleic acids has been investigated (Chen et al., 2014) building models of nucleosome core particles (NCPs) based upon time-resolved SAXS experiments, that addressed the symmetry of DNA release from the histone core and the interaction of histones with the DNA during unwrapping. This study clearly revealed the asymmetric nature of NCPs disruption and provided a bead models of DNA spatial organization, and an estimate of a timescale (200 ms) for the residence time of proteins in contact with DNA molecules. The vast majority of SAXS-based studies performed on nucleic acids are coupled with complementary experiments that provide dynamic details of the complexes formation, mainly MD (see further), mass spectrometry and nuclear magnetic resonance spectroscopy (Szameit et al., 2016; MacOšek et al., 2021). The usefulness of SAXS as an analytical tool for the characterization of RNA as drug has been demonstrated by Szameit and colleagues, who reported a study about protein-interacting RNA aptamer (Szameit et al., 2016). They studied the structure and target interaction properties of two RNA aptamers, AIR-3 and its truncated form AIR-3A, specific for human interleukin-6 receptor (hIL-6R), a key player in inflammatory diseases and cancer, recently exploited for in vitro drug delivery studies. AIR-3 (with its variants incorporating gemcitabine) and AIR-3A were investigated by different methods including RNA structure probing, SAXS and microscale thermophoresis. SAXS experiments allowed the determination of the overall shape (hence, partially, of dynamics) of self-recognition during AIR-3/A assembly’s formation, providing an indication of its dimeric state in the apo- and -holo- forms with the help of MD simulations.
Chemical reactivity of flexible molecules is influenced by inner dynamics, and it is also altered by binding. In the last decade, RNA profiling is employed for characterizing the inner dynamics and topology of RNA, both as single molecule and during molecular recognition. This is challenging, but a promising development in the field of RNA-based drug discovery, offers the possibility to comparatively monitor the effect of RNA binders, flexibility, and selectivity, including several quantitative interpretations of reactivity, in a relatively fast and direct way. RNA chemical probing is a versatile technique that can be used to elucidate RNA secondary and tertiary structures as well as RNA-ligand interactions at single-nucleotide resolution in the solution state (Stern et al., 1988; Waduge et al., 2019) (Table 2). Specific positions in a folded RNA are exposed to chemical agents, according to the local conformation and/or base-pairing patterns. Chemical probing can be broadly categorized into two groups depending on the nature of the chemical reaction of the probing agent: 1) base-specific attacks that are predominant in single-stranded regions of the folded RNA, and 2) non-base-specific attacks that are due to the ability of nucleotides, particularly in flexible regions of the folded RNA, to sample favorable conformations. Among the known chemical probes, dimethyl sulfate (DMS) is a particularly useful base-specific reagent that covalently modifies both purines and pyrimidines methylating N1 position of adenine, N3 position of cytosine and N7 position of guanine. Therefore, DMS can be used to examine Watson-Crick base-pairing accessibility of adenine or cytosine, or Hoogsteen base-pairing accessibility of guanine in solution. In addition, DMS can be utilized in a broad pH range and other solution conditions without effects on the reactivity. Regions with specific secondary structures within a folded RNA molecule can be identified by studying the DMS modification patterns, also as a function of different environment conditions (Peattie, 1979). Otherwise, DMS probing is very useful to identify regions within folded RNA that change conformation or are protected upon ligand binding, allowing the mapping of protein-RNA or RNA-RNA interactions DMS-based chemical probing supports the determination of secondary and tertiary structures of nucleic acids, while the information about the internal dynamics and accessibility of single nucleotides is normally conveyed by SHAPE (selective 2-hydroxyl acylation analyzed by primer extension) chemistry (Smola et al., 2015). Some studies (Gherghe et al., 2008; Hurst et al., 2018; Mlýnský and Bussi, 2018) suggested that SHAPE reactivity is correlated with nucleotide flexibility and to the extent to which a nucleotide is constrained by base pairing or other interactions. The RNA secondary structure prediction problem is reformulated using quantitative nucleotide-resolution SHAPE information in concert with thermodynamic parameters for RNA folding, by placing effective constraints on the possible structures in a conformational pool generated by computational modeling software (Zhou et al., 2021). Interestingly, SHAPE and DMS reactivities have demonstrated to be similar regardless of the probing method, especially for long non-coding RNAs [lncRNA, e.g., Malat1 and Braveheart (Bvht)]. SHAPE-MaP is a further extension of the SHAPE reactivity coupled with the mutational profiling of the RNA under investigation. The approach exploits conditions that cause reverse transcriptase to misread SHAPE-modified nucleotides and incorporate a nucleotide noncomplementary to the original sequence in the newly synthesized cDNA. The positions and relative frequencies of SHAPE adducts are thus immediately, directly, and permanently recorded as mutations in the cDNA primary sequence, thereby creating a SHAPE-MaP. In a SHAPE-MaP experiment the RNA is modified under denaturing conditions to control for sequence-specific biases in detection of adduct-induced mutations. This technique has revealed its power and flexibility in different directions ranging from the structural mapping of RNA of known structure to the topological analysis of an integral genome. The former analysis has been performed on Escherichia coli thiamine pyrophosphate (TPP) riboswitch aptamer domain in the presence and absence of saturating concentrations of the TPP ligand: SHAPE-MaP confirmed the known reactivity pattern for the folded, ligand-bound RNA and accurately reported nucleotide-resolution reactivity differences that occur upon ligand binding. The latter has been performed on the HIV-1 RNA genome, thus updating, and completing its high-resolution topological model. An extension of this technology has been proposed for its performance in live cells (Smola and Weeks, 2018): it can be applied in cell types ranging from bacteria to cultured mammalian cells and is compatible with a variety of structure-probing reagents, informing new biological hypotheses and emphasizing downstream analyses that reveal sequence or structure motifs important for RNA interactions in cells. A recent publication (Kim et al., 2020) proposed the use of SHAPE and DMS reactivity and of SAXS for determining the topology, the structure and the binding property of the 660 nucleotides long non-coding RNA (lncRNA) Bvht involved in the epigenetic control of cardiac development. This approach partially answers to the difficulty to predict large RNA structures due to the coexistence of several similar-but-different structures within 1–2 kcal/mol and to a strong influence of kinetic processes and/or protein–RNA interactions that may modulate RNA folding. Noteworthy, SHAPE/DMS results are used as spatial restraints to model the dynamic behavior of RNA at atomic level; besides a model of interaction between the regulator protein CNBP and Bvht, with the AGIL 5′ domain of RNA molecule is described based on the different SAXS curves. This study suggests that CNBP binding requires multiple domains of Bvht, with a major role of RHT/AGIL RNA motif. Moreover, in a more methodological sense, it demonstrates how even low resolution data together with RNA reactivity can support the dynamic description of structure-function relationship in RNA molecules.
Because of the rapidly expanding interest in the RNA field, the throughput of experimental tools has rapidly become a bottleneck in our ability to quantitatively and predictively correlate RNA structure, function, and dynamics. In this respect, a variety of computational tools have been developed since the beginning of the extensive investigation of RNA spatial organization (Figure 3; Table 2). These tools provide a valuable help in interpreting the available experimental data, by adding connections between structure, dynamics, and function, and by generating experimentally testable hypotheses. If this is a longstanding process in “standard” drug discovery approaches (i.e., small molecules with few degrees of freedom binding to proteins/receptors), the complexity is higher for RNA due to its structural heterogeneity as demonstrated by spectroscopy. This fostered the development of a variety of computational techniques with different levels of precision and accuracy necessary for interpreting experimental observables from different sources. Moreover, this allowed the inclusion of population-averaged interpretation of observables, above mentioned as an asset of the most modern drug discovery methodologies, in the drug discovery practices. MD has emerged as the most versatile technique for the integration of the available experimental techniques with numerical physical models of structure and dynamics. The main goals of atomistic MD simulations are the simultaneous simulation of structure and dynamics of RNA molecules explicitly and in real time to support the interpretation and planning of experimental measurements of such dynamics. At the same time, they provide reliable and experimentally testable predictions and insights that are not obtainable by current experimental methods. Due to the approximations required in MD simulations [see (Sponer et al., 2018) for details], experimental data can sometimes only qualitatively be reproduced or predicted, but even in these cases can be used to design/drive new experimental design. Due to the involvement of metal centers in the RNA reactivity (e.g., enzymatic activity), MD simulations may be complemented by quantum mechanical (QM) calculations, leaving to molecular mechanical (MM) the treatment of distal layers of the RNA that are not directly involved in forming or breaking covalent bonds, to provide context and to capture the impact of conformational dynamics on the reaction (Banáš et al., 2009). Despite the high statistical quality of such studies, they cannot capture the overall dynamics of large objects in large time windows, which is the main goal of the present section of this review. Coupled classical MD and QM/MD shed light on ribozyme catalytic mechanism, which involves manganese dicationic ion (Casalino et al., 2016). In the protein and protein-protein interactions field, standard MD simulations have been considered a gold standard methodology for the inspection of biomolecules plasticity and their role in molecular recognition, for many years. Concerning RNAs, the high heterogeneity of the transitions timescales disallows the straightforward application of the aforementioned methodologies. Thus, enhanced sampling methods are of strong interest to overcome complicated energy barriers and to efficiently explore a rough free energy surface, simulating events that occur in the range of biologically and thermodynamically relevant RNA motions timescale. Indeed, MD simulation has been recently employed to overcome the high costs and time-consuming SELEX process, as well as to study the interaction mechanism between the aptamer and the target molecule on the atomic scale (Su et al., 2021). In general, the standard MD simulations, for the study of recognition dynamics, are relatively often used for RNA, but always coupled to other techniques like free energy perturbation or MMPBSA calculations (Chen J. et al., 2019) or steered MD (Levintov and Vashisth, 2020), the details of which are beyond the aim of the present review. Among the others, three most popular classes of enhanced sampling methods, (and here reported as of interest for the general reader) commonly used in simulative molecular disciplines are Markov state models (MSM), replica exchange simulations (RES) and metadynamics (MetaMD) (Sponer et al., 2018). As previously reported for the experimental methodologies (Section 4), we highlight the most relevant and general aspects of works in the field without specific references to the role played, either of the target or of the drug, due the low number of studies in the field that see the RNA in the role of a drug.
The huge increase in the computational power available at relatively low expense allowed in the recent years the performance of extensive MD simulations that can be analyzed in terms of states and their relative population. A transition probability matrix can be constructed from MD simulations providing the probability of observing transitions between pairs of population clusters, by means of discrete jumps (Husic and Pande, 2018). The system is modeled as a Markov chain between the microstates, governed by the transition probability matrix, so that the probability distribution of the microstates at time depends only on the distribution of microstates proceeding them. Being already successfully applied to the study of many proteins systems (Shukla et al., 2015; Sirur et al., 2016; Konovalov et al., 2021), they are now starting to gain their space also in the world of RNA-related systems. Markov State Models (MSM) have been used in a smart way to reveal that RNA polymerase II (Pol II) translocation is driven purely by thermal energy and does not require the input of any additional chemical energy. Due to the very long timescales involved in the process of translocation, the authors applied a morphing method (i.e., the Climber algorithm) to the available experimental structures of Pol II. This led to the identification of four metastable states along the Pol II translocation pathway, two already known and two newly identified metastable states, and the entire free energy landscape of the whole cyclic process alongside the populations of the states and their interconversion kinetics. This thermodynamic and kinetic characterization of the system led to the comprehension of the role of the mutual internal rearrangement dynamics of a helix and a loop (namely, BH and TL) during the entire translocation mechanism. Few examples can be found in the literature on the application of the MSM to RNA-ligand complexes. A notable exception is constituted by the study of the structural diversity and dynamics of a theophylline-binding RNA aptamer in its unbound state (Warfield and Anderson, 2017). MSM have been used to characterize the ensemble of conformations that the aptamer adopts in the absence of theophylline and their interconversion kinetics. Via additional docking calculations, the theophylline binding site is found only in one ensemble of conformers accounting for one quarter of the overall population, whereas most of them are binding-incompetent, lacking a binding pocket that can accommodate theophylline. Moreover, the complete theophylline binding pathway has been simulated, supporting prior experimental observations of slow theophylline binding kinetics by showing that the binding site must undergo a large conformational rearrangement after the aptamer and theophylline forms an initial complex, namely, the rearrangement of a single base from a buried to a solvent-exposed orientation. Another example of the application of MSM applied to RNA complex, to characterizing the RNA fraying kinetics, is reported in Pinamonti et al. (2019). Here the authors set up a protocol to study the kinetic and the thermodynamic of fraying process, and reproduce the kinetic properties of an RNA double helix in qualitative agreement with experimental data.
In replica exchange schemes (RE) numerous replicas of the system are simultaneously simulated using different parallelization parameters (e.g., temperature or potential energy function) and, periodically, exchanges are attempted between them according to a Metropolis criterion (Metropolis et al., 1953). The most common schema employs replicas at different temperatures (T-REMD): at a higher temperature a system crosses more easily energy barriers and explores more efficiently its conformational space than a system at low temperature, consequently the ground state replica sampling is enhanced (simulated annealing principle). The replica exchange protocol can be generalized to methods where ergodicity is not obtained by increasing temperature but by scaling portions of the force field or adding penalty potentials disfavoring specific structures (e.g., by biasing/flattening potentials along selected dihedral angles). These methods are generally known as Hamiltonian replica exchange (H-REMD) methods since the different replicas use different Hamiltonian functions. They have been extensively used to characterize proteins and nucleic acids [for a more extended reference see (Sponer et al., 2018)], but only recently their application to RNA dynamics and recognition emerged in literature [e.g., in the determination of RNA stem loops dynamics in the 5′-UTR of SARS-CoV-2, (Bottaro et al., 2021)]. High temperature simulations alone have sometimes been used qualitatively to enhance sampling of RNA systems; however, they cannot be used to directly estimate the values of experimental observables at physiological temperatures. H-REMD sampling technique has been used by Pourjafar-Dehkordi and Zacharias (2021) to specifically accelerate domain motions of the Thermus Thermophilus Argonaute (TtAgo) system, a protein combined with a short microRNA (miRNAs) that can target mRNA molecules for translation inhibition or degradation and play a key role in many regulatory processes. In particular, the system has been studied in apo, guide bound, and guide/target bound states with H-REMD revealing the opening dynamics of the structure that can lead to accommodation of nucleic acids (namely DNA). The same outline has been almost followed for the characterization of the most common RNA binding domain in eukaryotes (Bochicchio et al., 2018), RRM (RNA Recognition Motif), with a comparison between free RNA (pre-miR20b) and RRM-bound RNA (Rbfox•pre-miR20b). The study has a twofold merit: methodologically it offers a quantitative comparison between NMR experimental and ensemble-averaged computed observables for the native system, providing a benchmark for the computational studies of this kind against experimental data; moreover, it proposes a very interesting and sound possibility of generating an RRM multiple mutant (R118D, E147R, N151S and E152T) able to bind RNA molecules that also contain point modifications with respect to the original template, i.e., in the present work the miR21 precursor (G28U, C30A and G33C). The last task required H-REMD simulations to eliminate any bias caused by the initial building up of the mutated structure. The study of the new complex revealed, despite the overall similarity between the two complexes, local changes in hydrogen bonding and an overall net thermodynamic stability of the system, suggesting a way to rationalize this type of molecules in a pharmaceutical perspective.
In metadynamics (Laio and Parrinello, 2002), a bias potential is added to compensate the underlying free-energy barriers along a preselected collective variable (CV), i.e., a predefined descriptor of the molecular system capable of discriminating the transition state and of enhancing the transition probability. More recently, the idea of constructing a potential adaptively during the simulation has been added to the original formulation of metadynamics. Several methods of this type have been proposed (Sponer et al., 2018). A recent interesting application of MetaMD has been illustrated for the characterization of the RNA targeting by Peptide Nucleic Acids (PNAs), one of the most established and efficient artificial systems for targeting DNA or RNA so far described (Verona et al., 2017). PNAs compounds are nucleic acid analogues in which the deoxyribose phosphate backbone is substituted by a polyamidic chain of N-(2-aminoethyl)-glycine units. Due to their high affinity for DNA and RNA, and to the high sequence-selectivity of their interaction with complementary nucleic acids, PNAs are widely used as probes for the recognition of specific DNA sequences and, conjugated to surfaces or to reporter groups, for diagnostic application or imaging (Nielsen, 2004). In drug development, PNAs have been used for blocking transcription of genomic DNA or for preventing translation of mRNA into proteins. PNAs:RNA duplexes stability has been evaluated, starting from the reconstruction of the free energy surfaces of a simplified single strand PNA and its chemical variant named g-PNA, demonstrating also in this case the extreme flexibility of the species and the existence of multiple conformers within 10 kcal/mol that could be accessible during the binding, some of them being already in a configuration ready to form a helix with a natural target oligonucleotide.
Although the feasible time scales of biomolecules all-atom MD simulations have impressively increased in recent decades, and enhanced sampling methods allow deep exploration of the free energy surfaces of large molecules, there are many phenomena that still cannot be efficiently studied by atomistic MD. Biologically interesting processes, such as the folding of RNA structures or the domain motions of ribosomal subunits during translocation, can occur time scales of the order of milliseconds or longer, and might also involve millions of atoms or more. One alternative approach to all-atoms simulations is the use of simplified coarse-grained (CG) descriptions, i.e., the reduction of the degrees of freedom of the original atomistic system by grouping selected sets of atoms and representing them as a smaller set of CG particles (beads) that interact through effective energy functions. This reduction reduces the complexity of the calculation, and, in general, defines a smoother energy surface, which can be explored more quickly, allowing larger systems and longer simulation timescales. Because of the used approximations, CG models will generally only faithfully reproduce a specific set of observables in a limited region around the conditions (thermodynamic and non-thermodynamic) assumed in their parameterization. Therefore, these approaches must be used with care, and may sometimes require a redefinition of the observables of interest based on the model resolution. However, CG simulations can provide important insights that complement atomistic MD simulations or contribute to interpret experimental observations. Many codes are currently available for performing simulations of RNA applying the coarse-grained scheme [OxDNA (Šulc et al., 2012), SimRNA (Boniecki et al., 2016), MARTINI (Souza et al., 2021)], some of them has been also recently used together with experimental data, for sampling conformational variability related to the reproduction of experimental observables [ERNWIN, Bvht (Kim et al., 2020)]. However, there is a plenty of room to apply these methods to RNA complexes. Nevertheless, parametrization of intramolecular interactions at the CG level is quite difficult, especially for protein-nucleic acid complexes, due to the low availability of experimental structures ready to be used for correctly setting up the corresponding force-field. Despite the increasing computational power at lower cost and the high quality of all the aforementioned methods, it is still impossible to simulate at atomic level large flexible systems of biological interest (e.g., lncRNAs) with high level of accuracy, which requires further development.
The computational dynamics of nucleic acids has been recently successfully coupled to experimental techniques to compensate their intrinsic uncertainties or to properly reconstruct structural ensembles linked to physical observables that depend on the coexistence of several states at the thermal equilibrium. The techniques that have been most efficiently coupled to MD simulations are NMR and SAXS, with a synergy that has been largely inspired by previous similar studies on protein systems. A recent example, is an application of SAXS-derived penalty functions to replica exchange MD simulations based on the maximum entropy principle (He et al., 2022). The application of this principle ensures that SAXS-driven simulations can be successfully used also in cases of extreme flexibility (in absence of a statistically dominant conformation). Interestingly, the authors propose a downstream comparison with experimental FRET data of one of the considered systems (HJH). Moreover, an extensive analysis is reported about the sensitivity of the method to detect the Mg2+ ions binding sites on the surface and in the RNA cavities. This topic has been recently covered by a similar study in the field (Bernetti et al., 2021) that, using MetaMD and reconstructing SAXS spectra a posteriori, demonstrated how explicit-solvent SAXS spectra are necessary to correctly reconstruct the ion-dependent structural ensembles. A particular computational focus is put by Chen P. C. et al. (2019) on the usage of data from multiple sources, combining information obtained by small angle scattering experiment from different sources, i.e., X-rays and neutrons, a technique that recently emerged to be increasingly important in the determination of NMR structural and dynamic organization [a multi-technique study in this sense is reported in Chen P. C. et al. (2019)]. Even if MD simulations-based refinement of SAXS data has a relatively longstanding tradition (Chen and Hub, 2015), the authors highlight the importance of integrating SANS together with SAXS to avoid overfitting problems in building ensembles of structures that are representative of experimental data and henceforth of the inner dynamics of macromolecules, especially in the study of complexes. Noteworthy, concerning the topic of the present review, the method has been proposed with a general profile for any system belonging to the soft matter field, and it includes also the refinement of the Sxl−Unr−msl2 mRNA ternary complex (SUM), that plays an important role in female Drosophila flies to maintain equal expression levels of X chromosome linked genes between the sexes, previously studied by X-ray crystallography and SAXS experiments and then refined using neutron scattering. As well as, combined with MD, to study aptamer LC-18, designed for the recognition of lung adenocarcinoma cells and identify its functional truncated portion (Morozov et al., 2021). Aside this more global structural aspect of nucleic acids dynamics and molecular recognition, some recent study demonstrated that SHAPE reactivity can be efficiently used in conjunction with MD simulations. A recent work by Hurst et al. (2018) established the possibility of correlate multiple key factors, such as the nucleotide interaction strength, SHAPE reagent accessibility, and base-pairing pattern to build an analytical semiempirical function, namely the three-Dimensional Structure-SHAPE Relationship (3DSSR) function, to characterize the conformational flexibility and SHAPE reactivity based on the conformational and energetics information. Even if only conceiving a future direct involvement of SHAPE experimental data, Chen and coworkers (Ebrahimi et al., 2019) established a multiplexed method that makes usage of secondary structure restraints incorporated in a bidimensional grid of replicas, to accurate predict RNA tertiary de novo fold. The central philosophy of this method is to conservatively restrain only unambiguously assigned regions of RNA secondary structure to increase the efficiency of finding the most stable tertiary structure configuration. Incorporating even a small number of long-range native contacts, dramatically reduces the conformational space to be sampled by the simulation model.
Prior to physicochemical characterizations of intermolecular interactions, the identification of small molecules that specifically bind to the RNA of interest is of paramount importance. For metabolite sensing RNA riboswitches, cognate ligands are often identified, validated, and annotated during their biochemical characterizations. For other RNAs of interest, disease-linked regulatory RNAs, RNA-binding small molecules are often identified from a large pool of chemical libraries via high-throughput screening (HTS). Despite having lower throughput relative to HTS, NMR spectroscopy is also a powerful tool for identifying and validating small molecules that interact with biomolecules and has played a significant role in protein-targeted drug discovery. Excellent reviews have been published in recent years (Thompson et al., 2019), which provide thorough discussions of various NMR experiments for identifying protein-binding small molecules as well as evaluating strengths and liabilities of individual methods. Since many of these methods are based on observing ligand NMR signals, the nature of a target, whether it is a protein or an RNA, has minor influence on experimental setups of these methods, enabling their direct applications in RNA-binding small molecules identification. A recent NMR based fragment-screening approach has been employed to identify small molecules able to target SARS-CoV-2 regulatory RNA elements, known as stem loops, to develop new, and more specific, antivirals (Sreeramulu et al., 2021). Saturation transfer difference (STD) NMR spectroscopy is one of the most widely used NMR methods in drug discovery, such as fragment-based drug discovery (FBDD) screening for protein targets. STD experiments build upon magnetization transfer between biomolecules, such as proteins or RNAs, and small ligands (Wagstaff et al., 2013). If a ligand binds the biomolecule, its NMR signals can also be saturated due to intermolecular nuclear Overhauser effect (NOE) linked to spin relaxation phenomena. In contrast, for any ligands that do not interact with the biomolecule, their NMR signals are minimally affected by the irradiation of biomolecular NMR signals. The difference between saturated and unsaturated spectra reveals the ligands that bind the biomolecule. STD experiment can efficiently screen a pool of small molecules and identify binding-competent ligands. Despite its wide usage for proteins, STD is less viable in screening RNA-binding small molecules, but it has been successfully employed to characterize RNA-binding small molecules (Mayer and James, 2002; Vasile et al., 2014; McRae et al., 2020). WaterLOGSY (water-ligand observed via gradient spectroscopy) is a ligand-observed NMR technique that can be used in target-directed drug screening or ligand validation to assess the binding of molecules to macromolecules (Calabrese et al., 2019). WaterLOGSY effects are achieved by applying indirect magnetic saturation of the macromolecule by selective saturation of bulk water protons. Magnetization is first transferred from water to labile (exchangeable) RNA protons, that are proximal to the ligand binding site and then to the compounds interacting with the RNA. WaterLOGSY has been widely used for detecting macromolecule-ligand interactions for RNA, DNA, and proteins because it is efficient and easy to interpret also in the context of ligands ranking among libraries. WaterLOGSY is very fast and requires a low RNA quantity. Moreover, WaterLOGSY experiments do not require RNA or small molecule labeling. Compared to small RNAs, larger RNA structures have longer rotational correlation times which results in a more efficient magnetization transfer, leading to stronger signals. (Thompson et al., 2019).
In fragment-based ligand discovery one or more small-molecule “fragments” of low molecular mass (200–400 Da) and low to moderate affinity are identified that bind a target of interest, and these fragments are then either elaborated or linked to create more effective ligands (Erlanson et al., 2004). Fragment-based ligand discovery can be also performed in silico, further reducing time and costs of ligand building (Warner et al., 2014; Li, 2020). This method has been successfully employed to identify initial hit compounds for RNA binding, however to date, fragment-based methods are not used to create a high-affinity RNA-targeting compound de novo. Zeller and coworkers have recently shown that many RNAs bind their ligands via multiple subsites, which are regions of a binding pocket that contact a ligand in an independent or cooperative manner (Zeller et al., 2022b). Besides high-affinity RNA binding can occur even when subsite binding shows only modest cooperative effects and when the linking coefficient is unfavorable. Identification of multiple fragments that bind the same RNA would make it possible to take advantage of potential additive and cooperative interactions between fragments within the binding pocket. Zeller et al. (2022a) developed a technology that leverages fragment-based screening and SHAPE-MaP RNA structure probing to discover small-molecule fragments that bind an RNA structure at roughly nucleotide resolution. A modular RNA screening, tested on the thiamine pyrophosphate (TPP) riboswitch, has been developed implementing SHAPE as a high-throughput assay for readout of ligand binding. The construct was designed to contain two target motifs to ensure internal mutual activity control. i.e., a pseudoknot from the 5′-UTR of the whole genome (dengue) and the TPP riboswitch aptamer domain. These two structures were connected by a six-nucleotide linker, designed to be single stranded, to allow the two RNA structures to remain structurally independent. Fragments that bound to both RNA structures were easily identified as nonspecific binders. Fragment hits are identified as multiple, statistically significant differences in SHAPE reactivities. 41 fragments out of 1,500 tested has been identified as binders and further characterized by isothermal titration calorimetry (ITC) to determine binding affinities for an RNA corresponding just to the target motif. Structure-activity relationship and ITC information eventually led to a linked-fragment ligand with no resemblance to the native binders and with high ligand efficiency and drug-likeness, that binds to the TPP riboswitch with high nanomolar affinity and that modulates RNA conformation during co-transcriptional folding.
Aptamers are generated using an iterative selection process that partitions oligonucleotides based on their binding or functional/catalytic activities through a process of directed chemical evolution called SELEX (Urak et al., 2016). A typical SELEX experiment consists of the enrichment of highly specific nucleotide sequences. The first step is to synthesize a random sequence DNA library (1012–1015) of ∼20–100 nucleotides containing flanking constant sequences required for PCR amplification. For RNA SELEX (Urak et al., 2016), the single stranded RNA library for each round of selection is prepared by in vitro transcription of dsDNA templates using T7 RNA polymerases. Modified nucleotide triphosphates (NTPs) are typically incorporated during the selection process to reduce the possibility of post-selection modifications impairing aptamer function (Urak et al., 2016). Recent breakthroughs in SELEX are the application of NGS technologies which, together with bioinformatics analysis, expedite the identification of finally selected sequences and allow tracking of aptamer evolution also in cells (cell-SELEX) (Sefah et al., 2010). Small molecules are attractive targets for aptamer selection, as these aptamers can be used as biosensors, as recognition modules in riboswitches or even as antidotes in drug usage. However, unlike for larger complexes such as proteins, the selection of small molecule aptamers has always been challenging due to the limited number of interaction moieties for chemical immobilization of baits on a matrix and the highly denaturing conditions (e.g., extreme pH, use of solvent etc.) of chemical immobilization that may compromise the target molecule before even starting the selection process. The difficulties in the selection of small molecule binding aptamers, can be avoided by a target-immobilization free protocol called Capture-SELEX (Boussebayle et al., 2019), where the roles of pool and target are inverted. The aptamer pools are immobilized through a capture-oligonucleotide used as an anchor. To elute aptamers from their support, the free ligand molecule is incubated with the immobilized pool, thus undocking aptamers from the capture-oligonucleotide. As a result, only aptamers able to bind the original unmodified free ligand are generated.
SAXS-based RNA screening (SAXScreen) procedure has been used to categorizes ligand titrations by computing pairwise agreement between scattering curves and by estimating affinities through the quantification of complex formation as deviation from the linear combination properties of solution SAXS (Chen et al., 2018). The resulting workflow for SAXScreen ranks putative interactions based only on intensities. To reduce RNA synthesis costs, it has been assumed that all ligands share a comparable binding mechanism, hence all the screening process has been based on the minimization of a single cost function containing weighted signal intensities of all the chemical species involved in the complex formation. This translates into an immediate and easy identification of the processes that deviate from a two-state binding and into a simultaneous curve fit, which produces a final ligand ranking based on dissociation constant, aside the recording of particle volume, i.e., radius of gyration. The optimized protocol allows up to 1,000 measurements per day, corresponding to 100 titrations depending on the desired precision of affinity estimation. An increase of 1–2 orders of magnitude is expected in the near future with improvements to beamline setups aimed at reducing downtime between measurements.
To overcome the difficulties to predict a reliable three-dimensional conformation of nucleic acids and to rapidly analyze their binding to target molecules, computational docking is a rapid and low-cost method to screen potentially interesting molecules. Nevertheless, docking of RNA molecules is different to docking of small molecules to proteins. First, the flexibility is an important component of the binding process and usually in docking simulations protein and ligand are considered as rigid, to speed up the computation. MORDOR is (probably the only) one example of induced-fit binding via flexible-RNA and flexible-ligand docking (Guilbert and James, 2008). Besides, RNA is a negatively charged molecule and charged ions are components of the system; ions need correctly parametrized force fields to be properly simulated. Force fields specific for nucleic acids are used in MD but they are not easily included in docking software. The electrostatic potential distinguishes proteins from nucleic acids, whereas docking software implement electrostatic potentials peculiar for protein-ligand complexes. Scoring functions specific for RNA-ligand have been developed since 2004 (RiboDock, DrugScoreRNA, rDOCK, etc.) (Morley and Afshar, 2004; Pfeffer and Gohlke, 2007; Ruiz-Carmona et al., 2014), until recently NLDock, RLDock, AnapuRNA and LigandRNA (Philips et al., 2013; Sun et al., 2020; Feng Y. et al., 2021; Stefaniak and Bujnicki, 2021), all of them are empirical or knowledge-based scoring functions. For this reason, the knowledge of structural information based on crystallographic data alone and in complex is fundamental, also to understand the features of this type of interaction. Actually, just some examples of RNA-protein docking are available; Chauvot de Beauchene group used a fragment-based approach and docking to design ab initio a ssRNA (Chauvot de Beauchene et al., 2016), and Guihot-Gaudeffroy group applied known tools to inspect RNA-protein complexes (Guilhot-Gaudeffroy et al., 2014); ad hoc scoring function are under development (Pérez-Cano et al., 2017). Besides only a few examples of RNA -RNA docking (Yan et al., 2018), could be useful, at the present stage, to improve the development of therapeutic RNAs. |
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PMC9649583 | Amany A. Alam,Doaa A. Goda,Nadia A. Soliman,Dina I. Abdel-Meguid,Ebaa E. El-Sharouny,Soraya A. Sabry | Production and statistical optimization of cholesterol-oxidase generated by Streptomyces sp. AN strain | 10-11-2022 | Cholesterol oxidase,Streptomyces sp. AN,Experimental design,Process,Optimization | Background Cholesterol oxidases (CHOs) have attracted enormous attention because of their wide biotechnological potential. The present study explores the production of CHOs by Streptomyces sp. AN. Evaluation of culture conditions affecting enzyme production, medium optimization and released metabolite characteristics were also investigated. Results The current work reports the isolation of 37 colonies (bacteria/actinobacteria) with different morphotypes from different soil/water samples. The isolate-coded AN was selected for its high potency for CHO production. Morphological characteristics and the obtained partial sequence of 16srRNA of AN showed 99.38% identity to Streptomyces sp. strain P12–37. Factors affecting CHO production were evaluated using Plackett-Burman (PB) and Box-Behnken (BB) statistical designs to find out the optimum level of the most effective variables, namely, pH, starch, NH4NO3 and FeSO4.7H2O with a predicted activity of 6.56 U/mL. According to this optimization, the following medium composition was considered to be optimum (g/L): cholesterol 1, starch 6, MgSO4.7H2O 0.1, CaCl2 0.01, FeSO4.7H2O 0.1, NH4NO3 23.97, yeast extract (YE) 0.2, K2HPO4 0.01, KH2PO4 0.1, NaCl 0.01, Tween 20 0.01, pH 6.36 and incubation temperature (30 °C) for 9 days. Spectophotometric analysis for released metabolites against cholesterol (standard) via Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) was carried out. FTIR spectrum showed the appearance of new absorption peaks at 1644 and 1725cm−1; this confirmed the presence of the Keto group (C=O) stretch bond. Besides, fermentation caused changes in thermal properties such as melting temperature peak (99.26; 148.77 °C), heat flow (− 8; − 3.6 Mw/mg), capacity (− 924.69; − 209.77 mJ) and heat enthalpy (− 385.29; 69.83 J/g) by comparison to the standard cholesterol as recognized through DSC thermogram. These changes are attributed to the action of the CHO enzyme and the release of keto derivatives of cholesterol with different properties. Conclusion Streptomyces sp. AN was endowed with the capability to produce CHO. Enzyme maximization was followed using a statistical experimental approach, leading to a 2.6-fold increase in the overall activity compared to the basal condition. CHO catalyzed the oxidation of cholesterol; this was verified by the appearance of a new keto group (C=O) peak at 1644 and 1725 cm−1 observed by FTIR spectroscopic analysis. Also, DSC thermogram demonstrates the alteration of cholesterol triggered by CHO. | Production and statistical optimization of cholesterol-oxidase generated by Streptomyces sp. AN strain
Cholesterol oxidases (CHOs) have attracted enormous attention because of their wide biotechnological potential. The present study explores the production of CHOs by Streptomyces sp. AN. Evaluation of culture conditions affecting enzyme production, medium optimization and released metabolite characteristics were also investigated.
The current work reports the isolation of 37 colonies (bacteria/actinobacteria) with different morphotypes from different soil/water samples. The isolate-coded AN was selected for its high potency for CHO production. Morphological characteristics and the obtained partial sequence of 16srRNA of AN showed 99.38% identity to Streptomyces sp. strain P12–37. Factors affecting CHO production were evaluated using Plackett-Burman (PB) and Box-Behnken (BB) statistical designs to find out the optimum level of the most effective variables, namely, pH, starch, NH4NO3 and FeSO4.7H2O with a predicted activity of 6.56 U/mL. According to this optimization, the following medium composition was considered to be optimum (g/L): cholesterol 1, starch 6, MgSO4.7H2O 0.1, CaCl2 0.01, FeSO4.7H2O 0.1, NH4NO3 23.97, yeast extract (YE) 0.2, K2HPO4 0.01, KH2PO4 0.1, NaCl 0.01, Tween 20 0.01, pH 6.36 and incubation temperature (30 °C) for 9 days. Spectophotometric analysis for released metabolites against cholesterol (standard) via Fourier-transform infrared spectroscopy (FTIR) and differential scanning calorimetry (DSC) was carried out. FTIR spectrum showed the appearance of new absorption peaks at 1644 and 1725cm−1; this confirmed the presence of the Keto group (C=O) stretch bond. Besides, fermentation caused changes in thermal properties such as melting temperature peak (99.26; 148.77 °C), heat flow (− 8; − 3.6 Mw/mg), capacity (− 924.69; − 209.77 mJ) and heat enthalpy (− 385.29; 69.83 J/g) by comparison to the standard cholesterol as recognized through DSC thermogram. These changes are attributed to the action of the CHO enzyme and the release of keto derivatives of cholesterol with different properties.
Streptomyces sp. AN was endowed with the capability to produce CHO. Enzyme maximization was followed using a statistical experimental approach, leading to a 2.6-fold increase in the overall activity compared to the basal condition. CHO catalyzed the oxidation of cholesterol; this was verified by the appearance of a new keto group (C=O) peak at 1644 and 1725 cm−1 observed by FTIR spectroscopic analysis. Also, DSC thermogram demonstrates the alteration of cholesterol triggered by CHO.
CHO is the enzyme that catalyzes the oxidation of cholesterol to cholestenone (cholest-4-en-3-one), with the reduction of an oxygen molecule to H2O2 (hydrogen peroxide) [1]. In recent times, microbial CHO has received great attention due to its wider use; CHO offers a broader range of industrial uses than clinical ones. The enzyme is used to analyse steroid levels in dietary samples. CHO is also used as a biosensor to measure serum cholesterol concentrations, which is crucial for diagnosing cardiovascular disease, atherosclerosis and other lipid disorders. CHO has also been implicated in the manifestation of viral diseases HIV, bacterial diseases (tuberculosis) and Alzheimer’s disease [2]. CHO exhibits anticancer properties in vitro when tested on rhabdomyosarcoma and breast cancer cell lines. It also possesses anticancer properties in an Ehrlich solid tumour model in vivo [3]. It has considerable insecticidal activity against the larvae of the Anthonomus grandis boll weevil, which decreases cotton yield [4]. In addition, for pimaricin (natamycin) production, Streptomyces natalensis CHO was utilized [5]. Significant attention has been received by CHO due to its wider use for the detection of cholesterol in food and blood samples, which has direct implications in lipid disorders including coronary heart diseases and atherosclerosis. Additionally, CHO is used in the production of steroids. Different bacteria have been shown to be involved in cholesterol degradation, while Actinomycetes are said to be the main group of organisms that degrade cholesterol. Various bacterial species have been implicated in cholesterol biological degradation via a functionalized favin adenine dinucleotide-containing CHO that oxidizes cholesterol and creates 4-cholesten-3-one, while converting oxygen to hydrogen peroxide [6, 7]. The degradation of cholesterol by Mycobacterium, Rhodococcus, Brevibacterium, Streptomyces and some other gram-positive (G+) as well as gram-negative (G−) genera including Comamonas, Burkholderia, Pseudomonas and Chromobacterium has been reported [8–12]. Streptomycetes, like the other Actinobacteria, are G+ with a high GC content. Over two-thirds of the clinically relevant enzymes and antibiotics of natural origin are produced by Streptomycetes. The most productive source of microorganisms for all kinds of bioactive metabolites, including those with agro-active properties, is thought to be the Streptomycetes. In fact, Streptomyces is the source of nearly 60% of the novel insecticides and herbicides reported between 1988 and 1992 [13]. Actinobacteria are gaining popularity due to their low toxicity, specificity and environmentally friendly nature. However, for the development of commercially accessible Actinomycete-based products with a long shelf life, novel species must be identified as well as the mode of action of these bioagents should be further explored. Thus, the aim of the present study was to isolate cholesterol-degrading microorganisms from different Egyptian localities and identify biochemically and genetically the most potent cholesterol-degrading isolate. The aim extended to optimize the nutritional and environmental conditions of the selected isolate to reach the highest productivity of CHO. Moreover, the developed end product (substrate degraded) was monitored and simply characterized through FTIR and DSC.
Samples were gathered from various locations in Egypt (El Nubaria, West Sinai Desert and New Borg El-Arab City). Three samples were taken from El Nubaria petroleum wells at a depth of 40–50 cm, while samples from the west Sinai desert were taken at a depth of 20–30 cm. Another sample was taken from a waste oil facility and a soap factory in the City of Borg El Arab. At a depth of 15–20 cm, agricultural soil was also taken from New Borg El-Arab City. Sea water samples were also taken from two other seashores (the North coast and Marsa Matrouh City). All samples were collected in sterile 50-mL Falcon tubes, coded and kept at 4 °C until needed. Luria-Bertani (LB), composed of starch 10 g, peptone 2 g, YE 4 g and agar 15 g dissolved in 1 L distilled water, was used for bacterial isolation after adjusting the pH at 7.0 ± 0.1 [14]. One gram of soil or 1 mL of liquid was mixed with 9 mL of sterile distilled water. The diluted samples were streaked into sterile LB-agar medium poured into Petri dishes and incubated at 30 °C for 48–72 h. Morphological different colonies were selected, purified and maintained on sterile LB slants, coded and stored at 4 °C with regular transfer at monthly intervals.
Qualitative screening for CHO production was done by growing different isolates on a selective medium (SM) composed of K2HPO4 0.250 g, MgSO47H2O 0.250 g, NaCl 0.005 g, FeSO4.7H2O 0.0005 g and cholesterol 1.0 g (as a sole carbon source) dissolved in 1 L of distilled water [15]. Several isolates were streaked on agar plates and incubated in an incubator at 30 °C for 7 days. Bacterial growth was used to assess bacteria’s ability to consume cholesterol and generate CHO. Filter paper discs were soaked in potassium phosphate buffer (pH 7.0) containing 0.5% cholesterol, 6% phenol, 1.7% 4-aminoantipyrine (4-AAP), and 3 U/mL of horse radish peroxidase. The soaked discs were then placed above the grown colonies and incubated at room temperature for another 24 h. The creation of red colour quinoneimine dye indicates CHO activity [16].
Monitoring of cholesterol degradation was carried out using the Bio Med-Cholesterol-LS kit (enzymatic colourimetric method) according to the manufacturer’s instructions. This used kit contains cholesterol standard reagent coded R1 with a concentration of 200 mg/dL and R2 reagent composed of Good’s buffer, peroxidase, 4-AAP and phenol derivatives. The reaction was carried out by mixing 10 μL sample (cell-free supernatant) with 1000 μL of R2 reagent. The reaction was incubated for 5 min at 37 °C and then read at 520 nm against blank which contains media free of inoculum. The standard was prepared in the same way using 10 μL of R1. Finally, the cholesterol degradation was calculated based on the following equation:
Wali and others employed the formation of hydrogen peroxide during the oxidation process of cholesterol to measure the activity of CHO [17]. A 100 μL of the enzyme was combined with 900 μL of the assay substrate, which included 87 mM potassium phosphate buffer, 0.89 mM cholesterol, 64 mM sodium cholate, 1.4 mM 4-aminoantipyrine, 21 mM phenol, 0.34% Tween 80 and 5 U/mL horse radish peroxidase. The reaction mixture was incubated for 5 min at 37 °C, and the generation of quinoneimine dye was monitored by measuring the absorbance at λ520. The activity of the enzyme was calculated according to the following formula: where Vt is the test’s total volume (1 mL), 13.78 is the quinoneimine dye’s millimolar extinction coefficient, df is the dilution factor, and Vs is the enzyme volume (100 μL) utilized in the experiment. Under the circumstances given above, one unit produces one micromole of hydrogen peroxide (half a micromole of quinoneimine dye) each minute.
For the morphological characterization of the chosen isolate, a phase-contrast microscope (PCM) (AXIOSTA R-plus, ZEISS) was utilized. Scanning electron microscopy (SEM) was performed at 20 kV in the Centre Laboratory, City of Scientific Research and Technological Applications, using a JSM 5300 (JEOL, USA). The plate assay method was used for qualitative screening of the chosen isolate towards some distinct enzymes. Instead of starch, 0.2% of the equivalent substrate was added to the isolation medium to make agar plates. The clear zones were visualized using enzyme-specific methods. The substrates for the plate assay were carboxymethyl cellulose (CMC), starch, agarose, skim milk and tributyrin for cellulase, amylase, agarase, protease and lipase, respectively. The production of cellulase was visualized by flooding the plates with congo red solution (0.1%), incubating for 30 min, removing the dye, and then fixing the colour with 2 M NaCl solution for 20 min. The presence of a pale orange zone around the colonies indicated a good positive result, while the rest of the plate was stained pink colour [18]. The production of agarase and amylase was visualized by flooding the plates with iodine-potassium iodide solution [19–21]; this reagent gives a translucent halo region around positive (agarase or amylase) colonies while the undegraded substrate(s) appeared in black-blue colour. Protease synthesis was determined by the formation of a clear zone around the colony [22]. Finally, for lipase, Arabic gum (1%) was dissolved in distilled H2O with gentle heating, and 0.2% tributyrin was added and emulsified well using Ultraturrax T25 blinder for 10 min, mixed with the medium and autoclaved, then poured in Petri dishes and inoculated with the tested isolate. The presence of a clear halo surrounding the colonies implies lipase/esterase synthesis [23].
16S rRNA gene sequencing was used to identify the most promising isolate possessing cholesterol-degrading activity. A genomic DNA was performed according to Kumar et al. [24]. The 16S rRNA gene was amplified by polymerase chain reaction (PCR) using primers designed to amplify the full length (1500bp) of the 16S rRNA gene according to the Escherichia coli (E. coli) genomic DNA sequence. A PCR reaction was completed, then a fraction of the PCR was evaluated on a 1% agarose gel according to the method published by Sambrook et al. [25], and the leftover mixture was purified using QIAquick PCR purification reagent (Qiagen Kit). Based on the enzymatic chain terminator technique described by Sanger et al. [26], the DNA sequence was acquired using a 3130 X DNA Sequencer (Genetic Analyzer, Applied Biosystems, Hitachi, Japan). Using the nucleotide blast tool (BlASTn) [27], a nucleotide homology search was performed against 16s rRNA sequences available in the database. Multiple sequence alignment and molecular phylogeny were performed using the MEGA software version 11 [28]. This alignment was used to create a neighbour joining (NJ) tree and then a maximum parsimony (MP) tree using bootstrapping [28].
Focusing on improving culture conditions, a progressive statistical-mathematical strategy was used to enhance the process of producing extracellular cholesterol-degrading enzymes from Streptomyces sp. AN. The first was a PBD-based screening of physicochemical variables. The second was BBD to optimize the most important factors that influence the enzyme production process. The current study used an empirical design developed by Plakett and Burman, [29] to clarify the independent factors that would have a substantial impact on the performance of CHO production. In this study, a fractional factorial design of PB was used to determine whether twelve independent variables, namely NaCl, starch, YE, NH4NO3, Tween 20, K2HPO4, KH2PO4, incubation temperature, pH, MgSO4.7H2O, CaCl2 and FeSO4.7H2O, had any significant linear effect on extracellular CHO activity. In the factorial design, the JMB software generated twelve test cases, as each factor was donated into two coded levels set to − 1, the low level, and +1, the high level. To depict the anticipated linear effect imposed by the tested independent variables on the process outcome in terms of CHO activity, an ordered polynomial equation was established. where Y represents the response (CHO activity), β0 represents the model’s intercept, βi represents the tested independent variable and xi represents the tested independent variable’s estimate. All of the experiments were carried out in 250-mL Erlenmeyer flasks with a 50-mL working volume and agitation rate (200 rpm), and all trials were carried out three times. The Pareto diagram is the best way to express the PB results since it shows the absolute relative significance of variables regardless of their nature [29]. For the next optimization phase, a conformational step should be performed. Variables with negative effect values were fixed to their − 1 coded values, while those with positive effect values were fixed to their + 1 coded values. The goal of this stage is to verify the PBD results and build the basic formula for the next optimization step.
Each independent variable that had a substantial impact on extracellular CHO yield as determined by PBD was subjected to RSM using BBD [30–32]. Where the most significant four variables were selected to determine their optimal level with respect to CHO activity (U/mL) as a response, a second-order polynomial function was fitted to correlate the relationship between the independent variables and the response. The applied design matrix consists of twenty-seven trials with three runs at the centre point and three levels donated by (− 1, 0, 1) for the selected variables (pH, starch, NH4NO3 and FeSO4.7H2O), where the design was used to determine the optimal level of the significant factors for CHO production. Through the following second-order polynomial equation for the four variables, BBD concludes all potential interactions among the specified independent factors that would affect the outcome: where Y is the predicted response; β0 is the constant; β1, β2, β3 and β4 are the linear coefficients; β12, β13, β14, β23, β24 and β34 are the cross product coefficients; and β11, β22, β33 and β44 are quadratic coefficients.
Multiple linear regressions were performed on the CHO production data using the JMP tool to estimate the t values, p values and confidence levels, with the p values expressed as a percentage. The Student t test was used to evaluate the significance level (p value). The t test for any individual effect allows for an assessment of the likelihood of discovering the observed effect by chance. It will be acceptable if the probability of the variable under test is modest enough. The confidence level is a percentage representation of the p value. The JMP software was used to calculate the ideal value of the activity. Three-dimensional and contour plots were prepared by the STATISTI CA 7.0 software in order to display the simultaneous effects of the four most important independent factors on each response.
The ideal settings discovered through optimization trials were tested experimentally and compared to the model’s data.
The cholesterol metabolites formed during the breakdown process were identified using FTIR analysis against the intact substrate (undegraded). The FTIR (Shimadzu FTIR-84 00 S, Japan) is linked to a PC, and the data was analysed with the IR Solution programme, version 1.21. The scan range for each sample was 4000 to 5000 cm−1, with a resolution of 1 cm−1.
After fermentation, lyophilized samples were exposed to DSC-60A to determine their pyrolysis pattern and compared to the control (un-degraded cholesterol). The experiment was carried out in a nitrogen atmosphere with a 10 °C min−1 heating rate and a 30 mL min−1 flow rate. The thermogram was taken at temperatures ranging from 25 to 350 °C. Temperature vs. heat flow was displayed on the graph.
Among 60 obtained isolates, 37 different morphotypes (shapes/colour) were selected and subjected to the colony staining method to test for cholesterol degradation and CHO production. The strains were first screened for qualitative estimation of CHO activity on plates containing 0.1% cholesterol as the only carbon source. Five isolates showed an ability to grow in the presence of cholesterol as a carbon source. Based on the intensity of the red colour appearance, the isolates can be ordered as follows, AN > PI > MR1 > AIR2. These isolates showed the same order based on cholesterol degradation (98.02, 74.6, 41.9 and 9.6%) according to the kit assay result (full data not shown). Quantitative detection of CHO by the examined isolates depicts that all were able to produce CHO (2.54, 1.32, 0.92 and 0.6 U/mL) after 9 days of incubation. Therefore, stain AN was selected for further study, because it was recognized as the most active in relation to colour intensity (plate assay), the highest titer of CHO activity (2.5 U/ml) and the highest degradation % of cholesterol (98%) through kit assay.
Luria-Bertani media (LB) agar plates with 5 different substrates adjusted at pH 7.0 and incubated at 30 °C were prepared. By using the suitable indicator for each enzyme, it was observed that AN isolate was able to produce lipase (tributryrin-substrate) and CHO while being negative for amylase, protease and agarase.
AN was G+ characterized by the presence of creamy-brown mycelia on LB agar plates. Under SEM, mycelia formed a spore structure-like sporangium (Fig. 1). The partial sequencing of AN (971 bps) was submitted to the BLAST in order to find homologies with other relevant 16S rRNA sequences. This partial sequence of AN-coded isolate showed 99.38% identity to Streptomyces sp. strain P12-37 (AC: MT255053.1). Subsequently, the investigated AN strain was deposited in GenBank under accession number (AC: MW582104.1) and designated Streptomyces sp. strain AN. The phylogeny of the AN strain (Fig. 2) explained that it localized in the cluster included p12–37 and EG1125 strains but closer to S. violascens strain EG1125 (AC: MN704434).
PBD design (the first approach) was applied to evaluate the relative significance of cultivation variables affecting the production of CHO by Streptomyces sp. AN. In attendance, twelve different variables including nutritional factors such as carbon source (starch, Tween 20), nitrogen source (YE; NH4NO3) and salts (FeSO4.7H2O, K2HPO4, KH2PO4, NaCl, CaCl2, MgSO4.7H2O) and physical factors such as pH and temperature were evaluated. The averages of the CHO activities (response U/mL) showed a wide variation from 2.54 to 4.41 U/mL (Table 1). Based on the regression analysis shown in Table 2; the regression coefficient of the variables, namely, starch, FeSO4.7H2O, NH4NO3, MgSO4.7H2O and KH2PO4, showed a positive effect on CHO activity where cultivation temperature, pH, Tween 20, YE, K2HPO4 and NaCl contributed negatively. The 12 variables were analysed using a linear multiple regression analysis method, and the % confidence level was calculated [confidence level % = (1 − p value) × 100]. Also, the main effect was calculated basically as a difference between the average measurements of each variable made at a high level (+ 1) and a low level (− 1) (Table 2). The ranking of factor estimates is shown in a Pareto diagram (Fig. 3), where starch has the highest effect (19%) while NaCl has the lowest (0.58%). The p value from the ANOVA analysis for each response was determined to analyse the relationship between the variables. The analysis of variance using the ANOVA test gives p = 0.0052, indicating a statistically significant relationship between the variables at a 99.99% confidence level. The R-squared statistic indicates that the model as fitted explains 0.99 of the variability in CHO activity. At the model level, the correlation measure for the estimation of the regression equation is the multiple correlation coefficients R and the determination coefficients R2. The closer the value of R to 1, the better the correlation between the measured and the predicted values as shown in Fig. 3. The polynomial model describing the correlation between the 12 factors and the CHO activity could be presented as follows: Y = 3.651436 + 0.268015 X1+ 0.03652 X2 − 0.01257 X3 + 0.127123 X4 + 0.092798 X5 − 0.11515 X6 − 0.06885 X7 + 0.004191 X8 − 0.00818 X9 − 0.37179 X10 − 0.16065 X11 − 0.12233 X12. On the basis of the calculated t values and confidence levels (%), starch, pH, FeSO4.7H2O, temperature and NH4NO3 of confidence level ≥ 96% were found to be the most significant variables affecting CHO activity produced by Streptomyces sp. AN. According to these results, a medium of the following composition (g/L), cholesterol 1, starch 2, MgSO4 0.1, CaCl2 0.01, FeSO4.7H2O 0.1, NH4NO3 20, YE 0.2, K2HPO4 0.01, KH2PO4 0.1, NaCl, 0.01, Tween 20 0.01 and pH 5, and incubation at 30 °C for 9 days under shaking (200rpm) was used as the basic medium for the next design. BBD (the second approach) was applied in order to reach the optimum response region for CHO production; the significant independent variables {pH (X1), starch (X2), NH4NO3 (X3), FeSO4.7H4O (X4)} were further explored at three levels based on the results obtained in PBD. Table 3 represents the levels of each tested variable in coded units, − 1 (low level), 0 (medium level) and 1 (high level). The four variables with 27 trials were analysed using the linear multiple regression analysis method, and the percentage of confidence level was calculated. The analysis of variance using the ANOVA test in the BB experiment was generated and summarized in Table 4, which gives p = 0.0123. Since the p value indicated in the ANOVA table is less than 0.05, it is concluded that there is a statistically significant relationship among the studied variables at a 95% confidence level (p = 0.05). The value of the determination coefficient R2 = 0.80 for CHO activity, being a measure of fit of the model, indicates that about 20% of the total variations are not explained by CHO activity. The multiple linear regression models describe the relationship between the enzyme activity and four studied variables, namely, pH, starch, NH4NO3 and FeSO4.7H2O. Surface plots (Fig. 4) show that higher levels of CHO activity were attained by increasing the concentration of starch and pH while decreasing the iron level and using a concentration near to the low value of ammonium nitrate in the medium. Contour analysis (Fig. 4) was calculated for Streptomyces sp. AN CHO to detect the centre point which gives maximum CHO activity. For predicting the optimal point, a second-order polynomial function was fitted to the experimental results (linear optimization algorithm) for CHO. where X1, X2, X3 and X4 are pH, starch, NH4NO3 and FeSO4.7H2O, respectively. The optimal levels of the four components as obtained from the maximum point of the polynomial model were estimated using the SOLVER function of Microsoft Excel tools and JMP-program and found to be (g/L) starch 6.0, NH4NO3 23.97 and FeSO47H2O 0.1 at pH 6.36, with a predicted activity of 6.56 U/mL (Fig. 5). In order to determine the accuracy of the quadratic polynomial, a verification experiment was carried out under the predicted optimal condition as determined previously. To prove the accuracy of the model, the % accuracy was calculated from the following formula: The estimated activity of Streptomyces sp. AN CHO was 6.61 U/mL. This means that the calculated model accuracy was 99.2%. In this study, the combination of PB and BB designs was shown to be effective and reliable in selecting the statistically significant factors and finding the optimal concentration of each factor. Based on the results obtained from the PB and BB designs, the expected medium composition for optimum CHO activity by Streptomyces sp. AN was (g/L) cholesterol 1, starch 6, MgSO4.7H2O 0.1, CaCl2 0.01, FeSO4.7H2O 0.1, NH4NO3 23..97, YE 0.2, K2HPO4 0.01, KH2PO4 0.1, NaCl 0.01, Tween 20, 0.01, pH 6.36 and incubation temperature 30 °C for 9 days. Finally, the production of Streptomyces sp. AN CHO has been systematically improved by almost 2.6-folds during various experimental designs compared with the basal medium.
Alterations related to the action of CHO produced by Streptomyces sp. AN were examined using FTIR spectra of cholesterol (substrate) powder samples before and after the fermentation course. Before the fermentation bioprocess, the FTIR spectrum of the substrate revealed the presence of absorption peaks at 3401, 2944, 1456, 1370, 1054, 955, 833 and 589.9 cm−1 (Fig. 6, control). These typical absorption peaks and intensity were noticed to be changed after fermentation. The absorption maxima at 3401, 2944, 1456, 1370 and 1054 cm−1 in the control (unfermented cholesterol substrate) were considerably reduced by fermentation, and new distinctive absorption peaks at 1644 and 1725 cm−1 were developed in fermented samples (Fig. 6, A).
The thermal characteristics of cholesterol substrate and released products after fermentation were estimated qualitatively and quantitatively using DSC analysis. The DSC-control thermogram (Fig. 7, control) revealed the normal endothermic transition of the cholesterol substrate powder, with melting temperatures peaking at 38.96, 148.77 and 196.24 °C and heat flow, capacity, and enthalpy (− 2 mW/mg, − 225.13 mJ and − 8.38 J/g; − 8 mW/mg, − 209.78 mJ and − 69.93 J/g; and 1 mW/mg, 49.73 mJ and 16.58 J/g, respectively). By comparing the DSC of the fermented product (Fig. 7, A) produced after optimization to the control-DSC, noticeable differences in the melting temperature peak (99.26 °C), heat flow (− 3.6 mW/mg), heat capacity (− 924.69 mJ) and heat enthalpy (− 385.29 J/g) were recognized.
Numerous scholars have noted that the formation of a red hue is caused by the formation of quinoneimine dye, indicating the development of CHO [16, 17, 33]. Similarly, the investigated bacterial strain Streptomyces sp. NA proved to produce CHO. In order to increase CHO production by Streptomyces sp. AN, a sequential optimization technique was adopted in two steps (PB and BB). El-Naggar et al. [34] investigated the effects of environmental and metabolic variables on Streptomyces cavourensis strain NEAE-42’s CHO. PB design offers a simple, quick screening process as well as statistically evaluating the significance of a large number of variables in one experiment, saving time and ensuring that each element has persuasive evidence. In this study, PB results showed a wide variation in CHO activity from 2.54 to 4.41 U/mL. According to the p values of the studied 12 variables, starch, pH, FeSO4.7H2O, temperature and NH4NO3 were found to be the most significant affecting CHO activity generated by Streptomyces sp. AN. Our results are significantly better than those of S. niger MTCC 4010 (0.27 U/mL), S. fradiae MTCC 4002 (0.32 U/mL), S. olivaceus MTCC 6820 (0.625 U/mL), S. hygroscopicus MTCC 4003 (0.472 U/mL), S. annulatus MTCC 6818 (0.355 U/mL) and S. clavifer MTCC 4150 (0.254 U/mL) [35]. In a previous study, some physical factors including the initial pH of the medium, cultivation temperature and shaking speed affecting the production of CHO by Rhodococcus equi were studied [36]. Also, medium pH, incubation temperature, inoculum size, inoculum age, fermentation period and shaking speed were studied for augmenting the CHO production by S. olivaceus MTCC 6820 [37]. For microbial growth, metabolic characteristics and metabolite production, the pH value of the cultural medium is critical. According to [38], changes in the pH of the culture medium have a considerable impact on the cells’ optimal physiological performance, the transfer of different nutrients via the cell membrane and cholesterol breakdown. Many investigators believe that confidence levels of variables greater than 90% are suitable and acceptable when evaluating the empirical statistical model [39, 40] for bioprocess optimization. This current investigation’s variables showed a confidence per cent greater than 96.85, p value ≥ 0.031. Additionally, one of the PBD’s advantages is that it allows operators to rank the effect of different variables on the measured response regardless of the factor’s nature. The medium components are improved by statistical approaches (RSM) in the second step of optimizing CHO production. The important contributing variables (pH, starch, NH4NO3 and FeSO4.7H2O) were further investigated at three levels: − 1, 0 and + 1 in order to approach the CHO’s optimum response area with activity. The findings of surface plots revealed that raising the starch concentration and pH value while lowering the iron and ammonium nitrate levels resulted in higher levels of CHO activity. The R2 value for CHO activity in this design was 0.99, indicating a strong correlation between the actual and anticipated values. The experimentally verified optimal settings from the optimization experiment were compared to the model’s anticipated optimum. The estimated CHO activity was 6.61 U/mL, and the polynomial model predicted a value of 6.56 U/mL. This high level of accuracy (99.2%) indicates that the model was validated under ideal conditions. Furthermore, the enzyme activity in the improved medium was 2.6 times higher than in the baseline conditions. This demonstrated the importance and usage of the optimization process. Our findings are consistent with [39] in which the RSM is a commonly accepted advanced numerical method for optimizing experimental conditions and solving analysis problems in which a response is strongly impacted by many variables for the production of industrially important biological molecules. After the fermentation process and degradation of cholesterol by excreted enzymes, primarily CHO produced by Streptomyces. sp. AN, caused changes in the existing functional groups of cholesterol. CHO reacted with the side chain of cholesterol, causing changes and modifications to the cholesterol’s existing functional groups. The new distinctive absorption peaks at 1644 and 1725 cm1 developed in samples after fermentation were pointed to the presence of a keto group (C=O) stretch bond, which was formed by CHO functioning on the cholesterol substrate. These results are supported by Saranya et al. [41], who used FTIR analysis to follow up or detect cholesterol metabolites formed throughout the breakdown process utilizing Pseudomonas sp., Bacillus sp. and Streptomyces sp. on cholesterol-containing medium. According to these findings, the C=O stretch bond and the ketone functional group may be seen in the frequency range of 1710–1665 cm1. According to the FTIR study, cholesterol degraded into the metabolites 4-cholestene-3-one, cholest- 4-ene-3, 17-dione, and androst-1, 4-diene-3, 17-Dione, these metabolites are cholesterol ketonic derivatives according to their chemical composition. After deterioration, the cholesterol’s functional groups deteriorated as well, resulting in ketones (C=O stretch bond). According to the results of the FTIR analysis, the primary metabolite produced might contain 4-cholestene-3-one. This speculation is supported by the findings of Wu et al., [42], who report the presence of C=O structure in FTIR with Vmax at 1672.3 cm−1, attributed to the presence of 4-cholesten-3-one for matching the standard IR spectrum of 4-cholesten-3-one in the Sadtler Standard Infrared Grating Spectra (number 28840K) [43]. According to the difference in the DSC thermogram of the control sample and the fermented product, it was observed that the melting temperature peak showed at 99.26, heat flow − 3.6 mW/mg, heat capacity (− 924.69 mJ) and heat enthalpy (− 385.29 J/g), all of which were attributed to the action of CHO generated by Streptomyces sp. AN and the release of keto derivatives of cholesterol with distinct characteristics. At the end of the fermentation process, CHO acting on an existing cholesterol substrate caused a change in thermal characteristics. The melting temperature of the released ketonic derivatives of cholesterol was frequently lower than that of the original substrate.
CHO, a member of the oxidoreductase family that catalyzes the oxidation of cholesterol molecules, has several applications. CHO has been thoroughly researched and utilized commercially for detecting the value of cholesterol in clinical samples, bioconversion of cholesterol into useful chemicals, food preparation and insecticidal efficacy against cotton weevils. Thus, the focus of this research has been directed toward the development and optimization of CHO, as well as the study of the enzyme’s mode of action using FTIR and DSC for its substrate before and after fermentation. |
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PMC9649584 | Guo Chen,Shuang Chen,Xingpeng Di,Shengyin He,Yugao Liu,Rui Qu,Yi Luo,Yuebai Liu,Luo Yang | Survivin knockdown alleviates pathological hydrostatic pressure-induced bladder smooth muscle cell dysfunction and BOO-induced bladder remodeling via autophagy 10.3389/fcell.2022.999547 | 28-10-2022 | survivin,autophagy,bladder smooth muscle cell,bladder outlet obstruction,bladder remodeling | Aim: Bladder outlet obstruction (BOO) leads to bladder wall remodeling accompanying the progression from inflammation to fibrosis where pathological hydrostatic pressure (HP)-induced alteration of bladder smooth muscle cells (BSMCs) hypertrophic and excessive extracellular matrix (ECM) deposition play a pivotal role. Recently, we have predicted survivin (BIRC5) as a potential hub gene that might be critical during bladder fibrosis by bioinformatics analyses from rat BOO bladder, but its function during BOO progression remains unknown. Here, we investigated the role of survivin protein on bladder dysfunction of BOO both in vitro and in vivo. Methods: Sprague-Dawley female rats were divided into three groups: control group, BOO group, and BOO followed by the treatment with YM155 group. Bladder morphology and function were evaluated by Masson staining and urodynamic testing. To elucidate the underlying mechanism, hBSMCs were subjected to pathological HP of 200 cm H2O and co-cultured with the presence or absence of survivin siRNA and/or autophagy inhibitor 3-MA. Autophagy was evaluated by the detection of Beclin1 and LC3B-II expression, proliferation was conducted by the EdU analysis and PCNA expression, and fibrosis was assessed by the examination of Col 1 and Fn expression. Results: BOO led to a gradual alteration of hypertrophy and fibrosis of the bladder, and subsequently induced bladder dysfunction accompanied by increased survivin expression, while these histological and function changes were attenuated by the treatment with YM155. HP significantly increased survivin expression, upregulated Col1 and Fn expression, enhanced proliferation, and downregulated autophagy markers, but these changes were partially abolished by survivin siRNA treatment, which was consistent with the results of the BOO rat experiment. In addition, the anti-fibrotic and anti-proliferative effects of the survivin siRNA treatment on hBSMCs were diminished after the inhibition of autophagy by the treatment with 3-MA. Conclusion: In summary, the upregulation of survivin increased cell proliferation and fibrotic protein expression of hBSMC and drove the onset of bladder remodeling through autophagy during BOO. Targeting survivin in pathological hBSMCs could be a promising way to anti-fibrotic therapeutic approach in bladder remodeling secondary to BOO. | Survivin knockdown alleviates pathological hydrostatic pressure-induced bladder smooth muscle cell dysfunction and BOO-induced bladder remodeling via autophagy 10.3389/fcell.2022.999547
Aim: Bladder outlet obstruction (BOO) leads to bladder wall remodeling accompanying the progression from inflammation to fibrosis where pathological hydrostatic pressure (HP)-induced alteration of bladder smooth muscle cells (BSMCs) hypertrophic and excessive extracellular matrix (ECM) deposition play a pivotal role. Recently, we have predicted survivin (BIRC5) as a potential hub gene that might be critical during bladder fibrosis by bioinformatics analyses from rat BOO bladder, but its function during BOO progression remains unknown. Here, we investigated the role of survivin protein on bladder dysfunction of BOO both in vitro and in vivo. Methods: Sprague-Dawley female rats were divided into three groups: control group, BOO group, and BOO followed by the treatment with YM155 group. Bladder morphology and function were evaluated by Masson staining and urodynamic testing. To elucidate the underlying mechanism, hBSMCs were subjected to pathological HP of 200 cm H2O and co-cultured with the presence or absence of survivin siRNA and/or autophagy inhibitor 3-MA. Autophagy was evaluated by the detection of Beclin1 and LC3B-II expression, proliferation was conducted by the EdU analysis and PCNA expression, and fibrosis was assessed by the examination of Col 1 and Fn expression. Results: BOO led to a gradual alteration of hypertrophy and fibrosis of the bladder, and subsequently induced bladder dysfunction accompanied by increased survivin expression, while these histological and function changes were attenuated by the treatment with YM155. HP significantly increased survivin expression, upregulated Col1 and Fn expression, enhanced proliferation, and downregulated autophagy markers, but these changes were partially abolished by survivin siRNA treatment, which was consistent with the results of the BOO rat experiment. In addition, the anti-fibrotic and anti-proliferative effects of the survivin siRNA treatment on hBSMCs were diminished after the inhibition of autophagy by the treatment with 3-MA. Conclusion: In summary, the upregulation of survivin increased cell proliferation and fibrotic protein expression of hBSMC and drove the onset of bladder remodeling through autophagy during BOO. Targeting survivin in pathological hBSMCs could be a promising way to anti-fibrotic therapeutic approach in bladder remodeling secondary to BOO.
Bladder outlet obstruction (BOO) is characterized by gradually increased intravesical hydrostatic pressure and low flow micturition pattern, is a prevalent condition among aging males that is primarily caused by benign prostatic hyperplasia (BPH) (Capolicchio et al., 2001; Metcalfe et al., 2010; Chen et al., 2012; Komninos and Mitsogiannis 2014). Given the irreversible bladder remodeling, late-stage BOO characterized by bladder decompensation has poor success rates in improving the voiding symptoms even after surgical treatments, which seriously affects the quality of life of patients and their partners (Komninos and Mitsogiannis 2014). Understanding the underlying mechanisms of obstruction to fibrosis may contribute to identifying new targets for pharmaceutical intervention and ensure that patients who live with a degree of obstruction never progress to the end stages of bladder dysfunction. As shown in previous studies, the role of cytokines including alpha-blockers, antimuscarinics, and β-Adrenergic receptors during BOO has been well-established (Dale et al., 2014; Gumrah et al., 2017; Yamada et al., 2018). In addition, TGF-β signaling could stimulate fibrotic changes in BSMCs after BOO, and miR-133 could modulate TGF-β-induced BSMC phenotypic changes by targeting CTGF (Duan et al., 2015). Platelet-derived growth factor could induce the proliferation of BSMC and basic fibroblast growth factor could upregulate Col3 and induce the proliferation of BSMC (Imamura et al., 2007; Preis et al., 2015). Hypoxia also affected the BSMC fibrosis, and miR-101 protected hypoxia-induced fibrosis by attenuating TGF-β-smad2/3 signaling (Wang et al., 2019). YAP inhibitor verteporfin improved the pathophysiologic changes of BSMC through the regulation of DNA methylation (Sidler et al., 2018). Moreover, increasing evidence indicated that mechanical stimuli contribute to bladder growth and development; while abnormal mechanical conditions secondary to BOO result in detrusor smooth muscle hypertrophy and extracellular matrix (ECM) accumulation (Ito et al., 2021). Our laboratory demonstrated that intravesical hydrostatic pressure triggered multiple signaling pathways including ERK, NF-κB, PI3K/SGK1, AMPK/mTOR, and so on that were involved in inflammation, dedifferentiation, and fibrosis (Chen et al., 2012; Chen et al., 2015; Liang et al., 2017; Chen et al., 2020a). But the mechano-regulatory processes during BOO remain unknown. Survivin is the smallest member of the inhibitor of apoptosis protein (IAP) family discovered in 1997 (Li et al., 2021). Recently, emerging evidence has suggested that survivin was highly expressed in most tumor tissues and fibrotic diseases, and its expression was associated with a diverse array of cellular pathological and physiological processes such as differentiation, proliferation, and invasion during scarring, liver fibrosis, and vascular remodeling (Fan et al., 2015; Wang et al., 2018; Sharma et al., 2021; Ye et al., 2022). Interestingly, our previous study has predicted that survivin was significantly associated with the fibrotic stage of BOO (Di et al., 2022). However, its effects during bladder remodeling remain unclear and require further investigation. Autophagy is a highly regulated catabolic process that maintains cellular homeostasis in response to various damaged, defective, or unwanted stimuli (Geir et al., 2005; Li et al., 2016; Daniel et al., 2018; Nam et al., 2019). Increasing evidence suggests that autophagy is closely related the bladder remodeling secondary to BOO due to its double-edged sword role in pro-cell survival and the pro-cell death properties (Chen et al., 2021). Our previous study has demonstrated that autophagy regulated the biological function of BSMCs under HP (Chen et al., 2020b). The emerging data indicated that survivin negatively modulated autophagy levels in various diseases, which piqued our interest in exploring whether the survivin has a similar role in the bladder remodeling of BOO (Ding et al., 2015; Pavel et al., 2018). We hypothesize that survivin plays a crucial role in the pathological process of bladder remodeling during BOO. In the present study, BOO rats were administered with survivin inhibitor YM155 to demonstrate the effect of survivin. To examine its possible underlying mechanism, cells were subject to HP to simulate the mechanical obstruction microenvironment of BOO. The mechanism in this pathological process provided new ideas for the prevention and targeted therapy of bladder remodeling after BOO.
All animal experiments were reviewed and approved by the West China Hospital Committee on Animal Care. Female rats (220–250 g) underwent surgical operation for the establishment of BOO was similar to our previous study (Liu et al., 2017). The sham-operated group underwent the same procedure but without urethral ligation. BOO rats in the experimental group were administered with survivin inhibitor YM155. The control group was treated with saline. BOO rats were given by gavage orally administered with 10 mg/kg YM155 or saline once other days from the third day after the operation for 6 weeks. After 6 weeks, the urodynamic characteristics were detected. Subsequently, rats were humanely euthanized for the detection of bladder weight, bladder structure, bladder fibrosis, and mucosa-removed for WB.
Cystometry was performed to evaluate the urodynamic parameters as previously described. Briefly, rats were anesthetized and a 25 G needle connecting to a polyethylene catheter was inserted and fixed in the dome of the bladder. Cystometry was performed through the continuous saline infusion at a rate of 2.4 ml/h. The micturition pressure, baseline pressure, threshold pressure, and micturition interval were recorded to evaluate bladder function.
After the end of the cystometry, bladders were excised and weighed, subsequently fixed in 10% neutral buffered formalin, embedded in paraffin, and cut into 5 μm sections. Masson’s trichrome staining was used to observe tissue fibrosis according to protocol. The blue-stained collagen and red counterstained muscle were analyzed utilizing ImageJ (Fiji) software. Ten randomly chosen areas at ×50 optical magnification in representative portions of each slide were calculated in a blinded fashion with a square micrometer and the mean area was expressed as the relative percent. The mean percent collagen area was defined according to the formula: collagen area/total visual area × 100%. We and other teams all have previously published research articles using these methods (Gao et al., 2021). The percentage area was selected in the program and generated automatically for each image. The averages were then calculated for each group. For immunohistochemistry, paraffin sections were processed with deparaffinization, antigen retrieval, removal of endogenous catalase, and blockage of the unspecific binding site. Sections were then incubated with primary antibodies overnight at 4°C. After washing in PBS, anti-mouse AF594 (A11032) and anti-rabbit AF-488 (A21206) from Invitrogen were used for incubation for 1 h in the dark at room temperature. Finally, nuclei were counterstained with DAPI. The intensities of α-SMA, Col1, and Fn were obtained using ImageJ software and normalized against DAPI intensities.
hBSMCs (No. 4310, ScienCell, San Diego, CA) were cultured with a special smooth muscle cell medium (SMCM) containing 10% FBS and 1% penicillin/streptomycin. Cells between passages 2 and 6 were incubated in conventional culture conditions (5% CO2 and 37°C) for all experiments. hBSMCs were pretreated with the indicated intervention reagents for 1 h before exposure to HP. The survivin inhibitor YM155(HY-10194) was obtained from MCE and autophagy antagonist 3-methyladenine (3-MA; M9281) was obtained from Sigma-Aldrich (St. Louis, MO).
Survivin siRNA and control siRNA were designed and constructed by GenePharma (Shanghai, China) with sequence (forward 5′-GCAUCUCUACAUUCAAGAA-3′ and reverse 5′-UUCUUGAAUGUAGAGAUGC-3′) were for survivin and sequence (forward 5′-UUCUCCGAACGUGUCACGU-3′ and reverse 5′-ACGUGACACGUUCGGAGAA-3′) were for control. They were transfected into hBSMCs by Lipo3000 according to the manufacturer’s protocol (Invitrogen). The efficiency of siRNA was detected 48 h after the transfection by Western blot.
hBSMCs were subjected to Hydrostatic pressure (HP) in our designed apparatus, which provided conventional culture conditions (5% CO2 and 37°C) for cell growth. Similar to the previous report study, hBSMCs in the apparatus were subjected to HP of 200 cm H2O for the pathological parameter mimicking the pathological condition of BOO (Chen et al., 2020a).
Western blot (WB) procedures were as previously described (Chen et al., 2020b), the membranes were incubated overnight at 4°C with the following antibodies: Microtubule-associated protein 1 light chain 3 beta (LC3B) (ab48394; 1:1,000; 17 kDa and 19 kDa), Beclin1 (ab207612; 1:2000; 52 kDa), Survivin (ab134170; 1:1,000; 16 kDa), GAPDH (ab9485; 1:2,500; 37 kDa),Col1 (ab96723; 1:1,000; 129 kDa), Fn(ab268020; 1:1,000; 262 kDa), and PCNA(ab29; 1:1,000; 29 kDa) from Abcam (Cambridge, MA, United States). The membranes were then exposed to an immobilon chemiluminescent substrate.
hBSMCs proliferation was also evaluated using the 5-ethynyl-2′-deoxyuridine (EdU) Kit (C10310-1, RiboBio, Guangzhou, China) according to the manufacturer’s instructions. Cells, stained with red, represented proliferation cells, while cell nuclei were blue. The positive cells were analyzed by ImageJ.
Microarray analysis (service provided by Kangchen Biotech, Shanghai, China) was based on our previous study to evaluate the bladder gene expression in the BOO model and sham-operated group using a Whole Rat Genome Oligo Microarray (Agilent Technologies, Santa Clara, CA) (Yang et al., 2018). After the microarray data of the BOO and sham-operated rats were obtained, we transformed the probe identification numbers into gene symbols. Moreover, we normalized the gene expression values with the Affy package, and log2 transformation and normalization were applied in the Robust Multichip Average signal intensity analysis. Our previous study predicted the potential role of survivin during BOO fibrosis by bioinformatics analyses. To investigate the specificity of survivin in BOO bladder, we further compared all IAP family member’s expression both in mRNA level and protein level between BOO bladder and sham-operated bladder, and we finally confirmed that only survivin expression significantly increased in BOO bladder compared with sham-operated bladder.
Three independent observations for each in vitro cellular experiment have been performed (n = 3) and in vivo rat experiments were divided into three groups (n = 8). Statistical analyses were performed with Graph Pad Prism 8 and with SPSS 20.0 software. The data were shown as the mean ± SD. D’Agostino-Pearson normality test was used to evaluate whether the continuous data were normally distributed, and according to the results, Student’s t-test or ANOVA for normally distributed data or a Kruskal-Wallis 1-way ANOVA test for not normally distributed data was used. A p-value less than 0.05 was considered statistically significant.
The underlying mechanism of bladder fibrosis flowed by BOO was not well known. Our microarray analysis results showed that among the common IAP members in the bladder, BIRC3 and survivin (BIRC5) were prominently upregulated in the BOO rat bladder when compared with those in the sham-operated bladder, while others were not changed significantly (Figure 1A). However, to better understand the role of survivin in bladder smooth muscle, the bladder-removed mucosa was used to assess the protein expression of survivin and BIRC3 by Western blot. As shown in Figure 1B, the survivin expression of mucosa-denuded bladders in BOO bladder was significantly increased when compared with sham-operated bladder. However, there was no significant difference in BIRC3 protein expression between the sham-operated group and the BOO group.
Our previous study has predicted that survivin was a critical hub gene in bladder fibrosis (Di et al., 2022). To investigate the effect of survivin during bladder remodeling, the rats were administered survivin inhibitor YM155. Based on the results of urodynamic parameters and Masson staining, BOO was successfully established after obstruction for 6 weeks. As we know, BOO significantly induced the deposition of ECM. After the performance of urodynamic testing, bladders were harvested for the evaluation of bladder weight, histological alteration, and collagen deposition. The results showed that the bladder weights of BOO significantly increased compared with the sham-operated group and the increase was attenuated by the treatment with YM155 (Figure 2A). Nevertheless, body weight did not display evident changes (Figure 2B). We observed the survivin protein in the BOO bladder decreased after the treatment with YM155. Similar trends were observed in the protein levels of PCNA (Figure 2C). Based on Masson’s trichrome staining results, BOO markedly increased the percentage of collagen in the bladder, while treatment with YM155 decreased the percentage of collagen (Figure 2D). As we know, bladder remodeling characterized ECM deposition mainly including Col1 and Fn. As Figure 2E showed, we further found that YM155 mainly decreased the Col1 and Fn of the bladder by the immunohistochemical evaluation (Figures 2F,G). These results indicated that YM155 might reverse bladder remodeling secondary to BOO.
The bladder function was analyzed according to the urodynamic curve. As shown in Figures 3A–C, the micturition interval in the BOO group was significantly shorter than in the sham-operated group, while treatment with YM155 attenuated the alteration. Micturition pressure, baseline pressure, and threshold pressure were higher in the BOO group compared with the sham-operated group. Baseline pressure was a slight, but significant, increase in BOO treated with YM155 compared with the BOO group, while micturition pressure and threshold pressure have no significant alteration by treatment with YM155 (Figures 3D–G). These observations indicated that the YM155 markedly suppressed the dysuria associated with BOO.
As our previous study demonstrated, pressure at 200 cm H2O was selected as the optimal pathological parameter mimicking the BOO cell model in vitro (Chen et al., 2020a, Jin et al., 2020; Gao et al., 2021). Exposure to 200 cm H2O HP for 24 h increased the expression of collagen including Col1 and Fn protein levels. In addition, the analysis of EdU and PCNA expression levels showed that HP enhanced the proliferation as well as upregulated survivin. To explore whether the HP-increased survivin was necessary for fibrosis, hBSMCs were transfected with siRNA before exposure to HP. We first confirmed the successful knockdown of siRNA by the western blot (Figure 4A). As the results of WB showed, survivin siRNA significantly suppressed the HP-induced Col1 and Fn expression (Figures 4B,C). As the results of EdU and the PCNA expression showed, survivin siRNA repressed the HP-induced cell proliferation (Figures 4C,D).
Our previous study has demonstrated that autophagy was strongly involved in the function of hBSMCs under HP (Chen et al., 2020b). The emerging data point to several aspects of autophagy that was closely related to survivin protein. To understand whether and how survivin affected autophagy during BOO, autophagy markers in BOO bladder were detected after the treatment with YM155 in vivo. We observed the YM155 alleviated BOO-decreased LC3B II/I and Beclin1 (Figure 5A). Cells were exposed to HP, and the level of LC3B II/I and Beclin1 were measured after the treatment with siRNA survivin in the presence or absence of 3-MA. As shown in Figure 5B, siRNA survivin treatment promoted autophagy level, which was evidenced by increased LC3B II and Beclin1, suggesting survivin negatively regulated autophagy level under HP. To investigate whether the increase LC3B II was due to enhanced autophagy or blockage of the lysosomal degradation of autophagic vacuoles, autophagic flux was assessed using the GFP-mRFP-LC3B assay. The results showed that the amounts of autophagosomes and autolysosomes were both increased, indicating that autophagic flux was unobstructed (Figure 5C). We further found that the autophagy inhibitor, 3-MA, not only decreased the siRNA survivin-increased autophagy level but also increased the siRNA survivin-decreased Col1 and Fn (Figure 5D). The PCNA expression and EdU analysis also demonstrated that 3-MA ameliorated the siRNA survivin-increased proliferation (Figures 5D,E). These results suggested that survivin negatively regulated autophagy, which played a critical role in fibrosis and proliferation of hBSMCs.
Accumulating evidence suggests that the impairment of bladder morphology and bladder function secondary to BOO is associated with fibrosis, which is characterized by bladder smooth muscle hypertrophy and ECM deposition (Uvelius, Persson, and Mattiasson 1984; Metcalfe et al., 2010; Metcalfe et al., 2010). There is widespread consensus that ECM deposition in the detrusor layer is the primary reason for dysfunction. Understanding the underlying mechanism from obstruction to hypertrophy and fibrosis using rat models may contribute to identifying targets for pharmaceutical intervention at the appropriate stage of bladder remodeling. Emerging studies indicate that survivin is critically involved in various physiological and pathological processes, such as wound healing, neovascularization, scar formation process, and so on, due to its dual effects of promoting the cell cycle and inhibiting apoptosis (Ding et al., 2015; Miyamoto et al., 2021; Sharma et al., 2021). Our previous report predicted that survivin was well related to bladder fibrosis by bioinformatics analyses from the whole bladder. To better understand the role of survivin in bladder smooth muscle, the bladder wall tested in the present study was removed mucosa. We demonstrated that among IAP members, BIRC3 and survivin were prominently upregulated. Based on the potential role in bladder fibrosis, it was of great interest to explore the effect of survivin on the pathological process of BOO. Altered ECM composition, especially, Col1 and Fn, has been reported in the hypertrophic bladder during the fibrotic stage of hypertrophy. The result showed that targeting inhibition of survivin by YM155 could downregulate the ECM deposition and improve bladder dysfunction secondary to BOO. The bladder detrusor, which is mainly composed of smooth muscle cells, is considered to be the important structural unit of the bladder wall. The late stage of BOO is characterized by muscle hypertrophy and enhanced production of extracellular matrix constituents. Besides the BSMC secreted collagens, other mechanisms such as epithelial-mesenchymal transition (EMT), fibroblast-to-myofibroblast transition (FMT), and macrophage-myofibroblast transition (MMT) are all involved in bladder fibrosis. Although collagen that existed in the bladder detrusor is not the most, the collagens in intercellular spaces of smooth muscle cells seriously affect bladder function. The biomechanical force caused by BOO is a critical element for bladder smooth muscle cell function and phenotype. Therefore, the present study focused on the bladder smooth muscle cells. The persistent obstruction increased intravesical pressures, which played a critical role in bladder remodeling (Chen et al., 2012; Liang et al., 2017). To extensively illustrate the underlying mechanism, hBSMCs were exposed to the HP of 200 cm H2O mimicking the conditions of BOO, and were used as the experimental model for the present investigation. We found that HP of 200 cm H2O significantly increased collagen expression and enhanced cell proliferation as well as upregulated survivin, which was similar to the results in vivo. We further used siRNA to knock down survivin protein, the results showed that siRNA survivin ameliorated the HP-increased fibrotic and proliferative response. As a multifunctional factor widely existing in mammals, autophagy participates in various physiological and pathological processes, such as immunity, inflammation, fibrosis, and so on. Our previous study indicated that HP significantly enhanced the proliferation and contraction of BSMCs, and we found that autophagy was of importance to BSMCs dysfunction. While the underlying mechanism is not well known. Emerging evidence suggests that crosstalk between apoptosis and autophagy plays an important role in supporting cell survival and proliferation. Survivin, as a member of the IAP family, was initially thought to be a connection with autophagy. We firstly indicated that survivin knockdown abolished the HP-decreased autophagy level by assessing the LC3B II/I ratio and Beclin1 expression. Subsequent experiments further revealed that autophagy inhibitor 3-MA suppressed the survivin knockdown-induced changes. This present study still has some limitations. Firstly, our study failed to explore human BOO tissues or samples for ethical reasons. Secondly, the effect of YM155 on HP-induced fibrosis of hBSMCs needs to be further explored. In conclusion, HP-induced survivin negatively regulated autophagy, which played a critical role in the pro-fibrosis and pro-proliferative of hBSMCs in vitro. A novel anti-fibrotic and anti-proliferative function of survivin might represent a potential target for therapeutic strategies in bladder remodeling. Clinical trials were needed to validate the effect of survivin on bladder dysfunction and bladder fibrosis in the future. |
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PMC9649591 | Rose Hodgson,Xijin Xu,Consuelo Anzilotti,Mukta Deobagkar-Lele,Tanya L. Crockford,Jessica D. Kepple,Eleanor Cawthorne,Aneesha Bhandari,Alberto Cebrian-Serrano,Martin J. Wilcock,Benjamin Davies,Richard J. Cornall,Katherine R. Bull | NDRG1 is induced by antigen-receptor signaling but dispensable for B and T cell self-tolerance | 10-11-2022 | Cancer genomics,Immune tolerance | Peripheral tolerance prevents the initiation of damaging immune responses by autoreactive lymphocytes. While tolerogenic mechanisms are tightly regulated by antigen-dependent and independent signals, downstream pathways are incompletely understood. N-myc downstream-regulated gene 1 (NDRG1), an anti-cancer therapeutic target, has previously been implicated as a CD4+ T cell clonal anergy factor. By RNA-sequencing, we identified Ndrg1 as the third most upregulated gene in anergic, compared to naïve follicular, B cells. Ndrg1 is upregulated by B cell receptor activation (signal one) and suppressed by co-stimulation (signal two), suggesting that NDRG1 may be important in B cell tolerance. However, though Ndrg1−/− mice have a neurological defect mimicking NDRG1-associated Charcot-Marie-Tooth (CMT4d) disease, primary and secondary immune responses were normal. We find that B cell tolerance is maintained, and NDRG1 does not play a role in downstream responses during re-stimulation of in vivo antigen-experienced CD4+ T cells, demonstrating that NDGR1 is functionally redundant for lymphocyte anergy. | NDRG1 is induced by antigen-receptor signaling but dispensable for B and T cell self-tolerance
Peripheral tolerance prevents the initiation of damaging immune responses by autoreactive lymphocytes. While tolerogenic mechanisms are tightly regulated by antigen-dependent and independent signals, downstream pathways are incompletely understood. N-myc downstream-regulated gene 1 (NDRG1), an anti-cancer therapeutic target, has previously been implicated as a CD4+ T cell clonal anergy factor. By RNA-sequencing, we identified Ndrg1 as the third most upregulated gene in anergic, compared to naïve follicular, B cells. Ndrg1 is upregulated by B cell receptor activation (signal one) and suppressed by co-stimulation (signal two), suggesting that NDRG1 may be important in B cell tolerance. However, though Ndrg1−/− mice have a neurological defect mimicking NDRG1-associated Charcot-Marie-Tooth (CMT4d) disease, primary and secondary immune responses were normal. We find that B cell tolerance is maintained, and NDRG1 does not play a role in downstream responses during re-stimulation of in vivo antigen-experienced CD4+ T cells, demonstrating that NDGR1 is functionally redundant for lymphocyte anergy.
Balancing the sensitivity required for an effective adaptive immune response, while limiting reactivity to prevent autoimmunity, depends upon an increasingly well-defined series of checkpoints. Lymphocyte anergy is a state of cell-intrinsic functional inactivation, classically described when B or T cells become unresponsive upon antigen-receptor binding by self-antigen. This is thought to occur during development when cells are exposed to self-antigen, at insufficient affinity or avidity to induce receptor editing or deletion, or in mature T and B cells when exposed to antigens in the absence of accessory signals in the periphery. Central to the fate decision between induction of a tolerogenic or immunogenic response in the periphery is Bretscher and Cohn’s two signal model of cell activation, in which full activation of B and T lymphocytes requires two distinct signals: antigen-engagement of the B cell receptor (BCR) or T cell receptor (TCR) respectively (signal one), which leads to limited initiation of intracellular signaling cascades, and co-stimulation by activated antigen-presenting cell (APC) ligands or CD4+ T cell help (signal two), which triggers the sustained, complete signaling required for productive activation. If signal two is not received within a temporally-defined window of signal one, this leads to induction of an unresponsive state and cell death, limiting inappropriate responses. In the context of B cell tolerance, immature B lymphocytes that are not eliminated upon encountering low avidity forms of self-antigen in the bone marrow (BM) can undergo phenotypic and biochemical changes to produce a tolerized, functionally-inactive state. As these anergic B lymphocytes emerge into the periphery, they have an increased threshold for activation, due to proximal blockade of BCR-signaling and selective inhibition of IgM trafficking to the cell surface, classically demonstrated in BCR Ig transgenic (tg) mouse models, including the anti-hen-egg lysozyme (HEL)-specific (IgHEL) model. Continual low avidity BCR-signaling in the absence of co-stimulation induces chronic low oscillations of calcium, resulting in sustained sub-optimal activation of ERK and NFAT dependent pathways, with proximal blockade of BCR-dependent signaling and a specific block in JNK, Card11, NF-κB and pathways downstream of toll-like receptors (TLRs). The resultant negative regulatory program diminishes both proliferative responses and capacity for cytokine production in response to stimulation. Furthermore, anergic B cells have a shortened lifespan in a mixed repertoire, due to enhanced dependence on the cytokine B cell activating factor (BAFF) and increased expression of proapoptotic Bim. However, through high avidity stimulation of the BCR and in the presence of antigen-specific T cell help, these anergic B cells can initiate differentiation and proliferation and can be recruited into the immune response to foreign antigen. Compared to B cell tolerance, central T cell tolerance by thymic clonal deletion or anergy induction is more robust; however not all self-antigens are presented in the thymus and thus peripheral tolerance plays an important role in preventing autoimmunity. In vivo peptide-induced CD4+ T cell anergy has been described as a tolerogenic, autonomous mechanism induced in peripheral T cells that have experienced antigen stimulation in the absence of co-stimulation. Characterized by the inability to proliferate or produce cytokines upon re-stimulation, this hypo-responsive state cannot be reversed by IL-2, in contrast to in vitro antigen-experienced T cells re-stimulated with signal one alone. Alongside the shared phenotype of clonally anergic CD4+ T cells and tolerized B cells, the signaling pathways that characterize the two phenomena also have parallels. In both cell types, the NFAT pathway is selectively activated, modulating negative regulators that induce further downstream anergy factors necessary for the hypo-responsive state. Elucidating the shared and specific molecular mechanisms that block BCR and TCR signaling during the induction of lymphocyte tolerance may unlock new treatments for autoimmune and immunodeficient diseases. NDRG1 was previously identified as a clonal anergy factor upregulated in naïve and anergic CD4+ T cells by signal one alone via the TCR-dependent early growth response genes, Egr2 and Egr3, and degraded in a proteasome-dependent manner by co-stimulation. While in vitro induction of NDRG1 expression led to a hypo-responsive state in T cells, the loss of NDRG1 in vivo was described as partially rescuing T cells from peptide-induced tolerance. NDRG1 is a stress- and hypoxia-inducible 43 kDa protein regulating cell growth and differentiation, and reported to possess potent anti-metastatic activity in a cell type and tissue-specific manner. Though ubiquitously expressed in human tissues, the amount and subcellular localization of NDRG1 expression varies by cell type, with relatively higher staining in kidney, prostate, ovary, intestines and brain. Particular localization to the cytoplasm of Schwann cells in peripheral nerve tissue is important, since loss of NDRG1 causes sensorimotor neuropathy. NDRG1 expression is suppressed via metabolic regulators including N-myc, and the locus is hypermethylated in many cancer tissues including gastric, colon, pancreatic, prostate and some breast cancers. NDRG1-mediated inhibition of tumor growth and metastasis may explain the anti-cancer effects of iron chelating agents such as Dp44mT. However positive correlation between tumor tissue NDRG1 levels and cancer progression in some human studies, suggests pleiotropic and context specific roles, with silencing of NDRG1 inhibiting migration, invasion and viability of hepatocellular cancer and sarcoma cell lines. The potential for pharmacological targeting of NDRG1 in cancer highlights the importance of understanding the role of NDRG1 within adaptive immunity. In this study, we show that Ndrg1 is the third most differentially expressed gene in anergic B cells compared to naïve B cells and that the transcriptional regulation of Ndrg1 parallels that described in stimulated CD4+ T cells. Despite this expression pattern, our experiments using a Ndrg1−/− mouse model demonstrate that NDRG1 is not required for the tolerogenic responses downstream of self-antigen engagement in B cells. In contrast to the observations of Oh et al. when we replicate the experimental protocol of in vivo peptide-induced T cell anergy, we find NDRG1 does not affect immune response or IL-2 production in antigen-experienced CD4+ T cells. These findings re-position NDRG1 as a bystander in anergy and have implications for the development of NDRG1 as a therapeutic target in cancer.
To investigate the pathways underlying B cell anergy, we performed RNA Seq on B220+CD19+IgMa+CD93−CD21midCD23hi naïve and B220+IgMloIgDa+ anergic splenic B cell subsets sorted respectively from IgHEL tg, and double tg mice expressing IgHEL with or without sHEL as a self-antigen (IgHEL/sHEL) (Fig. 1a). Differential gene expression analysis revealed significant upregulation of anergy-associated transcription factors Egr2 and Egr3, as well as Egr2 dependent targets Nab2 and Nrgn, in anergic compared to naïve B cells, a pattern of expression consistent with NFAT signaling in anergic cells (Fig. 1a, b, Supplementary Table 1). Of the 868 significantly differentially expressed transcripts, Ndrg1 was identified as the third most upregulated in anergic B cells by fold change (log2 7.378), and eleventh highest by p value (7.41 × 10−117 adjusted; Supplementary Table 1). NDRG1 has been described as a CD4+ T cell anergy factor downstream of Egr2/3, which together with these findings suggested it might be a common participant in the regulation of lymphocyte tolerance. Significant differential expression of Ndrg1 was verified by qPCR of mRNA extracted from naïve IgHEL or anergic IgHEL/sHEL B220+ cells (1.424 ± 0.1807 vs. 20.05 ± 2.069, p value 0.0009), confirming the association between NDRG1 and B cell anergy.
In CD4+ T lymphocytes, NDRG1 has been proposed as an anergy factor, induced by signal one and inhibited by signal two. To investigate whether Ndrg1 shared a similar pattern of molecular regulation in B cells, qPCR was performed on mRNA isolated from primary IgHEL B cells stimulated for 24 h ex vivo via the BCR by anti-IgM or the cognate antigen sHEL (signal one), in the presence or absence of co-stimulation by anti-CD40 or lipopolysaccharide (LPS; signal two). Consistent with the transcriptional control pattern of other anergy factors, in vitro antigen-receptor signaling via signal one alone induced Ndrg1 mRNA expression in primary B cells, but addition of signal two stimulation was associated with a dose-dependent suppression of Ndrg1 mRNA (Fig. 2a). This pattern of Ndrg1 expression was also observed in vitro using A20 immortalized B cells engineered to express the IgHEL BCR (Fig. 2b), supporting the possibility that NDRG1 may play a role in the induction or maintenance of B cell anergy in central or peripheral tolerance.
To explore the role of NDRG1 in B cell immune function, we created a Ndrg1−/− mouse model using CRISPR/Cas9 directed mutagenesis (Supplementary Fig. 1a). The gene targeting produced an 11 bp deletion within exon 4 of the Ndrg1 gene in oocytes causing a frameshift mutation Ndrg1 null allele, which was propagated further to produce homozygote Ndrg1−/− mice at expected ratios from heterozygous parents (Supplementary Fig. 1b). PCR-amplification and subsequent Sanger sequencing of cDNA reverse transcribed from RNA isolated from WT and Ndrg1−/− kidney and splenic B cell lysates demonstrated that the edited RNA transcript is expressed in both tissues (Supplementary Fig. 1c, d) with the 11 bp deletion detectable within the transcript amplified from Ndrg1−/− kidney and B cell samples with no exon skipping or repair (Supplementary Fig. 1d). NDRG1 protein is expressed at very low levels in WT splenocyte samples and thus undetectable by transitional western blot, however knockout was confirmed by immunoblot analysis of WT and Ndrg1−/− in kidney lysates using a N-terminal binding antibody (Supplementary Fig. 1e). In splenic lysates, immunoprecipitation and blotting confirmed the complete loss of NDRG1 protein expression, likely a result of nonsense-mediated decay of the altered transcript (Supplementary Fig. 1f). In humans, nonsense Ndrg1 mutations are one of the causes of hereditary motor and sensory peripheral neuropathy-type Lom, also known as Charcot-Marie-Tooth disease type 4d (CMT4d). Consistent with the human disease, and previously reported neurological phenotyping of Ndrg1−/− mice, our Ndrg1−/− mice display a shaking, tremorous phenotype with muscle weakness and abnormal clasping of the hind limbs, and reduced proportion of unsupported versus supported rearing (Supplementary Fig. 1g), indicating hind limb weakness and providing further evidence that the protein had been correctly targeted. Ndrg1−/− mice were smaller than their WT littermate controls, as previously described (Supplementary Fig. 1h).
Immunophenotyping of Ndrg1−/− non-tg mice by flow cytometry demonstrated normal development of B cells in the BM (Fig. 3a, d), and no difference in the development, distribution or absolute numbers of peripheral B cell populations within the spleen or absolute B220+ B cell numbers in the mesenteric lymph nodes (MLNs) (Fig. 3b, e). Peritoneal B1 B cell populations were also comparable (Fig. 3c), indicating that Ndrg1 deletion has no effect on development or distribution of unperturbed B cell populations; and serum IgM, IgG and IgA titers were equivalent in Ndrg1−/− and WT mice (Fig. 3f). To investigate the requirement for NDRG1 during the response to stimulation by foreign antigens, Ndrg1−/− and WT B cells were cultured under a range of stimulatory conditions for 24 h and 72 h. The absence of NDRG1 did not alter the in vitro upregulation of activation markers CD69, CD86 or CD25, the extent of proliferation or class-switching in response to TLR ligands (Supplementary Fig. 2a–d).
To explore whether loss of NDRG1 is sufficient to break tolerance to self-antigen at resting state, we tested adult WT and Ndrg1−/− mouse serum for autoantibodies to nuclear epitopes (ANAs), a sensitive hallmark of autoimmune disease in both mice and humans. There was no increase in ANAs in the absence of NDRG1 expression (WT 1/9, 11.1% positive vs. Ndrg1−/− 1/9, 11.1% positive, P = 1, two-tailed Fisher’s exact test). To isolate and test for a specific defect in B cell tolerance, we investigated whether the provision of T cell help might be sufficient to induce autoantibodies to endogenous self-antigen in the absence of NDRG1. Accordingly, WT or Ndrg1−/− non-tg and WT or Ndrg1−/− sHEL mice were primed with HEL-conjugated to sheep red blood cells (HEL-SRBC) and then boosted with HEL-SRBC at 30 days (Fig. 4a). There was no difference in the humoral immune response between WT or Ndrg1−/− mice in these experiments. As expected, the sHEL expressing mice generated lower HEL-specific IgG titers than non-tg mice, despite equivalent GC formation across groups (Fig. 4b), indicating B cell tolerance to self-antigen, despite the availability of T cell help, remained intact in the absence of NDRG1 (Fig. 4c). To investigate whether NDRG1 might play a role in tolerance and anergy induction during B cell development in the context of self-antigen, we took further advantage of the IgHEL and sHEL-tg models. Lethally irradiated CD45.1+ non-tg and sHEL mice were reconstituted with WT or Ndrg1−/− CD45.2+ IgHEL BM (Fig. 4d). Irrespective of NDRG1, flow cytometric analysis showed downmodulation of surface IgMa on mature recirculating B cells in the BM and spleens of sHEL recipients, typical of anergy (Fig. 4e, f); and ex vivo stimulation of the same cells with sHEL showed a block in BCR signaling (Fig. 4g). Anti-HEL-specific IgMa was also equivalently suppressed in sHEL recipients reconstituted with WT or Ndrg1−/− IgHEL BM (Fig. 4h). These findings show that NDRG1 is dispensable for the IgM modulation, and block in both proximal BCR signaling and plasma cell differentiation, characteristic of anergic B cells. Persistent self-antigen stimulation of the BCR in anergic B cells also has the effect of inhibiting proliferative responses to LPS, an essential checkpoint to prevent autoimmunity during exposure to external co-stimulation such as with bacterial infection. To exclude the possibility that NDRG1 might be induced to mediate the inhibition of co-stimulation by LPS, we stimulated CTV-labeled WT and Ndrg1−/− IgHEL naïve and IgHEL/sHEL double tg anergic splenic B cells ex vivo with LPS and increasing amounts of sHEL as self-antigen. Proliferation was equivalently inhibited in Ndrg1−/− anergic B cells compared to WT, indicating that the refractory mechanisms in place to inhibit autoreactive B cell responses to LPS do not depend upon NDRG1 expression (Fig. 4i).
The antigen-induced differential signaling pathways in anergic B cells cause a shortened lifespan in a polyclonal repertoire, due to an inability to compete with non-anergic cells for BAFF, and increased expression of the proapoptotic protein, Bim. Since NDRG1 has been implicated in the induction of apoptosis, we hypothesized that NDRG1 might act downstream of signal one to modulate the threshold for survival in a diverse B cell repertoire. To test this, we reconstituted lethally irradiated non-tg or sHEL CD45.1+ mice with 80:20 mixtures of Ndrg1−/− or WT CD45.2+ IgHEL BM and WT non-tg CD45.1+ BM (Fig. 5a). Analysis of the reconstituted mice showed equivalent numbers of Ndrg1−/− and WT IgHEL B cells in a mixed repertoire (Fig. 5b, Table 1) and an equivalent increase in turnover, in the presence of self-antigen, as judged by BrdU labeling in vivo (Fig. 5c). These data show that NDRG1 is not required to maintain the rapid turnover of anergic B cells that have developed in the presence of self-antigen in the context of competition with non-autoimmune cells for BAFF. Similarly, NDRG1 deficiency did not produce an altered anergic response in B cells exposed to self-antigen in the presence of directly competing WT IgHEL anergic B cells in a mixed chimera model. Flow cytometric analysis of CD45.1+ sHEL recipients reconstituted with equal parts WT CD45.1+ IgHEL BM and either WT IgHEL or Ndrg1−/− IgHEL CD45.2+ BM showed comparable levels of surface IgM (Fig. 5d, e), and equivalent Bim expression between Ndrg1−/− and WT IgHEL mature B cell populations in the chimeric mice (Fig. 5f).
Our experiments have demonstrated that NDRG1 is dispensable for the induction and maintenance of anergy when B cells develop in the presence of self-antigen and when challenged with signal two alongside chronic BCR stimulation; however, this does not exclude a role for NDRG1 unique to migration and re-localization of antigen-stimulated B cells, which is linked to their ability to find and interact with T cell help. When naïve B cells first bind antigen, they relocate to the outer periarteriolar lymphoid sheath where they require signal two in the form of T cell help for survival and entry into the follicle to initiate a productive germinal center. Similarly, in the context of self-antigen and competing naïve cells, anergic B cells also relocate to the T cell rich cortical zones, where they may be rescued by T cell help; but otherwise typically die from a combination of insufficient engagement with BAFF and increased Bim-dependent apoptosis. To exclude a possible role for NDRG1 in these interactions, equal ratios of WT IgHEL CD45.1+ and WT or Ndrg1−/− IgHEL CD45.2+ splenocytes were labeled with CTV dye and transferred into non-tg or sHEL CD45.2+ recipients (Fig. 6a). After 48 h, the total number of transferred CTV+ HEL-binding IgHEL B cells were significantly reduced in sHEL compared to non-tg recipients, but ratios of CD45.1+:CD45.2+ surviving transferred HEL-binding cells were equivalent for WT and Ndrg1−/− donor populations (Fig. 6b). These findings show that NDRG1 does not play a role in the elimination of signal one experienced B cells in the periphery. This conclusion is supported by the equivalent induction of CD95, BAFF-R, Bcl2 and Bim proteins in the CD45.1+ WT and CD45.2+ Ndrg1−/− antigen-experienced B cells (Fig. 6c). In sHEL recipients, IgMa downmodulation and upregulation of the activation marker CD86 were similar in CD45.1+ WT and CD45.2+ Ndrg1−/− IgHEL B cells (Fig. 6c). Levels of localization markers CD23 and CXCR5 were also reduced equivalently on exposure to sHEL in CD45.2+ Ndrg1−/− antigen-experienced B cells, suggesting follicular exclusion is also unlikely to depend on NDRG1 expression (Fig. 6c). To further confirm that NDRG1 has no role in inducing apoptosis downstream of signal one in the absence of signal two, mixed cultures of WT CD45.1+ and WT or Ndrg1−/− CD45.2+ non-tg B cells were stimulated with a titration of plate-bound anti-IgM for 16 h in vitro. The ratio of CD45.1+:CD45.2+ B cells was not altered by increasing concentrations of anti-IgM in cultures (Fig. 6d), despite equal antigenic engagement as measured by CD86 upregulation (Fig. 6e), supporting our conclusion that NDRG1 does not induce B cell death in response to signal one in the absence of signal two.
The finding that NDRG1 is either dispensable or acts redundantly for the induction and maintenance of B cell anergy, led us to re-examine the evidence for NDRG1 regulation of induced T cell anergy. As previously reported, NDRG1 deletion had no effect on thymocyte development (Supplementary Fig. 3a, c) or maturation and distribution of peripheral T populations (Supplementary Fig. 3b, d). To confirm that NDRG1 acts as a T cell-specific anergy regulator, we recapitulated the previously described in vivo peptide-induced T cell anergy assay using our mouse model of NDRG1 deficiency. In these experiments, CD45.2+ ovalbumin (OVA)-specific TCR tg CD4+ OT-II+ T cells were isolated from WT or Ndrg1−/− OT-II+ tg mice and adoptively transferred into CD45.1+ congenic recipients, with subsequent intravenous injection of endotoxin-free OVA peptide on day 1. After 5 days, naïve and challenged splenic WT and Ndrg1−/− OT-II+ CD4+ T cells were harvested and stimulated in vitro (Fig. 7a). As reported previously, IL-2 concentration in the culture supernatant from antigen-experienced CD4+ T cells was lower compared to that from naïve cells (Fig. 7b; p = < 0.05 at all concentrations of OVA). However, we could not replicate the reported increase in IL-2 in Ndrg1−/− relative to WT antigen-experienced T cells (Fig. 7b). In contrast to some previous findings using a thymidine incorporation assay, flow cytometric analysis of CTV dilution reproducibly demonstrated a significantly enhanced proliferation response in antigen-experienced OT-II+ T cells compared to naïve (Fig. 7c; p = < 0.05 between 0.1 and 5 μg/ml OVA), irrespective of NDRG1 expression. Bystander WT CD45.1+ non-tg cells demonstrated no proliferation in cultures containing either WT or Ndrg1−/− OT-II+ cells at any concentration of OVA (Fig. 7c, red points). We therefore considered that the assay may represent a stimulatory response rather than anergy, and hypothesized that the lower IL-2 in the supernatant of antigen-experienced cells may consequently reflect a higher rate of consumption due to greater proliferative responses of these cells compared to cultures containing naïve cells. To test this, production of cytokines IL-2 and IFNγ was directly measured by intracellular staining. Ag-experienced populations produced more IL-2 and IFNγ in a dose-dependent manner than naïve CD4+ T cells, consistent with a stimulatory rather than anergic response (Fig. 7d, p = < 0.05 at 0.1–0.5 μg/ml OVA), a response that was unaffected by NDRG1 deficiency (Fig. 7d).
The development of more effective and better tolerated treatments for cancers, autoimmune and immunodeficiency diseases relies on defining the key molecular mechanisms that distinguish productive from tolerogenic lymphocyte responses. Despite the success of many monoclonal therapies targeting immune checkpoints, they are associated with an increased risk of autoimmune adverse events. Pre-clinical investigation into the immune consequences of anti-cancer therapy is therefore key to preventing serious immunopathology. NDRG1 is a stress- and hypoxia-inducible regulator of cell growth and differentiation, implicated in some contexts as a metastasis inhibitor due to involvement within multiple signaling pathways. Consistent with this, anti-cancer iron chelators, Dp44mT and DFO may act via NDRG1-upregulation. Pertinently for this study, in multiple human cancers increased tissue expression of NDRG1 has been associated with poor outcomes, with in vitro evidence for suppression of NDRG1 as a novel anti-cancer strategy. The therapeutic targeting of NDRG1 presents a promising treatment opportunity, but requires clarification of the immune effects of NDRG1 inhibition. In 2015, Ndrg1 was described as a T cell anergy factor, regulated by the signal one/signal two axis, induced downstream of NFAT and Egr2 upon TCR signaling and repressed by co-stimulation. Ndrg1−/− CD4+ T cells were reported to be partially resistant to peptide-induced anergy in vivo. Selective activation of the calcium-regulated transcription factor NFAT, and Egr family driven gene signatures, are known to contribute to both B and T lymphocyte unresponsiveness, with defects in maintenance of T and B cell tolerance, including increased production of self-reactive Ig, in NFAT1−/− mice. Our findings identified Ndrg1 as the third most upregulated gene in anergic B cells compared to naïve cells. The evidence for shared genetic regulation of Ndrg1 by the signal one and two costimulatory axis in B and T cells suggested that NDRG1 could represent a potential central factor regulating lymphocyte tolerance downstream of Egr2/3 and NFAT activation. Our experiments employing a CRISPR/Cas9 generated NDRG1-deficient mouse demonstrated that NDRG1 is either dispensable or acts redundantly within tolerance mechanisms during B cell development and activation. NDRG1 deficiency did not affect induction and maintenance of B cell anergy in the IgHEL > sHEL BM chimeric model, nor did it alter the maintenance of cell-intrinsic tolerance to self-antigen at resting state, upon immunization or during direct competition. We hypothesized that NDRG1 action may be limited to specific properties of B cell tolerance such as influencing the threshold for breakdown of tolerance in response to external stimulation, as Ndrg1 expression is closely regulated by signal two in B and T lymphocytes; however, the characteristic inhibition of signal two was intact in Ndrg1−/− anergic B cells. Furthermore, the loss of NDRG1 had no effect on the response of naïve splenic B cells exposed to self-antigen, or the induction of cell death due to the absence of rescue by signal two. There was no requirement for NDRG1 in these aspects of B cell tolerance, despite clear regulation of NDRG1 by the signal one/signal two axis in lymphocytes. To strengthen the findings from our mouse model of NDRG1 deficiency, and with the aim of demonstrating that NDRG1 functions in CD4+ T cell tolerance, we recapitulated an experimental design to show the role of NDRG1 in in vivo peptide-induced T cell anergy. In contrast to the results reported previously, we saw increased proliferation of endotoxin-free peptide antigen-experienced in vivo pre-stimulated CD4+ T cells, as would be expected during re-stimulation rather than an anergic response. The reported reduction in IL-2 in culture supernatant upon re-stimulation of antigen-experienced CD4+ T cells was reproducible, but NDRG1 deficiency did not affect this phenomenon. On further exploration, we found that the intracellular levels of IL-2 and IFNγ were higher in the antigen-experienced cells, consistent with increased consumption of cytokines associated with a higher proliferative response on re-stimulation, explaining the reduced IL-2 detected in the culture supernatants of these cells. The in vivo peptide-induced T cell anergy model is well described and characterized by reduced IL-2 in culture supernatant, though the response to IV peptide may be variable, and is not consistent in non-chimeric OT-II tg mice. Our use of CTV viable proliferation dye in contrast to thymidine incorporation to measure proliferation accounts for any disproportionate loss or inhibition of OT-II+ CD4+ T cells during in vitro culture. Though we find that non-tg cells from the transfer recipient contribute minimally to proliferation in antigen-experienced conditions, we cannot exclude a suppressive or cytotoxic effect on antigen-experienced OT-II+ cells from non-tg peptide experienced cells in vitro in previously described experiments; such effects have been shown to inhibit proliferation in an OT-I+ CD8+ in vivo peptide model. Together, these experiments suggest that this experimental system may not reliably test for peptide-induced CD4+ T cell unresponsiveness and call into question the proposed role of NDRG1 in vivo T cell anergy. In contrasting our results with the previous observations in T cells, we also note that where the model of NDRG1 deficiency described here was generated using CRISPR/Cas9 on a pure C57BL/6 background, the Ndrg1−/− mouse used in previous experiments exploring T cell anergy was created using 129 ES cells, a method which can be associated with co-transfer of immunomodulatory genes alongside the mutant gene. Although the mice were backcrossed for 8 generations, a 129 strain-specific immune effect cannot be fully excluded. Taken together our results position NDRG1 as a bystander, which may solely represent a biomarker of lymphocytes chronically stimulated by signal one alone. An alternative possibility is that anergy is maintained in the absence of NDRG1 due to functional redundancy. Mammals have 4 NDRG1 proteins, which share 53–65% homology and transcriptional repression via myc. The preservation of central nerves despite peripheral demyelination in CMT4d has been attributed to compensation by NDRG2–4, However, it is not clear if any such compensation is relevant outside the nervous system, and notably NDRG2–4 are not upregulated in anergic B cells (Fig. 1). When the anergic B cell engages antigen, failure to recruit Syk family kinases to the BCR results in Lyn mediated inhibition of the PI3K pathway via SHIP-1 and PTEN. NDRG1 has been shown to exist in a positive feedback loop with PTEN, whilst inhibiting PI3K. However, the lack of redundancy with loss of either SHIP-1 or PTEN suggests that NDRG1 does not act reciprocally with these proteins. In addition to a role in PI3K/AKT signaling, evidence from the cancer field indicates that NDRG1 can also modulate the TGFβ/SMAD and RAS/RAF/MEK/ERK axes, inhibit NF-κB and the ErbB receptors, suppress sonic hedgehog and modulate the phosphorylation of Cbl-b, Cbl-b itself associated with maintenance of B and T cell tolerance. NDRG1 also inactivates canonical Wnt/β-catenin signaling in BM stromal progenitor cells and cancer cells, while promoting β-catenin plasma membrane expression. Stable β-catenin expression on CD4 T cells has been linked to an unresponsive anergic like phenotype, while uncontrolled β-catenin accumulation in development induces B cell anergy. Furthermore, an independent compensatory role for NDRG1 within lymphocytes in pathways such as PI3K, Src or Wnt/β-catenin cannot be fully discounted; transcriptomic or proteomic profiling of Ndrg1−/− anergic B cells in comparison to WT anergic, and Ndrg1−/− naïve B cells may identify alterations in such pathways. Beyond tolerance, there is in vitro and in vivo evidence for immunomodulatory NDRG1 function in mast cells, where it appears to promote degranulation response to stimuli. However, while NDRG1 deficiency is a well-described cause of CMT in humans, autoimmunity or immunodeficiency are not described in CMT4d. The regulation of NDRG1 points to a central role in the cellular stress response. In addition to N-myc and C-myc, NDRG1 is upregulated after DNA damage via p53, by hypoxia via HIF1α and Egr1, and by PTEN. While the modulators of NDRG1 specific to B cells have not been well characterized, several known drivers of the anergic versus activation axis in B cells are linked to NDRG1 regulation. These include transcription factors, Egr2 and Egr3, downstream of NFAT activation in B and T cell anergy, of which Egr2 has been shown to regulate NDRG1 in T cell clones. A role for HIF1α in both B cell development and in regulating B cell tolerance has been reported and hypoxia and HIF1α-regulation of NDRG1 is well characterized. Downstream of BCR and BAFF-R stimulation, via PTEN/PI3K, the NF-κB pathway is key for B cell activation, proliferation and survival; Rictor, an essential mTOR component, is an NDRG1 regulator, and Rictor-deficient B cells have impaired BCR and BAFF-R engagement and associated reduced AKT (273) and NDRG1 phosphorylation. These shared regulatory pathways between B cell anergy and NDRG1 suggest possible explanations for the observed correlation between NDRG1 levels and anergy. In conclusion, we found no evidence that NDRG1 has any functional role during antigen-receptor signaling in B or T lymphocyte populations. We have shown that, while NDRG1 is dispensable for B cell tolerance, the association of NDRG1 expression with B cell anergy represents a biomarker for anergic cells, stimulated by the BCR in the absence of signal two, and reminiscent of other cell types under conditions of cell stress. The fact that NDRG1 expression was not required for the induction or maintenance of B cell anergy, or other processes of chronic BCR stimulation, enhances the potential for targeting NDRG1 for human anti-cancer treatment.
All procedures involving animals were performed in accordance with the Animals (Scientific Procedures) Act 1986, amended 2012, with procedures reviewed by the clinical medicine Animal Care and Ethical Review Body and conducted under Home Office Project License, P79A4C5BA. Mice were housed in individually ventilated cages, provided with food and water ad libitum and maintained on a 12 h light:12 h dark cycle (150–200 lux). The only reported positives on FELASA health screening over the entire time course of these studies were for Helicobacter spp, Chilomastix Sp, Enteromonas muris, Trichomonas Sp, mouse Norovirus, and Entamoeba spp. C57BL/6JOlaHsd mice were purchased from Envigo. IgHEL (C57BL/6-Tg(MD4)4Ccg/J), soluble HEL (sHEL) tg (C57BL/6-Tg(ML5)5Ccg/J) and OT-II tg mice (C57BL/6-Tg(TcraTcrb) 425Cbn/Crl) have been described previously and were maintained on a C57BL/6 background. Ndrg1−/− mice were generated by CRISPR/Cas9 directed mutagenesis on the IgHEL background as described in Supplementary Fig. 1a in collaboration with Dr Ben Davies, Transgenics Core, Wellcome Center for Human Genetics. CRISPR/Cas9 nuclease guide (5’GATGACAGGACGGTTGCCCTTGG) was designed against exon 4 of the Ndrg1 gene (ENSMUSG00000005125). Exon 4 is the most upstream exon of the Ndrg1 gene for which, if exon skipping occurred despite mutagenesis, the resulting transcript would still lead to nonsense-mediated decay. Complementary oligos for the guide sequence (5′-CACCGATGACAGGACGGTTGCCCT-3′, 5′-AAACAGGGCAACCGTCCTGTCATC-3′) were annealed, creating a compatible linker for cloning into CRISPR/Cas9 vector pX330-Puro, pre-digested with BbsI. A template for in vitro transcription using a T7 RNA polymerase was generated from this plasmid by PCR and used for guide-RNA preparation using the EnGen sgRNA synthesis kit (NEB), followed by purification of the resulting RNA with the Megaclear Clean-up kit (Ambion). Guide-RNA efficiency and specificity was tested by lipofection into murine melanocyte, B16F10 line. C57BL/6J oocytes derived from IgHEL studs were microinjected with 20 ng/μl guide-RNA and 20 ng/μl Cas9 mRNA before reimplantation into pseudo-pregnant foster mothers at the two-cell stage. Founder mice harboring an indel deletion in Ndrg1 were identified by PCR and Sanger sequencing using the following primers, Ndrg1 genomic set 1 for PCR and Sanger Sequencing; forward 5′-GGACTGTGCTTGTATGACATTC-3′, reverse; 5′-GTGTCCATAGTCAGTGGGTCAG-3′, Ndrg1 genomic set 2 PCR; forward 5′-CCAAACTCACGGTTCATGCC-3′, reverse 5′-CAGGTGATGGGCCTCTGTCT-3′, amplifying a 522 bp region and 151 bp region of Ndrg1 exon 4, respectively. Founder mice were then backcrossed for 7 generations with wild-type (WT) C57BL6/J mice, and intercrossed to generate homozygotes, selecting both Ndrg1−/− IgHEL tg and Ndrg1−/− non-tg animals for experiments. The following primers sets were used for PCR and Sanger sequencing of Ndrg1 mRNA, mRNA set 1 spanning whole CDS; forward 5′-ATGTCCCGAGAGCTACATG-3′ and reverse 5′-TTAGCAGGACACCTCCATGG-3′ and mRNA set 2; forward 5′-ATGTCCCGAGAGCTACATG-3′ and reverse 5′-AGTTGAAGAGGGGGTTGTAG-3′.
HEL-SRBC were prepared by incubating SRBC in Alsever’s solution (FirstLink) with 20 mg HEL per 10 ml 10% SRBC in conjugation buffer (0.35 M Mannitol, 0.01 M NaCl in HBSS) and later addition of 100 mg EDCI (Sigma Aldrich). After washing with HBSS, 200 μl 2 × 109 HEL-SRBC were injected intraperitoneally into each mouse.
For BM chimeras, sHEL and non-tg CD45-1+/+ or CD45-1+/− recipients were irradiated with two doses of 4.5 Gy, spaced by 3 h, and intravenously injected with at least 5 × 106 Ndrg1−/− IgHEL or WT IgHEL BM cells. All chimeras were given water treated with 1% Enrofloxacin antibiotic (Baytril, Bayer) for the first 3 weeks of reconstitution. BM chimeras were allowed to reconstitute for at least 7 weeks before immunization or analysis. Specified mice received BrdU supplemented with 1% sucrose for 7 days in their drinking water at a final concentration of 0.8 mg/ml.
Hind limb strength was measured by observing over 5 min the number of times an animal reared to stand, placing weight onto hind legs, either unsupported or supported, the ratio of unsupported/supported rearing was then calculated.
Total B cells were isolated from RBC-lysed splenocyte suspensions by positive selection using Miltenyi Biotec MACS LS columns and CD45R (B220) microbeads. B220+ or B220− splenocytes were cultured at a final density of 1–2 × 105 cells/mL in complete R10 (RPMI media with 10% FCS plus non-essential amino acids, 1 mM sodium pyruvate and 50 µM 2-β-mercaptoethanol, 20 mM hepes, 2mM L-glutamine, 10units/mL penicillin, 10 µg/mL streptomycin and 20 µg/ml neomycin) at 37 °C, 5% CO2. For RNA extraction, cells were washed with PBS after 24 h culture and pelleted for later RNA extraction as described below. For proliferation analysis, 106–107 B220+ or B220− cells were labeled with 2.5 µM CellTraceViolet (CTV) dye before culturing for 72–96 h prior to analysis by flow cytometry as below. The following stimuli were prepared as indicated in complete R10: anti-IgM, µ-chain specific (Jackson ImmunoResearch; 115-005-075), LPS from Salmonella species (Sigma Aldrich; L7770), HEL (Sigma Aldrich; L6876-5G), anti-CD40 (Biolegend; 102908), anti-CD3e (Biolegend; 100331), anti-CD28 (Biolegend; 122004) and recombinant mouse IL-4 (Biolegend; 715004).
A20 is a Balb/c B cell lymphoma line, originally sourced from ATCC, a gift from Professor Simon Davis, University of Oxford. HyHEL10 IgM heavy chain and kappa light chain were cloned from IgHEL mouse cDNA into pHR-SIN-CSGW vector under the control of the spleen focus forming virus promotor, then transfected into A20 B cells in which the endogenous BCR had been targeted by CRISPR-Cas9 gene editing. HyHEL10 BCR surface expression was confirmed by flow cytometry.
Cell suspensions were isolated in RPMI media containing 2% fetal calf serum (FCS), 10–20 mM Hepes and cells counted using WBC counting fluid (1.5% acetic acid, 0.5% methyl violet in water) and a haemocytometer. If required for downstream analyses, samples were RBC-lysed. 0.5–2 × 106 cells were aliquoted and washed with 200 μl FACS buffer (PBS with 2%FCS, 20 mM Hepes, 0.05% sodium azide). Cells were stained in antibody staining cocktail, then washed for acquisition. HEL-binding cells were detected by pre-incubation with 200 ng/ml HEL, washed with FACS buffer, then counterstained with HyHEL9 conjugated to pacific blue or FITC in house. Data were collected on a BD FACS CANTO10c. The following antibodies used during flow cytometric staining were from Biolegend; anti-B220 (1:400–500, 103236, 103232, 103212, 103243), anti-CD19 (1:400, 115528, 115530), Zombie Aqua live/dead (1:200, 423102, 423106), anti-IgM (1:400–500, 406508, 406512), anti-IgD (1:400, 405704, 405716, 405708), anti-IgDa (1:400, 406104), anti-IgMa (1:600, 408608), anti-CD23 (1:250, 101614), anti-CD93 (1:100, 136510), anti-CD21 (1:400, 123418, 123412), anti-CD24 (1:400, 101820, 101836), anti-CD5 (1:100, 100629), anti-CD86 (1:400, 105028), anti-CD44 (1:100, 103020, 103006), anti-CD25 (1:500:800, 102008), anti-CD4 (1:400, 100430), anti-CD8a (1:400, 100734), anti-CD62L (1:100, 104412), anti-CD3 (1:100, 100214, 100330, 100328), anti-CD69 (1:200, 104530), anti-CD45.1 (1:200, 110730, 110722, 110708), anti-CD45.2 (1:200, 109841, 109818, 109824), anti-BAFF-R (1:200, 134103), anti-CD95 (1:200, 152604) and anti-TCRVα2 (1:200, 127806). The following antibodies were from BD Pharmingen; anti-CD21 (1:400, 563176), anti-BP-1 (1:100, 553735), anti-IgMa (1:500, 553516), anti-CD43 (1:100, 562865), anti-IgM (1:500, 553437), phospho-PLCγ2 (1:25, 558498), phospho-BLNK (1:25, 558443), anti-BrdU (1:20, 364108), anti-CXCR5 (1:200, 145504), anti-Bcl2 (1:200, 633506), anti-Bcl2 quantification kit (1:5, 556537), anti-CD95 (1:200, 557653) and anti-IgG-1 (1:200, 563285). The following antibodies were from eBioscience anti-IgM (48-5890-82), anti-CD4 (1:400, 25-0041-82), phospho-ERK (1:50, 53-9109-42) and phospho-SYK (1:50, 12-9014-42). CellTraceViolet cell proliferation kit (C34557) was from ThermoFisher. Anti-Bim was from CST NEB (1:100, 10408S).
2–5 × 106 splenocytes were stimulated with 1μg/ml sHEL or 10μg/ml anti-IgM F(ab)2 (Jackson Immunoresearch) at 37 °C for 5 min and subsequently fixed and permeabilized using the BD Cytofix/Cytoperm kit in combination with Cytofix and Perm Wash buffer (BD Biosciences; 554655, 554722, 557885) before staining in intracellular antibody staining cocktail. To detect BrdU incorporation, surface marker stained cells were fixed using BD Cytofix/Cytoperm, treated with BD Cytoperm Permeabilization Buffer Plus (BD Biosciences; 561651) by protocol, and fixed again before treatment with 30μg DNase/106 cells for 1 h at 37 °C. Cells were further incubated with fluorescent anti-BrdU prior to acquisition.
Splenic B220+ CD19+ CD21mid CD23hi naïve follicular B cells were isolated from IgHEL tg mice and splenic B220+ IgMalo IgDa+ anergic B cells were sorted from IgHEL/sHEL double tg mice. Cell populations were surface stained and sorted with a FACS sorter (BD-AriaIII), and collected in ice-cold medium (50% FCS).
RNA data are deposited in Gene Expression Omnibus under accession number, GSE135650. DESeq2 v.1.28.1 was used for differential expression analysis between follicular and anergic B cell populations. Counts were transformed using variance stabilizing transformation for visualization in DESeq2. A gene was considered differentially expressed if the log2 fold change was > = ±1 and adjusted p value <0.05.
RNA was extracted from homogenized murine kidney lysates or 106–107 pelleted cells using the Qiagen RNeasy Plus kit (Qiagen; 74134) or using TRIzol extraction (Life Technologies; 12183555) by protocol. Equivalent quantities of RNA were reverse transcribed to cDNA using SuperScript II Reverse Transcriptase (ThermoFisher Scientific; 18064014), priming with Oligo(dT12–18) primer (ThermoFisher; 18418012). Expression of Ndrg1 and Gapdh mRNA was quantified by qPCR using SYBR Green PowerUp Master Mix (ThermoFisher Scientific; A25776) with the following primers Ndrg1 qPCR forward 5′-ACCCTGAGATGGTAGAGGGTCTC-3′, reverse 5′-CCAATTTAGAATTGCATTCCACC-3′, Gapdh qPCR forward 5′-TGTGTCCGTCGTGGATCTGA-3′, reverse 5′-TTGCTGTTGAAGTCGCAGGAG-3′, and run on a Bio-Rad CFX96 detection system. Standard curve construction demonstrated equivalent efficiencies of Ndrg1 and Gapdh cDNA amplification, therefore changes to Ndrg1 mRNA levels were directly calculated relative to endogenous Gapdh expression using the following equation: 1/(2^(Ndrg1 Ct – Gapdh Ct))*100.
Kidney protein lysates were produced by homogenization in lysis buffer containing 50 mM Tris-HCl pH7.4, 0.5%NP40, 150 mM NaCl, 20 mM MgCl2, protease and phosphatase inhibitors (Sigma Aldrich; 4906837001/5892970001), or in supplemented RIPA buffer (Santa Cruz; sc-24948). 1–25 μg protein lysate was reduced by addition of SDS-containing reducing buffer containing 0.1 M DTT and denatured by boiling at 95 °C for 5 min and loaded onto 4–12% Tris-HCl gels (ThermoFisher; NP0323BOX). Proteins were transferred to a nitrocellulose membrane, blocked with 5% BSA or 5% non-fat milk and probed with indicated antibodies. Antibodies; anti-NDRG1 D6C2 mAb (Cell Signaling Technologies; #9408, 1:1000) and anti-rabbit IgG, HRP-linked antibody (Cell Signaling Technology; #7074). Monoclonal anti-β-actin-peroxidase (Sigma; A3854, 1:10000) was used to detect the loading control and ECL prime (Amersham; RPN2232) was used for signal detection.
Immunoprecipitation of NDRG1 protein was performed using Dynabeads Co-Immunoprecipitation Kit (Invitrogen; 14321D). Following the kit specific protocol, rabbit α-NDRG1 antibody (Abcam; 196621) was conjugated to superparamagnetic Dynabeads M-270 Epoxy beads at 5μg per 1 mg of beads and incubated with protein extracted from freshly isolated tissue for 30 min at 4 °C. Purified NDRG1 was analyzed by Western blot, probed using a primary rabbit α-NDRG1 antibody (Abcam; 196621; 1:500) and secondary peroxidase conjugated α-rabbit IgG antibody (Jackson; 111-035-144, 1:500). Protein was detected with the chemiluminescent ECL Prime Western Blotting Detection Reagent (Cytiva; RPN2232).
For HEL-binding IgMa or IgG, plates were coated with 10 μg/ml HEL in carbonate buffer pH9.6 (20 mM Na2CO3, 35 mM NaHCO3), washed with wash buffer (PBS/Tween20), then blocked with PBS, 1%BSA. After washing, serially diluted serum samples were added in PBS/0.1%BSA, after incubation and washing, plates were incubated with 0.5 μg/mL biotinylated anti-IgMa (BD Pharmigen, #553515; clone DS-1) or anti-IgG-HRP (Bethyl laboratories) in 1%FCS, 1% milk powder, 0.1% Tween20 and NaN3 in PBS, then washed. Avidin-alkaline phosphate (A7294) was added to the wells at 1:3000 in PBS/0.1%BSA and incubated, then washed, then plates developed with addition of 1 mg/ml Sigma 104 phosphatase substrate (#104105) in 50 mM Na2CO3, 0.5 mM MgCl2 pH9.8. Anti-IgG-HRP (Bethyl laboratories; A90–131P) was used to detect serum HEL-specific IgG. Absorbance was measured at 405 nm using a Bio-Rad model 550 Microplate reader. Background absorbance values were subtracted from absorbance readings before interpolation to standard curve using Hyperbola (X as concentration). Bethyl laboratories mouse IgG (E90–131), IgM (E90–101) and IgA (E90–103) quantification kits were used by protocol with serum titration of samples as follows: IgG 1:4000, IgM 1:2000, IgA 1:2000, developed using TMB substrate (Life Technologies; 00-4201-56) and detected at 450 nm. IL-2 concentration in the supernatant was quantified by Mouse Ready-Set-Go IL-2 ELISA kit (eBioscience; 88–7024) and developed using TMB substrate as above.
For ANA staining, serum samples were diluted at 1:100 in PBS and stored at −20 °C before incubation on Hep-2 cell coated slides (A.Menarini; 37806). Slides were washed with PBS, then water, then incubated with FITC-conjugated, goat anti-mouse IgG (ThermoFisher; 62–6511). After washing, slides were mounted and imaged on Nikon wide-field TE20000U Microscope (GFP channel: 20×, 400 ms) then analysed with automated cell segmentation and fluorescence intensity quantification with ImageJ. Positive scoring samples (2+) were manually cross-checked before scoring.
CD45.2+ CD4+ T cells were isolated from WT OT-II+ or Ndrg1−/− OT-II+ tg mice and adoptively transferred to CD45.1+ non-tg congenic recipients, with subsequent intravenous injection of 500μg endotoxin-free OVA peptide on day 1 (Insight Biotech; 21-51023-G (323–339)), as tested using Pierce LAL Chromogenic Endotoxin Quantitation kit (ThermoFisher; #88282). 5 days later, total splenic CD4+ cells were magnetically isolated from recipient mice and naïve unstimulated WT or Ndrg1−/− OT-II+ control mice, before loading with CTV dye. As determined by flow cytometry, 4 × 104 CD45.2+ Vα2+ OT-II+ CD4+ T cells were cultured in vitro with a titration of OVA peptide and 4 × 105 irradiated splenocytes. IL-2 in culture supernatant at 48 h was measured by ELISA and proliferation of CD45.2+Vα2+ OT-II+ CD4+ T cells was detected by flow cytometry at 72 h.
GraphPad Prism Software was used for statistical analyses and unless otherwise specifically mentioned unpaired, two-tailed Student’s t tests were used for statistical comparison between groups, correcting for multiple comparisons using the Holm-Sidak method. Experimental groups were determined by genotype and were therefore not randomized and were not blinded. Experimental animals were not excluded from analysis except according to pre-specified experimental design on the basis of failed chimeric reconstitution or failed cell transfer. All experiments included age and sex matched controls, which were co-housed littermates wherever possible. Sample sizes were selected on the basis of previously published studies. Data shown is pooled from, or representative of replicate experiments as indicated in figure legends.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9649594 | Laís Ferraz Brito Sousa,Hellen Braga Martins Oliveira,Nathan das Neves Selis,Lorena Lobo Brito Morbeck,Talita Costa Santos,Lucas Santana Coelho da Silva,Jully Chayra Santos Viana,Mariane Mares Reis,Beatriz Almeida Sampaio,Guilherme Barreto Campos,Jorge Timenetsky,Regiane Yatsuda,Lucas Miranda Marques | β-caryophyllene and docosahexaenoic acid, isolated or associated, have potential antinociceptive and anti-inflammatory effects in vitro and in vivo | 10-11-2022 | Cytokines,Infectious diseases,Cellular microbiology | Inflammation is a complex biological response involving the immune, autonomic, vascular, and somatosensory systems that occurs through the synthesis of inflammatory mediators and pain induction by the activation of nociceptors. Staphylococcus aureus, the main cause of bacteremia, is one of the most common and potent causes of inflammation in public health, with worse clinical outcomes in hospitals. Antioxidant substances have been evaluated as alternative therapeutic analgesics, antioxidants, anti-inflammatory agents, antitumor agents, and bactericides. Among these, we highlight the essential oils of aromatic plants, such as β-caryophyllene (BCP), and polyunsaturated fatty acids, such as docosahexaenoic acid (DHA). The objective of this study was to evaluate the biological activities of BCP–DHA association in in vitro and in vivo experimental models of antinociception and inflammation. To determine the anti-inflammatory effects, monocytes isolated from the peripheral blood of adult male volunteers were infected with methicillin-resistant S. aureus and incubated with treatment for cytokine dosage and gene expression analysis. Antinociceptive effects were observed in the three models when comparing the control (saline) and the BCP-DHA treatment groups. For this purpose, the antinociceptive effects were evaluated in animal models using the following tests: acetic acid-induced abdominal writhing, paw edema induced by formalin intraplantar injection, and von Frey hypernociception. There was a significant reduction in the GM-CSF, TNFα, IL-1, IL-6, and IL-12 levels and an increase in IL-10 levels in the BCP-DHA treatment groups, in addition to negative regulation of the expression of the genes involved in the intracellular inflammatory signaling cascade (IL-2, IL-6, IRF7, NLRP3, and TYK2) in all groups receiving treatment, regardless of the presence of infection. Statistically significant results (p < 0.05) were obtained in the acetic acid-induced abdominal writhing test, evaluation of paw edema, evaluation of paw flinching and licking in the formalin intraplantar injection model, and the von Frey hypernociception test. Therefore, BCP and DHA, either administered individually or combined, demonstrate potent anti-inflammatory and antinociceptive effects. | β-caryophyllene and docosahexaenoic acid, isolated or associated, have potential antinociceptive and anti-inflammatory effects in vitro and in vivo
Inflammation is a complex biological response involving the immune, autonomic, vascular, and somatosensory systems that occurs through the synthesis of inflammatory mediators and pain induction by the activation of nociceptors. Staphylococcus aureus, the main cause of bacteremia, is one of the most common and potent causes of inflammation in public health, with worse clinical outcomes in hospitals. Antioxidant substances have been evaluated as alternative therapeutic analgesics, antioxidants, anti-inflammatory agents, antitumor agents, and bactericides. Among these, we highlight the essential oils of aromatic plants, such as β-caryophyllene (BCP), and polyunsaturated fatty acids, such as docosahexaenoic acid (DHA). The objective of this study was to evaluate the biological activities of BCP–DHA association in in vitro and in vivo experimental models of antinociception and inflammation. To determine the anti-inflammatory effects, monocytes isolated from the peripheral blood of adult male volunteers were infected with methicillin-resistant S. aureus and incubated with treatment for cytokine dosage and gene expression analysis. Antinociceptive effects were observed in the three models when comparing the control (saline) and the BCP-DHA treatment groups. For this purpose, the antinociceptive effects were evaluated in animal models using the following tests: acetic acid-induced abdominal writhing, paw edema induced by formalin intraplantar injection, and von Frey hypernociception. There was a significant reduction in the GM-CSF, TNFα, IL-1, IL-6, and IL-12 levels and an increase in IL-10 levels in the BCP-DHA treatment groups, in addition to negative regulation of the expression of the genes involved in the intracellular inflammatory signaling cascade (IL-2, IL-6, IRF7, NLRP3, and TYK2) in all groups receiving treatment, regardless of the presence of infection. Statistically significant results (p < 0.05) were obtained in the acetic acid-induced abdominal writhing test, evaluation of paw edema, evaluation of paw flinching and licking in the formalin intraplantar injection model, and the von Frey hypernociception test. Therefore, BCP and DHA, either administered individually or combined, demonstrate potent anti-inflammatory and antinociceptive effects.
Inflammation is a physiological reaction to tissue injury and damage resulting from the presence of trauma, infection, foreign bodies, or immune reaction, which occurs with the synthesis of inflammatory mediators and pain induction by activation and sensitization of nociceptors. Pain plays a crucial role in the body, protecting against harmful stimuli and promoting the repair of local injuries. The coding and neural processing of harmful stimuli are referred to as nociception, a process that begins with the transformation of environmental stimuli (physical or chemical) into action potentials at pain receptors (nociceptors) located in peripheral nerve fibers and culminates in their transfer to the central nervous system through signal transduction. The largest defense mechanism is in the innate immune system, where there is invasion of microorganisms and tumor cells through the action of macrophages, neutrophils, and dendritic cells. The adaptive immune system includes the activity of more specialized cells, such as B and T lymphocytes, which act in the eradication of pathogens through the production of specific receptors and antibodies. Inflaming is therefore a vital step for the body, as it contributes to the return of homeostasis. At the tissue level, inflammatory signs of pain, flushing, heat, edema, and loss of function result from cellular responses to damage. In this environment, several inflammatory mediators are synthesized and secreted, and are divided into two classes: pro- and anti-inflammatory. Examples include cytokines (interferons, interleukins, and tumor necrosis factor-alpha), chemokines (monocyte chemoattractant protein-1), eicosanoids (prostaglandins and leukotrienes), and the nuclear transcription factor NF-ĸB. Interleukin 10, however, has both pro- and anti-inflammatory properties and is therefore considered to be regulatory. One of the most common and impactful causes of inflammation in public health is S. aureus, a gram-positive bacterium found in the nasal microbiota of 20–40% of the human population. The rupture of mucous and cutaneous barriers through chronic diseases, skin lesions, or surgical interventions allows S. aureus to enter tissues and the bloodstream, causing infection. This bacterium may be susceptible to the antibiotic methicillin (MSSA) or methicillin-resistant (MRSA), the latter of which was first described in 1961 and has since become the leading cause of bacteremia with highly poor clinical outcomes such as endocarditis, osteomyelitis, sepsis, septic shock, and high hospital costs. Since the discovery of penicillin in the 1990s, the interest in natural products has increased with regard to the inhibition of free radicals, attenuation of inflammation, and in the antibiotics sector. Simultaneously, bacterial resistance has increased due to the frequent prescription of an against non-bacterial infections and its indiscriminate and irregular use, leading to sublethal doses that allow an increase in the resistance spectrum of microorganisms. Therefore, the choice of antibiotics to treat MRSA infections is restricted. Among the natural products, essential oils (OE) and volatile and strong-smelling substances present in aromatic plants have been studied in various contexts. β-caryophyllene (BCP), a bicyclic sesquiterpene found in the OE of Cannabis sativa, cinnamon, clove, oregano, copaiba oil-resin, and black pepper, exhibits strong anti-endemic, anti-inflammatory, antitumor, and bactericidal properties. Polyunsaturated fatty acids such as docosahexaenoic acid (DHA), an omega-3 derivative, have also been studied in the modulation of inflammation through the synthesis of bioactive mediators that act in inflammation resolution. It has also been shown that BCP may have improved effects when administered with DHA; however, only the antinociceptive activity of this combination has been tested thus far in experimental models in vivo and in vitro. In a previous study, we analyzed the anti-inflammatory activity of β-caryophyllene and docosahexaenoic acid in a model of S. aureus-induced sepsis in Balb/C male mice. A positive effect was observed in the reduction of neutrophil migration in the carrageenan-induced peritonitis model, reduction of total and differential leukocyte counts after bacterial infection, lower neutrophil migration in the histological analysis of the kidneys and lungs, and lower bacterial load in the treated group. From this we triage the doses to be studied later. Therefore, the aim of this study was to determine the anti-inflammatory and antinociceptive activities of β-caryophyllene (BCP) and Docosahexaenoic acid (DHA), isolated and associated, in vitro and in vivo experimental models.
The volunteers were healthy and fit for the clinical trial according to laboratory analyses. The peripheral blood mononuclear cells (PBMCs) inoculated with the S. aureus C80 strain showed a significant increase in GM-CSF production when compared to the control group (inoculated with sterile saline, BCP, DHA, and B/D) (Fig. 1A). Both the infected and treated groups and the treated and infected groups exhibited a reduction in the expression of this cytokine when compared to the infected-only groups. Those who were first infected and later treated with BCP and DHA had a significant reduction in TNF-α expression compared to the group infected with C80, whereas the group infected and treated with BCP-DHA showed significance with regard to LPS (Fig. 1B). The group that was treated and later infected showed a TNF-α reduction in comparison to the C80 group. These treatments caused a significant reduction in IL-1β expression, and the group previously infected and treated showed a greater decrease in this cytokine (Fig. 1C). Figure 1D shows that there was a significant reduction in IL-6 expression in the treated groups compared to those infected only with C80. The groups infected and treated with BCP and with the BCP-DHA combination showed a significant decrease in IL-12 expression (Fig. 1E), whereas the highest IL-10 expression was observed in the group treated with DHA and infected with S. aureus (Fig. 1F). Figure 2 illustrates the comparative analysis of the treated group with the infected group with regard to innate and adaptive immunity genes. The innate immunity genes CCL5, IL-2, and IL-6, which are related to the synthesis of pro-inflammatory cytokines IRF7, NLRP3, TLR6, TLR7, and TYK2, associated with standard recognition and the defense response of the organism, were under-expressed with BD treatment. MYD88, the defense response gene, was hyper-expressed. Comparison between the infected and treated groups with the group that was only treated revealed that there was no hyperexpression of any gene with statistical significance; however, pro-inflammatory cytokine activation genes such as CCL2, CSF2, IL-2, IL-6, and IL-17A, and those involved in the standard recognition and defense response to viruses and bacteria such as CD40, IRF7, LY96, NLRP3, and TYK2 were under-expressed. The comparison between the treated and infected versus infected-only groups showed significant under-expression of CCL2, CCL5, CSF2, IL-2, IL-6, IL-17A, CD40, IRF7, NLRP3, LY96, and TYK2, as in the previous groups, as well as in the CCR5-Th1 adaptive response gene marker, and hyperexpression of TLR7 and CCR4-Th2 markers. In the acetic acid test, there was a lower number of abdominal writhing among females in the indomethacin-treated group, followed by the isolated BCP and BD association groups (Fig. 3A). In males, there was statistical significance in all test groups regarding the control, and the BCP group presented similar results to indomethacin, but with no statistical difference between the groups (Fig. 3B). When comparing the behaviors between the sexes of the treated groups, there was an observable statistical difference only in the comparison of the saline control group (Fig. 3C). In the neurogenic phase of the formalin test (first 5 min), the group treated with BCP demonstrated a significant reduction in the number of flinches in females compared to the saline group, which was not observed in the other treatment groups (Fig. 4A). There was no statistical difference in the male evaluations during the same period (Fig. 4B). In the comparison between sexes (Fig. 4C), it was observed that the nociception levels of the males were higher than those of females, but a statistical difference was found only in the group treated with the standard analgesic. When observing the inflammatory phase (5–30 min), the three treatments caused a significant reduction in the number of paw flinches in females, and the group treated with the BCP and DHA combination presented a similar result to that of the group that received morphine (Fig. 4D). In males, the best results achieved in the 5–30 min period were from the groups treated separately with BCP and DHA (Fig. 4E), but they showed a higher number of flinches when compared to the females, with proven statistical significance in the group treated with the BCP and DHA combination and with morphine (Fig. 4F). Figure 5 shows the paw weight results. Only female rats had reduced paw edema in the isolated BCP groups and their association with DHA. The comparison between sexes also showed no statistically significant differences. The three treatments significantly reduced the intensity of mechanical nociception measured using the von Frey method in female and male mice, with the best results obtained in the BCP groups in females and DHA groups in males (Fig. 6A,B). As shown in Fig. 6C, males were more sensitive to hypernociception in the BCP-treated group, with statistical significance, whereas females were more sensitive in the DHA-treated and B/D-associated groups, but without statistical significance.
Natural products have long been used in medicinal preparations and studied in the development of medicines, such as analgesic, antimicrobial, antitumor, and anti-inflammatory treatment. The mechanisms of inflammation and infection constitute important research focuses, as they involve several cellular processes, such as chemotaxis, phagocytosis, immune response, differentiation, and apoptosis. In the MRSA infection model in our study, the cytokine dosage showed that treatment with BCP-DHA significantly reduced the levels of GM-CSF, TNFα, IL-1, IL-6, and IL-12. The synthesis of IL-10 was higher in the previously treated and later-infected groups and lower in the infected and later-treated groups. GM-CSF is a hematopoietic growth factor known for its ability to form granulocyte and macrophage colonies, with its receptor activation, the GM-CSFr, as an initiator to an intracellular signaling cascade that includes signal transducer activation and transcription activator 5 (STAT5), Janus kinase (JAK), MEK/ERK pathway, phosphatidylinositol 2-kinase (PI3K), and NF-kB acting on myeloid cells in inflammation and autoimmune diseases. In the aging model and memory analysis used by Lindsey et al., BCP had no effect on this cytokine. In addition, the presence of DHA in the medium containing GM-CSF resulted in depletion of the hematopoietic stimulant factor in a cystic fibrosis model, showing that the deficiency of AGPI and its derived inflammation resolution is limiting to the unregulated inflammation present in the disease. TNF-α has broad functional relevance, mainly in terms of stimulation of cellular apoptosis through Faz and caspases, and pro-inflammatory responses through NF-ĸB activation. In a previous study, an increase in TNF-α in the group treated with the BCP-DHA combination was found in the sepsis model, which may be related to its susceptibility to stimulation by COX inhibitors. Both natural products develop this role. We now find divergent results with decreased concentrations of this cytokine using an in vitro model. The main IL-1 β secretors are hematopoietic cells such as monocytes, macrophages, microglia, Kupffer cells, and dendritic cells after activation by damage-associated molecular patterns (DAMPs). It is a cytokine that is closely linked to inflammation and fever, and its synthesis is associated with inflammasome activation. The pro-inflammatory characteristic also represents IL-6, a cytokine involved in immune responses, hematopoiesis, bone metabolism, the development of autoimmune diseases, bacterial infections, and metabolic side effects. In vitro research suggests that the BCP agonist activity on CB2r is the main mechanism responsible for the inhibition of pro-inflammatory pathways and cytokine synthesis, such as IL-1β, IL-6, and TNF-α. Inhibition of IL-1β and TNF-α by DHA has also been described in the literature, with emphasis on greater PPAR-γ activation and NF-ĸB inhibition. The IL-12 cytokine family is involved in modulating the behavior of T-cell populations and targeting immune responses in various diseases owing to their pro-inflammatory role. They are link between innate and adaptive immunity, mediating the differentiation of naive CD4 T cells into T helper subtypes, and regulating the functions of different cell effectors. A reduction in IL-23, a cytokine of the IL-12 family, was observed in the animal senescence model treated with BCP, and DHA inhibited dendritic cell maturation, reducing IL-12 family levels (IL-12p70, IL-23, and IL-27). Following a regulatory pathway, the IL-10 family plays an important role in regulating the immune response during host defense, autoimmune and inflammatory diseases, and cancer. IL-10 acts primarily with immunosuppressive functions on leukocytes, whereas other members of the family act preferentially on epithelial cells, where they control defense mechanisms against viral, bacterial, and fungal infections, protect tissue integrity, and promote repair and regeneration. DHA supplementation promotes the reduction of IL-10 synthesis by conferring cardioprotection in a myocardial injury model. A study with the essential oil of Polygonum minus identified BCP as one of its components and used it as a positive control in the cisplatin-induced hepatotoxicity model, demonstrating a reduction in the concentration of IL-10 and, therefore, hepatoprotection. Therefore, the reduction in our study demonstrates protection against exacerbation of inflammation during the infectious process. Many anti-inflammatory effects occur in the body by altering the expression patterns of innate immune genes. In our study, we observed that treatment with BCP-DHA, regardless of the presence of infection, negatively regulated the gene expression of IL-2, IL-6, IRF7, NLRP3, and TYK2, acting directly in NF-ĸB inhibition and the synthesis and activity of pro-inflammatory cytokines (Fig. 7). Within the interferon (IFN) signaling pathway, IRF7 has been recognized as the major signaling regulator of IFN type 1 and is involved in the control of excessive inflammation and autoimmunity. In a silica-induced lupus model, DHA inhibited the expression of IRF7 and other IFN-associated genes involved in the inflammatory response. In the inflammatory pathway, NLRP3, a component of the inflammasome cytosolic multiprotein complex generated in bone marrow-derived macrophages, stimulated by microbial agents, is also involved. The NLRP3 inflammasome plays a critical role in innate immunity by stimulating active caspase-1 synthesis, which converts IL-1β and IL-18 precursors into their biologically active forms in a sequence of inflammatory responses that leads to pyroptosis. Inhibitory activity of BCP on NLRP3 was observed in a gouty arthritis model in animals and of DHA on the NLRP3 inflammasome induced using silica inhalation. The role of BCP-DHA in the negative regulation of the non-receptor tyrosine kinase TYK2, whose activation regulates the signal transduction pathways of IL-12, IL-23, and IFN type 1 receptors, has brought novelty to the literature, each of which is implicated in the pathogenesis of various inflammatory diseases. Under infectious stimuli, we observed that there was also an under-expression of genes related to inflammatory pathways, such as CCL2, CSF2, CD40, and LY96. CCL2 is a monocyte chemoattractive protein expressed in endothelial cells, smooth muscles, fibroblasts, astrocytes, T cells, and tumor cells after an inflammatory stimulus (e.g., IL-1β, IL-6, TNFα, LPS, and GM-CSF). Treatment with DHA led to rapid and limited CCL2 expression in a myocardial injury model, indicating restricted inflammation. In a dextran sulfate-induced colitis, there was negative regulation of CCL2 expression after treatment with BCP. CSF2, which encodes the GM-CSF cytokine, is directly involved in the conversion of the M1 to M2 phenotype in macrophages and plays a crucial role in inflammation. CD40 belongs to the TNFα family and therefore has pro-inflammatory and pro-thrombotic effect. As mentioned previously, BCP and DHA act by negatively regulating the expression of GM-CSF and TNF-α cytokines. LY96, or myeloid differentiation factor 2 (MD2), is a co-receptor that, by identifying the LPS, is coupled to TLR4, forming the LPS-MD2-TLR4 complex, and promotes the recruitment of myeloid differentiation protein (Myd88), activating the MAPKs and NF-kB, resulting in stimulation of the synthesis of pro-inflammatory cytokines. The literature describes the agonist effects of BCP on CB2r, including inhibition of the CD14/TLR4/MD2 complex as a potent pathway for regulating inflammation. It has recently been suggested that DHA inhibits the formation of this complex, preventing the intracellular inflammatory signaling cascade by competing with LPS for the binding site. In the treated-only group, positive regulation of Myd88 was observed; this is a downstream TLRs adaptor protein, with the exception of TLR3, which is critical for signal transduction in the pathway that results in NF-ĸB activation. Although studies have shown that BCP and DHA promote the suppression of Myd88 expression, the present study showed the opposite effect. In the treated and later infected groups, there was increased expression of TLR7 and CCR4, a chemokine receptor predominantly expressed by Th2 cells and related to inflammatory diseases and T cell neoplasms, such as leukemias and lymphomas. The above observations show the relevant antinociceptive potential of the BCP-DHA combination with results compatible with the first-choice drugs in the nociception tests performed, as well as its anti-inflammatory ability by significantly inhibiting the cytokine levels of pro-inflammatory GM-CSF, TNFα, IL-1, IL-6, and IL-12. The present study showed the negative regulation of crucial genes in the inflammatory process, such as IL-2, IL-6, IRF7, NLRP3, CCCL2, CSF2, CD40, and LY96, whose pathways result in the inhibition of NF-ĸB dissociation and in the synthesis of the pro-inflammatory cytokine/acting. TYK2 inhibition through treatment using BCP-DHA was also shown for the first time. In the present study, we showed that BCP-DHA association and isolated BCP presented the best antinociceptive responses in the acetic acid-induced writhing test in both sexes. The formalin test demonstrated a significant reduction in the number of flinches in females treated with BCP during the neurogenic phase. In the inflammatory phase, a better treatment result was observed with BCP-DHA in relation to morphine in females, and better results were seen for isolated compounds in males. Reduced paw edema was also observed in females of the mentioned groups. Both isolated and combined BCP and DHA promoted a significant reduction in mechanical nociception in intraplantar Cg injections in the von Frey test in females and males. Our findings of better pain responses of isolated BCP and BCP-DHA are similar to those found by Fiorenzani et al. with the formalin test in the first study that associated these substances, except that they worked only with male rats, and the present study used both sexes. Recent studies have shown that differences between sexes contribute to individual differences in pain perception and treatment, but the specific mechanisms of disparity remain inconclusive. It is possible that interactions between biological, sociocultural, and psychological factors, mainly sex hormones (androgens and estrogens), the endogenous opioid system, and genetic factors, are involved in the process. Experimental pain models highlight the pronociceptive role of estrogen in male rats that received intracerebroventricular estradiol injections, as well as the antinociceptive effect on neuropathic pain in mice. Male and female rats reacted differently to structural and functional changes related to pain induced by sciatic nerve ligation, with males showing gradual allodynia reduction and complete recovery, whereas allodynia and gliosis in females lasted for 4 months, suggesting that male results are associated with testosterone and that females are sensitive to changes in serum testosterone. Overall, women appear to have greater pain sensitivity and better pain responses than men. In addition, studies suggest that there are differences in responses to pharmacological and non-pharmacological treatments, depending on the type of treatment and the characteristics of the pain. Nociception, as a part of the inflammatory process, can be neurogenic or inflammatory, and its stages depend on the duration of the process, as well as immunological factors. The antinociceptive effect of BCP was described in the acetic acid test, with a result similar to that of indomethacin in neuropathic pain in a model of diabetes induced by streptozotocin in mice and in study that used a partial ligation model of the sciatic nerve in mice. This may be due to its binding to the CP55.940 site of cannabinoid receptor type 2 (CB2r), which leads to inhibition of microglial activation during neuropathic pain, causing analgesia through supraspinatus, spinal, and peripheral CB2r activation. The antinociceptive activity of DHA in thermal and chemical pain models is abolished in the presence of naloxone, indicating that it acts in the opioid pathway as a receptor antagonis. Therefore, the potential antinociceptive effects of this combination were highlighted in this study. In addition, our results strongly suggest a possible new therapeutic alternative in the context of pain and inflammation, requiring further study to improve the doses and routes of administration.
This study was conducted at the Microbiology and Immunology Laboratory of the Multidisciplinary Health Institute of the Bahia Federal University (IMS/UFBA) after approval was obtained from the Ethics Committee on Research on Human Beings (CEPSH) and the Ethics Committee on Animal Use (CEUA) of the Bahia Federal University, under protocols nos. 2,791,699 and 060/2018, respectively. All procedures were initiated only upon approval by the latter, and after the donors provided informed consent. The 80 strains of methicillin-resistant S. aureus (MRSA) used in the study were isolated from the nasal swabs of healthy children, aged between 6 and 8 years, from daycare centers located in Vitória da Conquista, Bahia, Brazil. The strain was obtained from another study approved by the IMS/UFBA CEPSH (protocol no. 08731912.5.0000.5556).
BCP and DHA marketed by Sigma-Aldrich® were used. DHA was diluted in 10% ethanol, as recommended by the manufacturer. Doses used were defined based on a previous study.
Peripheral blood collections were performed on three male participants, older than 18 years, who agreed to participate in the study and signed the free informed and clear consent form (TCLE). Previously, the volunteers had been subjected to an evaluation of their general health status through laboratory tests because alterations in these parameters can interfere with the cell culture and immunological response. On the day of the experiment, blood samples (20 mL) were kept at room temperature and processed within 2 h of collection. PBMCs were separated using centrifugation in with a Ficol column (200×g for 10 min at 4 °C) and resuspended in RPMI 1640 medium with 10% fetal bovine serum (FBS, GIBCO BRL). After the evaluation of the quantity and adequate viability of the cells, monocytes were inoculated and/or treated with BPC and DHA. Monocytes were previously grown in polystyrene bottles using specific media for each cell type (incubation: 37 °C with 5% CO2). Upon reaching an ideal confluence of approximately 70% (~ 106/mL), the cells were infected with S. aureus (1 × 106) and incubated with BCP-DHA (Sigma Aldrich) for 30 min at 37 °C (10–10 × 10–7 M). Negative controls, containing infected cells without treatment or untreated cells, were also analyzed. All experiments were performed in triplicate. After this process and the determined time of evaluation had passed, the supernatant of each culture was collected and frozen at − 70 °C for later cytokine production. For gene expression testing, cell suspensions were transported to microtubes for RNA extraction. Cytokine dosage in the cell culture supernatant was measured using flow cytometry with a Human Th1/Th2/Th17/Th22 13plex – Flow Cytomix commercial kit (eBioscience: Bender Med Systems Gmbh) to quantify GM-CSF, IL-1β, IL-6, IL-10, IL-12, and TNFα, according to the procedures described by the manufacturer’s instructions. FlowCytomix Pro software was used for data analysis, according to the manufacturer’s guidelines. The gene expression of inflammatory markers was evaluated using the PCR array methodology. The mRNA of the macrophage samples was extracted using TRIzol® LS (Life Technologies™) and following the manufacturer’s protocol. cDNA was obtained using retro-transcription (RT) of mRNA with the SuperScript® IV Reverse Transcriptase kit with the addition of complementary oligonucleotides to the poly-A mRNA tail (Oligodt) and an RNase inhibitor. The cDNA obtained was subjected to analysis using the Human Innate & Adaptive Immune Responses PCR Array (Qiagen-AS Bioscience) for the evaluation of 84 genes involved in host response associated with inflammation and bacterial infection. These genes are involved in innate immunity, adaptive, humoral, and inflammatory and defense responses to bacteria and viruses.
A total of 168 mice of the Balb-C lineage, aged between 6 and 8 weeks, from the Multidisciplinary Center for Biological Research in the Animal Science Laboratory of Campinas State University (CEMIB/UNICAMP) were used. As recommended by the American Society for Microbiology, we analyzed males and females (84 animal from each sex). The animals were kept in the IMS/UFBA Vivarium for mice (six animals per cage) under controlled conditions of light (lights on from 7a.m to 7p.m) and temperature (23 ± 3 °C) and had free access to water and food.
Five experimental groups of six mice (BALB/c) each, fasted for 12 h, were used per sex. Before the test (24 h), the following was administered subcutaneously: 0.9% NaCl solution (10 mg/kg), indomethacin (10 mg/kg), BCP (5 mg/kg), BCP + DHA (2.5 mg/kg each), and DHA (5 mg/kg). The next day, the animals received an injection of acetic acid (0.6%; 10 mL/kg, intraperitoneal). The produced abdominal wall writhing, followed by trunk turning and hind limb extension, was counted for 20 min as an indication of nociception.
Five experimental groups of six mice (BALB/c) each, fasted for 12 h, were used per sex. Animals were subcutaneously administered the following 24 h before the test: 0.9% NaCl solution (10 mg/kg), morphine (5 mg/kg), BCP (5 mg/kg), BCP + DHA (2.5 mg/kg each), and DHA (5 mg/kg). The next day, a formalin solution was administered at a 1.5% concentration (formaldehyde, intraplantar, 20 μL) to the right posterior paws of the animals. The number of flinches was quantified over 30 min as an indicator of nociception. The first 5 min determined the response to pain of neurogenic origin, whereas the final 15 min determined the response to pain of inflammatory origin. In this test, edema in the right paw was also measured, and the difference between the weight of the right paw and the weight of the left paw was calculated by weighing the paws.
Von Frey filaments were used to determine mechanical nociception. The animals were placed in acrylic boxes with mesh network floors consisting of non-malleable wire for 30 min before the experiment to adapt to the environment. The test consisted of producing pressure on the paws of the animals with a hand force transducer adapted with a tip (0.8 mm2 tip diameter, Von Frey electronic, Insight Equipment©, Brazil). The stimulus was automatically stopped when the animal presented a response characterized by flinching in response to the stimulated paw and the intensity was recorded. The interval between two consecutive tests on the same paw was at least 1 min, totaling six trials per animal. The maximum force applied was 50 g. Four experimental groups of six mice (BALB/c) each, fasted for 12 h, were used per sex. Animals were subcutaneously administered the following 24 h before the test: 0.9% NaCl solution (10 mg/kg), BCP (5 mg/kg), BCP + DHA (2.5 mg/kg each), and DHA (5 mg/kg). The next day, carrageenan (100 μg/paw, i.pl.) was injected into the ventral surface of the right hind paws of the animals, and after 3 h, the von Frey test was performed. The hypernociception intensity was used to quantify the change in pressure (Δ of reaction in grams) obtained by subtracting the value observed before the experimental procedure (0 h) from the reaction value.
Statistical analysis was performed using the GraphPad Prism software (version 6.0; GraphPad Software, San Diego, CA, USA). The comparisons performed in the different experiments were determined using individual variation or variation of error (s2), the analysis of the Shapiro–Wilk normality test, and the Mann–Whitney test by pairs, because the data did not present a normal distribution. The results are expressed as mean ± standard deviation of the mean (DPM). Statistical differences were considered significant at p < 0.05, using a 95% confidence interval.
All methods in this current study are reported in accordance with ARRIVE guidelines. All experiments with mice also were conducted in accordance with internationally accepted principles for the use and care of laboratory animals, as established in the Brazilian guideline for the care and use of animals in teaching or scientific research activities (DBCA) related to principles of conduct that ensure the care and ethical management of animals used for scientific or teaching purposes, and carried out after approval by the Ethics Committee on the Use of Animals (CEUA) of the Instituto Multidisciplinar em saúde of Universidade Federal da Bahia (project number 60/2018). The study also was developed after approval by the Human Research Ethics Committee of the Federal University of Bahia, Multidisciplinary Institute for Health (CAAE: 79446117.5.0000.5556) and was carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. The strains used were obtained from other studies after approval by the Ethics Committee of Research with Human Beings of the Multidisciplinary Health Institute campus Anísio Teixeira (CAAE nº 08730012.4.0000.5556) and 08731912.5.0000.5556 (nasal strains). For nasal samples, informed consent was obtained from the parents or guardians. |
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PMC9649596 | Yue Wang,Xulong Huang,Bin Xian,Huajuan Jiang,Tao Zhou,Siyu Chen,Feiyan Wen,Jin Pei | Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer 10.3389/fgene.2022.1005896 | 28-10-2022 | bioinformatics,key genes,Paris polyphylla smith,non-small cell lung cancer,machine learning | Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood. Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR. Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis. Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC. | Machine learning and bioinformatics-based insights into the potential targets of saponins in Paris polyphylla smith against non-small cell lung cancer 10.3389/fgene.2022.1005896
Background: Lung cancer has the highest mortality rate among cancers worldwide, and non-small cell lung cancer (NSCLC) is the major lethal factor. Saponins in Paris polyphylla smith exhibit antitumor activity against non-small cell lung cancer, but their targets are not fully understood. Methods: In this study, we used differential gene analysis, lasso regression analysis and support vector machine recursive feature elimination (SVM-RFE) to screen potential key genes for NSCLC by using relevant datasets from the GEO database. The accuracy of the signature genes was verified by using ROC curves and gene expression values. Screening of potential active ingredients for the treatment of NSCLC by molecular docking of the reported active ingredients of saponins in Paris polyphylla Smith with the screened signature genes. The activity of the screened components and their effects on key genes expression were further validated by CCK-8, flow cytometry (apoptosis and cycling) and qPCR. Results: 204 differential genes and two key genes (RHEBL1, RNPC3) stood out in the bioinformatics analysis. Overall survival (OS), First-progression survival (FP) and post-progression survival (PPS) analysis revealed that low expression of RHEBL1 and high expression of RNPC3 indicated good prognosis. In addition, Polyphyllin VI(PPVI) and Protodioscin (Prot) effectively inhibited the proliferation of non-small cell lung cancer cell line with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67μM, respectively. The number of apoptotic cells increased significantly with increasing concentrations of PPVI and Prot. Prot induces G1/G0 phase cell cycle arrest and PPVI induces G2/M phase cell cycle arrest. After PPVI and Prot acted on this cell line for 48 h, the expression of RHEBL1 and RNPC3 was found to be consistent with the results of bioinformatics analysis. Conclusion: This study identified two potential key genes (RHEBL1 and RNPC3) in NSCLC. Additionally, PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Our findings provide a reference for clinical treatment of NSCLC.
Lung cancer is the leading cause of cancer deaths (18.4% of all cancer deaths), with more than 2.1 million lung cancer cases (725,352 in women and 1,368,524 in men) and more than 1.76 million deaths in 2018 (Thai et al., 2021). It has the third highest incidence rate among female cancer patients and the first highest incidence rate among male cancer patients worldwide (Bray et al., 2018). Approximately 85% of lung cancers are non-small cell lung cancer (NSCLC), which mainly includes lung adenocarcinoma (LUAD), lung squamous carcinoma (LUSC), and large cell carcinoma (LCC) (Herbst et al., 2018), but most NSCLC patients are diagnosed with advanced stages of the disease due to the limitations of current screening technologies (Ettinger et al., 2017). Despite the advances in targeted drug therapy and surgery for NSCLC, the desired survival rates are still not achieved (Hirsch et al., 2017). Therefore, it is valuable to combine the large amount of disease-related bioinformatics data available to uncover novel predictors for prognosis and potential therapeutic targets for NSCLC research and treatment (Mangogna et al., 2019). In recent years, the study of disease mechanisms and the exploration of relevant disease-characterizing genes as well as anticancer drug targets with the help of metabolomics (Li, 2015; Hurgobin et al., 2018; Hong et al., 2020) have made good progress, especially the prediction of potential targets (Zhu et al., 2012) and relevant biomarkers (Zheng et al., 2021), which has greatly advanced the field. Compared with time-consuming and expensive traditional acting target studies, bioinformatics analysis combined with machine learning can screen potential acting targets more rapidly and accurately, and it provides exploratory predictions at a lower cost to inform subsequent conducted biological experiments and clinical applications (Sepulveda, 2020; Talukder et al., 2021). In some studies of non-small cell lung cancer (Ni and Sun, 2019; Wang et al., 2020; Li et al., 2021), TTC21 was found to be highly expressed in lung adenocarcinoma by public database analysis, achieving a good prognosis (Wang et al., 2020). Promising therapeutic targets for LUAD were revealed, including genes such as CDK1, CDH1, CDKN3, CDKN2A, CD34, IL6, FOS, MMP9, VWF, EDN1, BIRC5, UBE2C, AURKA, CCBN2, and EGR1 (Ni and Sun, 2019). PIWIL4, IFIT1B, 8IGF2BP1 TLR8, PABPC1, ZC3H12C, PECAM1, ENG, and GAPDH were identified as core genes for the construction of prognostic models (Li et al., 2020; Wang et al., 2021). Although several biomarkers or targets for NSCLC have been identified in recent years. However, the sensitivity and specificity of these biomarkers or therapeutic targets lack further validation due to sample heterogeneity and many confounding factors. Therefore, in-depth experimental validation analysis of novel prognostic predictors and therapeutic targets is needed in the future. Paris polyphylla smith is used by folk medical practitioners in China and India to treat a variety of lung diseases, including consumption, pneumonia, and lung cancer (Liu et al., 2019; Yan et al., 2021). The anticancer activity of the saponins in Paris polyphylla smith has received increasing attention in recent years. Polyphyllin VI (PPVI)has been reported to induce Caspase-1-mediated scorching through induction of the ROS/NF-κB/NLRP3/GSDMD signaling axis in NSCLC (Teng et al., 2020). It has also been found that PPVI induces apoptosis and autophagy in NSCLC through the ROS-triggered mTOR signaling pathway (Teng et al., 2019). In addition, it could induce the accumulation of ROS in cells and down-regulated the Bcl-2 expression, up-regulated the Bax expression and induced the activity of cleaved caspase-3 in cells (You et al., 2021). Moreover, PPVI can be used in a variety of tumor cells such as osteosarcoma, hepatocellular carcinoma, and glioma by regulating ROS/JNK(Yuan et al., 2019), Fas death pathway and mitochondria-dependent pathway (Liu et al., 2018), and JNK/P38 (Liu et al., 2020). Protodioscin also has inhibitory effects on non-small cell lung cancer cell lines (Hu and Yao, 2002), but the anti-non-small cell lung cancer research of protodioscin basis is extremely weak. In this study, we searched for differential genes through gene expression data published in GEO, and explored possible pathways using Disease Ontology (DO), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis after normalization of differential genes; further screened and validated potential signature genes RHEBL1 and RNPC3 in non-small cell lung cancer. The correlation between the expression of signature genes and the prognosis of patients with NSCLC was evaluated using KM-plotter and GEIPA database, and it was found that high expression of RNPC3 and low expression of RHEBL1 significantly prolonged the survival cycle of patients, and the key genes were analyzed for immune infiltration. By molecularly docking the bioactive saponins of paris polyphylla smith with the corresponding proteins of RHEBL1 and RNPC3, Molecular docking screening of compounds Polyphyllin VI and protodioscin with affinity ≤ −7 kcal/mol. And the toxicity, apoptosis and cycle of Polyphyllin VI and protodioscin on A549 cells were verified by CCK-8, flow cytometry. We also verified the effects of Polyphyllin VI and protodioscin on toxicity, apoptosis and cycle of A549 cells by CCK-8, Flow Cytometric Analysis. Also using qPCR, we found that RNPC3 gene expression was significantly upregulated and RHEBL1 gene expression was significantly downregulated in A549 cells after 48 h of Polyphyllin VI and protodioscin action, which was consistent with the results of bioinformatics analysis. This study provides evidence for a new potential clinical strategy for non-small cell lung cancer.
We searched Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/, accessed on 3 January 2022) for eligible datasets meeting the criteria and downloaded the platform files and series matrix files about GSE33532, GSE151103, and GSE44077. Find the correspondence between probe names and gene names based on the information in the platform file, and use Perl scripts to convert the probe matrix into gene expression matrix. The combined data files were subjected to differential analysis by limma software package, and the differential judgment criterion was | log FC(Fold Change)| > 1 with False Discovery Rate< 0.01 (Deng et al., 2021; Ebrahimi Sadrabadi et al., 2021). GSE151103 samples are from 126 patients with stage IA-IV non-small cell lung cancer, including tissue samples from 188 patients with non-small cell lung cancer (133 LUAD samples and 55 LUSC samples), and 172 matched normal tissue samples. GSE33532 included lung cancer tissue samples (80 cases) and matched distant normal lung tissue (20 cases) from 20 patients with stage I-II non-small cell lung cancer (16 men and 4 women, including 11 cases of lung adenocarcinoma, 4 cases of lung squamous cell carcinoma and 5 cases of mixed type). GSE44077 samples were taken from tissue samples from 20 patients with stage I-III a NSCLC (n = 9 women and 11 men) (including 14 lung adenocarcinoma, 5 lung squamous cell carcinoma and 1 unspecified non-small cell lung cancer). Multiple cytologically controlled normal airways and unaffected normal lung tissues were different from the tumor (n = 194 samples). GSE33532 and GSE151103 were used as train sets, GSE44077 as validation set.
GO, KEGG, and DO analyses were used to explore the enrichment of differential genes. Through Gene Ontology, we divided the function of genes into three parts: cellular component (CC), molecular function (MF) and biological process (BP). KEGG enrichment analysis was performed to observe in which pathways the differential genes were enriched, and the corresponding bar and bubble plots were completed using the barplot and ggplot2 software packages. DO analysis of differential genes was performed by clusterProfiler, org. Hs.eg.db, enrichplot, GSEABase, and DOSE packages to explore in which diseases differential genes were significantly enriched. GO, KEGG and DO enrichment analyses were performed with p < 0.05 as cutoff values (Wang et al., 2022).
Least absolute regression and selection operator (Lasso regression) can adjust the variables and complexity when fitting the generalized linear model, and can effectively avoid overfitting. It is suitable for multivariate discrete, continuous or multivariate dependent variables. Lasso regression and support vector machine recursive feature elimination (SVM-RFE) were used to screen the characteristic genes of non-small cell lung cancer, and then R-packet Venn was used to obtain the two overlapping genes to further screen the diagnostic genes of non-small cell lung cancer. R-Packet limma and ggpubr were used to verify whether the feature genes were different in the validation set (GSE44077).
Through receiver operating characteristic curve (ROC curve), we can observe the accuracy of the genes screened by machine learning as the diagnostic genes of non-small cell lung cancer. We used R-packet pROC to draw ROC curves (Huang et al., 2022).
The immunocyte infiltration was analyzed by cibersort software package (https://cibersort.stanford.edu/), and the results were screened under the condition of p < 0.05 (Tang et al., 2022). The results were used to analyze the correlation between immune cells. At the same time, the difference of immune cells and the correlation between target genes and immune cells were analyzed. It is executed by R package e1071, corrlot, barlot, violot, reshape2, ggpubr, and ggExtra.
We used Kaplan-Meier (KM) plotter (http://kmplot.com) to explore the potential prognostic significance of feature genes in non-small cell lung cancer. Survival analysis included Overall survival (OS), first-progression survival (FP) and post-progression survival (PPS). In addition, we used the “Correlation Analysis” module of the GEPIA database (http://gepia.cancer-pku.cn/index.html) to determine the correlation between RHEBL1 and RNPC3 in LUAD and LUSC based on the TCGA-LUAD and TCGA-LUSC data in this database. In this study, we determined the correlation between RHEBL1 and RNPC3 in LUAD and LUSC, respectively, using Spearman’s correlation coefficient (Zhou et al., 2021).
The relevant proteins were downloaded from the PDB Protein Structure Database (www.pdbus.org). RHEBL1 X-ray diffraction (PDB ID: 3OES), RNPC3 solution NMR (PDB ID: 5OBN). Discovery Studio 2016 (Version16.1.0) processed the relevant proteins and the relevant saponin-like components using autodock to dock the relevant small molecule to the protein batch three times (Trott and Olson, 2010), and the docking score was Affinity, with smaller values indicating stronger binding. The following rule of thumb (rule of thumb) is usually used. Affinity ≤ −7 kcal/mol is more strongly bound (Huang et al., 2022).
Protodioscin, Polyphyllin VI were provided by Push bio-technology (Chengdu, China), Cisplatin was provided by RHAWN (Shanghai, China). Dulbecco’s Modified Eagle’s Medium (DMEM) was obtained from Hyclone (Logan, UT, United States). Fetal bovine serum (FBS) was obtained from Every Green (Zhejiang, China), Penicillin Streptomycin Solution was obtained from Hyclone (Logan, UT, United States). TransZol Up Plus RNA Kit was purchased from TransGen Biotech (Beijing, China), PrimeScript RT Reagent Kit was purchased from Takara Bio (Shiga Prefecture, Japan).
A549 was purchased from the ATCC (Manassas, VA, United States). Cells were cultured at 37°C and 5% CO2. DMEM contained penicillin (100 U/ml), streptomycin (100 g/1), and FBS (10%). Cell Counting Kit-8 (CCK-8) assay was used to determine cell viability. Cells were seeded in 96-well plates (100 μl/well). The density of A549 was 5 × 103 cells/well. After treating the cells with different concentrations of Polyphyllin VI (14 μM, 7 μM, 3.5 μM, 1.75 μM, 0.875 μM, and 0.4375 μM), protodioscin (75 μM, 50 μM, 25 μM, 12.5 μM, and 6.25 μM)and cisplatin (2 μM) for 48 h, respectively, the control group was set. Subsequently, the cell viability was measured by CCK-8 assay according to the manufacturer’s instructions.
Apoptosis was detected by the Annexin V-FITC/PI Apoptosis Kit. To collect A549 cells treated with different protodioscin (75 μM, 50 μM, and 25 μM), Polyphyllin VI(14 μM, 7 μM, and 3.5 μM)and Cisplatin (2 μM)for 48 h. A total of 500 μl Binding buffer, 5 μl Annexin V-FITC and 10 μl PI was added in sequence in the dark, after mixing and standing for 15 min, the cell fluorescence was detected by flow cytometry (BD Biosciences, CA, United States). Multi Sciences Cell Cycle Staining Kit was used to determine Cell cycle. A549 cells were treated with protodioscin (75 μM, 50 μM, and 25 μM), Polyphyllin VI (14 μM, 7 μM, and 3.5 μM) and Cisplatin (2 μM) for 48 h. 1 ml DNA Staining solution and 10 μl Permeabilization solution was added for staining. Incubate for 30 min in the dark, and then immediately detect the cell cycle by flow cytometry (BD Biosciences, CA, United States).
Total RNA of A549 was extracted using the TransZol Up Plus RNA Kit according to the instructions, reverse transcription was performed using the PrimeScript TM RT reagent Kit with gDNA Eraser (Perfect Real Time), and the Bio-RAD CFX96TM Real- Time System for quantitative PCR, RHEBL1 primer (forward, 5′-TACC GCTGTGTAGGGAAGACA-3′, reverse, 5′-CCACTGTAGGATCGTAGCCTT-3′), RNPC3 primer (forward, 5′-GTGCGGGTCCTGTCAGATAAG-3′, reverse, 5′-TGAACTCGATCTTGCTCTTTTGC-3′), and GAPDH (forward, 5′-GGAGCGAGA TCCCTCCCCAAAAT-3′, reverse 5′-GGCTGTTGTCATACTTCTCATGG-3′). Real-time PCR was performed in triplicate using samples derived from three independent experiments.
All statistical analyses were performed with R software version 4.1.3 (https://www.r-project.org/) and SPSS 25.0 software (IBM, Armonk, NY, United States) for pre-processing and analysis of public data, including sample data merging, ID transformation and duplicate removal. In addition, Pearson correlations were used to assess correlations between genes and genes, and genes and associated immune infiltrating cells. Differences between the two groups were assessed using t-test (for normally distributed data) or Mann-Whitney test (for non-normal distribution) and p < 0.05 was considered statistically significant. Graphs were constructed using R software and online drawing tools as well as Graphpad (https://www.graphpad.com/).
The workflow of this study is shown in Figure 1. A total of 204 differential genes for non-small cell lung cancer were screened by differential gene analysis, 137 differential genes were down-regulated and 67 differential genes were up-regulated. The data were normalized by limma and sva packages (Figure 2A). The 50 most significantly up-regulated differential genes and the 50 most significantly down-regulated differential genes were plotted as a heat map (Figure 2B). and then three packages, dplyr, ggplot2, and ggrepel, were cited, and the genes with significant differences were labeled with names to visualize the differential genes (Figure 2C).
DO analysis revealed (Figure 3A) that urological diseases, non-small cell lung cancer, and brain diseases topped the list of terms. The results of GO analysis (Figure 3B; Supplementary Figure S1) showed that in BP enrichment analysis, the screened differential genes were significantly enriched in muscle contraction, detection of abiotic stimulus, negative regulation of circadian rhythm; CC enrichment analysis showed significant enrichment in contractile fibers, cation channel complex and voltage-gated calcium channel complex; MF enrichment analysis showed significant enrichment in receptor ligand activity, signaling receptor activator activity and cation channel activity. The results of KEGG enrichment analysis showed (Figures 3C,D) that NSCLC differential genes were significantly enriched in Neuroactive ligand-receptor interactions, CAMP signaling pathways, Cytokine-cytokine receptor interactions were significantly enriched in these pathways, and these pathways accounted for the top three in the KEGG correspondence. Cytokines control immune-related events, and cytokines play a crucial role in the immune regulation of disease and are involved in plethora of physiological and pathophysiological processes, including cancer development and autoimmunity (Spangler et al., 2015; Scheller et al., 2019). The above pathways strongly suggest that non-small cell lung cancer is closely associated with immune cells.
To identify signature genes associated with clinical features of non-small cell lung cancer patients, a total of 42 non-small cell lung cancer signature genes were screened by Lasso regression analysis using the training sets GSE151103 and GSE33532 (Figure 4A; Supplementary Figure S2). Meanwhile, 25 disease signature genes were screened by support vector machine recursive feature elimination (SVM-RFE) (Figure 4B; Supplementary Figure S3). The intersection of the two was taken to screen five non-small cell lung cancer signature genes from the training sets GSE151103 and GSE33532: DOCK6, GPC2, RHEBL1, RNPC3, and PIWIL2 (Figure 4C). The introduction of the validation set GSE44077 to validate the five genes screened (Figure 4D) revealed that the expression levels of RNPC3 (p = 0.00036) in lung cancer tissues of non-small cell lung cancer were significantly higher than those of matched normal controls, and the expression levels of RHEBL1 (p = 6e-0.9) were significantly lower than those of matched controls; while DOCK6 (p = 0.81), PIWIL2 (0.24) and GPC (0.055)were not significantly different between the disease and normal control groups. P< 0.05 was considered a significant difference. The above screening revealed that the expression levels of RNPC3 and RHEBL1 differed significantly in treatment and control group. To further verify the accuracy of these differential genes as diagnostic genes for non-small cell lung cancer, ROC curve analysis of the above these genes was applied and found that RNPC3 (AUC: 0.856, 95% CI: 0.820–0.889), and RHEBL1 (AUC: 0.863, 95% CI: 0.834–0.890) all had AUCs greater than 0.8 (Figures 4E,F). The validation set (GSE44077) was further included in the test ROC, and the results showed that the AUC of RNPC3 (AUC: 0.738, 95% CI: 0.654–0.823) and RHEBL1 (AUC: 0.749, 95% CI: 0.681–0.810) was still greater than 0.7 (Figures 4G,H). It has been found that RHEBL1 is involved in sphingosylphosphorylcholine-induced events in A549 lung cancer cells via binding to AKT1 leading to activation of it (Kim et al., 2017). In addition, anti-RNPC3 antibody can be used as a marker for cancer scleroderma including lung cancer (Shah et al., 2017). This suggests a high probability of diagnosing non-small cell lung cancer with RNPC3 and RHEBL1.
The above analysis showed that the expression of the feature gene RNPC3 was significantly higher in the treatment group and RHEBL1 was significantly lower in the treatment group compared to the control group. Further analysis of the effect of the expression status of the signature gene on the prognosis of non-small cell patients by Kaplan-Meier (KM) plotter, compared with the high expression group, the low expression group of RHEBL1 in non-small cell lung cancer patients (including LUAD and LUSC) showed significantly favorable in OS, FP. Unfortunately, the high and low RHEBL1 gene in PPS expression did not differ significantly in terms of prognosis (Figure 5A). In contrast, low expression of RNPC3 was significantly associated with poor prognosis in OS, FP and PPS (Figure 5B). Further breakdown by gender revealed that high expression of RNPC3 in female and male related patients (LUAD, LUSC) was significantly associated with a good prognosis in related patients. low expression of RHEBL1 was significantly associated with favorable survival in both male and female NSCLC patients (Figure 5C). Correlation analysis by the GEPIA database revealed a significant negative correlation between RHEBL1 and RNPC3 in LUAD and LUSC, which is consistent with the above results (Figures 5D,E). In conclusion, low expression of RHEBL1 or high expression of RNPC3 was significantly associated with good prognosis in patients with non-small cell lung cancer.
Increasing results suggest that tumor-infiltrating immune cells are potential predictors for patients with non-small cell lung cancer (Koyama et al., 2016; He et al., 2017; Chiou et al., 2021). KEGG functional enrichment showed that non-small cell lung cancer was enriched in immune-related pathways. Therefore, the correlation between 22 immune cells was further analyzed to explore their relationship and possible interactions. The highest positive correlation was found between NK cells activated and Mast cells resting (Pearson’s correlation = 0.44), followed by the correlation between Monocytes and Neutrophils (Pearson’s correlation = 0.42). In terms of negative correlation, T cells CD8 was significantly negatively correlated with T cells CD4 memory Resting (Pearson’s correlation = −0.45) (Figure 6A). To explore the immune differences between the non-small cell lung cancer group and matched normal controls, we evaluated the expression of GSE151103, GSE33532 datasets with 22 infiltrating immune cells such as B cells naive, B cells memory, Plasma cells, T cells CD8, T cells CD4 naïve relationship (Figure 6B), and further differential analysis of immune cells, immune infiltration results showed that lung tissues of non-small cell lung cancer patients with Plasma cells (p < 0.001), T cells CD4 memory activated (p = 0.011), T cells regulatory (Tregs) (p = 0.020), Macrophages M0 (p = 0.022), Dendritic cells activated (p = 0.010) were significantly upregulated, T cells follicular helper (p = 0.006), Monocytes (p = 0.005), Mast cells resting (p < 0.001) were significantly downregulated (Figure 6C); finally, the correlation analysis between RNPC3, RHEBL1 and immune cells was explored (Figures 6D,E; Supplementary Figure S4), and RHEBL1 was significantly downregulated with Macrophages M0, T cells regulatory (Tregs), Dendritic cells activated, and Plasma cells were significantly positively correlated with immune cells such as Neutrophils, T cells follicular helper, and Mast cells resting; RNPC3 was significantly and negatively correlated with Mast cells resting, RNPC3 was significantly and positively correlated with immune cells such as Macrophages M2, NK cells activated, T cells CD8, Monocytes, and with Macrophages M0, Plasma cells, T cells regulatory (Tregs), Dendritic cells activated, T cells CD4 memory activated, and other immune cells. In conclusion, the expression of RHEBL1 and RNPC3 significantly correlated with immune cells infiltrated by tumors in patients with non-small cell lung cancer.
The saponins with good biological activity in paris polyphylla smith were selected for this molecular docking (Supplementary Table S1), and the saponins were docked with RNPC3 (PDB ID: 5OBN) and RHEBL1 (PDB ID: 3oes) using Vina software for three times. The docking results of compounds with docking scores in the range of −7 kcal/mol–−10 kcal/mol with both RNPC3 and RHEBL1 were plotted. The molecular docking results showed that the average scores of RHEBL1 with protodioscin, Polyphyllin VI, and Polyphyllin V docked three times were −8.4, −7.2, and −7.2, respectively (Figure 7A), the average scores of RNPC3 docked three times with protodioscin, Polyphyllin VI, and Pennogenin were −9.3, −7.6, and −7.5 (Figure 7B). From the docking results, it can be seen that Polyphyllin VI: LEU123, THR88, SER89, TYR14, ARG15, and CYS16 are key amino acid residues in the active site of RHEBL1 (Figure 7C). Protodioscin: SER89, THR88, SER86, CYS16, ARG15, and TYR14 are the key amino acid residues of the active site of RHEBL1 (Figure 7D). Polyphyllin VI: ARG505, ALA504 PRO418, GLU476, and LYS477 are key amino acid residues in RNPC3 (Figure 7E). Protodioscin: ARG502, ALA501, ASN419, PRO418, ASP509, and LYS477 are the key amino acid residues of the RNPC3 active site (Figure 7F).
We evaluated the inhibitory effects of Polyphyllin VI and Protodioscin on A549 cell line using CCK-8 assay and set cisplatin as positive control group and tested 48 h after administration. As shown in Table 1, Polyphyllin VI and Protodioscin effectively inhibited the proliferation of non-small cell lung cancer cell line A549 with IC50 of 4.46 μM ± 0.69 μM and 8.09 μM ± 0.67 μM, respectively. Further, Annexin V-FITC/PI staining and flow cytometry were used to detect apoptosis. The results showed that the number of normal cells (LL quadrant) gradually decreased and the number of apoptotic cells (UR quadrant, LR quadrant) gradually increased with increasing concentrations of Polyphyllin VI (3.5 μM, 7 μM, and 14 μM). 14 μM Polyphyllin VI and 2 μM Cisplatin treated groups increased from 6.57% ± 1.24% to 40.26% ± 2.44% and 15.14% ± 1.66% of cells, respectively (**p < 0.01, Figures 8A,C). The number of apoptotic cells increased significantly with increasing concentrations of Protodioscin (25 μM, 50 μM, 75 μM), where the percentage of cells in the 75 μM Protodioscin and positive group cisplatin (2 μM) increased from 6.57% ± 1.24% to 32.89% ± 2.39% and 15.14% ± 1.66% (**p < 0.01, Figures 8B,D). These data showed that Polyphyllin VI and Protodioscin induced apoptosis of A549 cells.
The cell cycle was detected by BD flow cytometry after treating A549 cells for 48 h with Polyphyllin VI, protodioscin and cisplatin, and the results showed that 25 μM, 50 μM, and 75 μM protodioscin stalled the A549 cell cycle in G1/G0 phase, with the 75 μM protodioscin group increasing the ratio from 48. 13% ± 1.04% to 72.69% ± 1.71% (*p < 0.05, **p < 0.01), and the 2 μM cisplatin group also significantly induced A549 cell cycle arrest in the G1/G0 phase (Figures 9A,C). Interestingly, in A549 cells, the percentage of Polyphyllin VI in G2/M phase exhibited a dose-dependent increase accompanied by increasing Polyphyllin VI concentrations (3.5 μM, 7 μM, and 14 μM). 14μM Polyphyllin VI, 2 μM cisplatin-treated A549 cells exhibited a dose-dependent increase, with a significant increase in the percentage of cells in G2/M phase compared to control (*p < 0.05, **p < 0.01, Figures 9B,D). The above results suggest that protodioscin dose-dependently induced A549 cell cycle arrest in the G1/G0 phase, whereas Polyphyllin VI blocked the A549 cell cycle in the G2/M phase.
We identified the characteristic genes RHEBL1 and RNPC3 in non-small cell lung cancer by characteristic differential gene analysis, and immuno-infiltration analysis revealed that simultaneous low expression of RHEBL1 or high expression of RNPC3 significantly prolonged the survival time of related patients. Therefore, we further validated our findings by quantitative PCR, which showed that after 48 h of Polyphyllin VI and Protodioscin action on A549 cells, Protodioscin and Polyphyllin VI significantly promoted the low expression of RHEBL1 compared to the control group (*p < 0.05, * *p < 0.01, Figure 9E), while for RNPC3, the relative mRNA expression of Protodioscin and Polyphyllin VI was significantly higher after acting on A549 (*p < 0.05, **p < 0.01, Figure 9F). This is consistent with the results analyzed in GEO, KM-plotter and GEPIA databases. Overall, Protodioscin and Polyphyllin VI significantly promoted either low expression of RHEBL1 or high expression of RNPC3, causing patients with non-small cell lung cancer to exhibit a good prognosis.
Among malignancies, lung cancer has the highest incidence and is the leading cause of cancer death in men and women worldwide. Despite advances in early diagnosis of lung cancer and in targeted therapies, lung cancer is mostly diagnosed at advanced stages and patient survival is low. Therefore, it is important to identify effective prognostic signature genes for lung cancer. In this study, we screened 204 differential genes for non-small cell lung cancer by bioinformatics analysis of the non-small cell lung cancer-related datasets GSE33532, GSE151103, and GSE44077 in the GEO public database (Figure 2). Differential genes were found to be associated with lung diseases, including non-small cell lung cancer, by DO analysis. By GO enrichment analysis and KEGG enrichment analysis, these differential genes were found to be closely associated with immune cells. By GO enrichment analysis and KEGG enrichment analysis, the screened differential genes were found to be closely associated with immune cells (Figure 3). The differential genes were further screened by Lasso analysis and support vector machine recursive feature elimination (SVM-RFE) analysis of the training sets GSE33532 and GSE151103, and verified by ROC curve analysis, which revealed that the expression levels of two genes, RNPC3 and RHEBL1, were significantly different in non-small cell lung cancer tissues and controls (Figure 4). Further testing revealed that low expression of RHEBL1 and high expression of RNPC3 in non-small cell lung cancer significantly improved the prognosis and prolonged life expectancy of cancer patients (Figure 5). KEGG enrichment analysis implied that these differential genes were associated with immune cells, so 22 immune infiltrating cells were introduced to explore their relationship with RHEBL1 and RNPC3, and the results showed that both were significantly associated with immune cells infiltrating tumors in patients with non-small cell lung cancer (Figure 6). The results of bioinformatics analysis, molecular docking, flow cytometry and quantitative PCR suggest that RHEBL1 and RNPC3 may be potential targets for Polyphyllin VI and Protodioscin in the treatment of non-small cell lung cancer. Polyphyllin VI and Protodioscin with affinity ≤ −7 kcal/mol were selected by molecular docking of the two characterized genes screened with the active ingredients in paris polyphylla smith and found by CCK-8 assay that Polyphyllin VI and Protodioscin both inhibited the A549 cell line (Polyphyllin VI: IC50 = 4.46 ± 0.69; Protodioscin: IC50 = 8.09 ± 0.67). Polyphyllin VI have been found to be effective in the treatment of non-small cell lung cancer through the p53 pathway and death receptor pathway (Lin et al., 2015), the ROS/NF-κB/NLRP3/GSDMD signaling axis, and the mTOR signaling pathway (Teng et al., 2019). We detected apoptosis in A549 cells by flow cytometry and found a significant increase in the proportion of apoptotic cells accompanied by increasing concentrations of both compounds; in flow cycle assays, we found that Protodioscin dose-dependently induced A549 cell cycle arrest in G1/G0 phase, while Polyphyllin VI blocked A549 cell cycle arrest in G2/M phase. Further analysis revealed that both substances inhibited the proliferation of the A549 cell line and significantly down-regulated the mRNA expression of RHEBL1 and up-regulated the mRNA expression of RNPC3, which was consistent with the bioinformatic predictions. These results confirm that RHEBL1 and RNPC3 may be potential targets for Polyphyllin VI and Protodioscin in the treatment of non-small cell lung cancer and may also be candidate diagnostic genes for determining the prognosis associated with non-small cell lung cancer. The involvement of RhebL1 in sphingosylphosphorylcholine-induced events including (K8) phosphorylation, reorganization, migration and invasion was examined (Kim et al., 2017). In breast cancer cells MCF7, scholars found that overexpression of RhebL1 increased the expression of mesenchymal markers and decreased the expression of E-cadherin in MCF7 and that downregulation of this gene significantly reduced the migration and invasion of MCF7 cells (Kim and Lee, 2018). Several studies also found that high expression of exogenous RHEBL1 promoted the growth of malignant mesothelioma cells and probed that RHEB-mTORC1 signaling has a pro-carcinogenic effect (Bonneau and Parmar, 2012; Sato et al., 2021). In terms of RNPC3, little is known about the role of RNPC3 protein in human cancers, and some studies have shown that genes including RNPC3 can be used as diagnostic genes for lung adenocarcinoma (Li et al., 2020). In addition, RNPC3 rearrangements have been associated with the development of B-type acute lymphoblastic leukemia (Chen et al., 2020). Several limitations remain in this study, although we investigated the correlation between patients with non-small cell lung cancer and immune infiltration, the immune infiltration results were not further validated. Secondly, Polyphyllin VI and Protodioscin promoted apoptosis and cell cycle arrest in A549 cells, which may be related to the expression profile of the characteristic genes RHEBL1 and RNPC3, however, the molecular mechanisms and roles involved need to be further investigated. Finally, the analysis of RHEBL1 and RNPC3 as potential targets and prognostic genes in this study was mainly based on the mRNA level, and no further in-depth analysis was done at the protein level to make the data more convincing. In conclusion, our findings strongly suggest that RHEBL1 and RNPC3 are potential targets for Polyphyllin VI and Protodioscin in the treatment of non-small cell lung cancer, and may also be candidate diagnostic genes for non-small cell lung cancer-related prognosis.
In conclusion, we explored two potential key genes (RHEBL1 and RNPC3) for NSCLC by machine learning and bioinformatics approaches. We further validated by molecular docking and in vitro experiments that PPVI and Prot may act on RHEBL1 and RNPC3 to affect NSCLC. Additionally, this study provides new insights into the clinical treatment and molecular mechanisms of NSCLC, revealing potential therapeutic targets. |
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PMC9649600 | 36181361 | Jincheng Wang,Yuchen Sang,Shengxian Jin,Xuezheng Wang,Gajendra Kumar Azad,Mark A. McCormick,Brian K. Kennedy,Qing Li,Jianbin Wang,Xiannian Zhang,Yi Zhang,Yanyi Huang | Single‐cell RNA ‐seq reveals early heterogeneity during aging in yeast | 01-10-2022 | early heterogeneity,iron transport,mitochondrial dysfunction,single cell RNA sequencing,yeast aging | Abstract The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single‐cell resolution through transcriptomic sequencing. We optimized a single‐cell RNA sequencing (scRNA‐seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell‐to‐cell transcriptional variability during aging. The single‐cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation–reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level. | Single‐cell RNA ‐seq reveals early heterogeneity during aging in yeast
The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single‐cell resolution through transcriptomic sequencing. We optimized a single‐cell RNA sequencing (scRNA‐seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell‐to‐cell transcriptional variability during aging. The single‐cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation–reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.
Abbreviations BP biological processes CV coefficients of variation CV2 squared coefficient of variation ERCC external RNA control consortium FIT3 facilitator of iron transport GO Gene Ontology HVGs highly variable genes log2FC log2FoldChange OSR oxidative stress response PCA principal component analysis RLS replicative life span scRNA‐seq single‐cell RNA sequencing TF transcription factor TPM Transcripts Per Kilobase of exon model per Million mapped reads UPR unfolded protein response WT wild type
It has been known for a long time that budding yeast S. cerevisiae has limited division potential, only producing a finite number of daughter cells before death (Mortimer & Johnston, 1959). This phenomenon is defined as replicative aging, and the number of daughter cells produced before death is defined as the replicative lifespan (RLS) (Kaeberlein et al., 2007). Owing to its relatively short lifespan, detailed knowledge of its biology and its easy genetic manipulation, S. cerevisiae is regarded as an ideal model organism to study aging (Denoth Lippuner et al., 2014). Indeed, many aging genes and signaling pathways initially found in yeast have also been later found to be conserved in other organisms, such as C. elegans, M. musculus, and even humans (McCormick et al., 2015). A dilemma of replicative aging research in yeast exists between the rarity of old cells among an exponentially growing population either on a solid agar plate or in liquid media and the large number of pure old cells conventionally required for biochemical, genomic, or transcriptomic analysis. To address this problem, several approaches have been developed to enrich old yeast cells, including magnetic sorting, elutriation, genetic programming, and even computation (Hendrickson et al., 2018; Hu et al., 2014; Leupold et al., 2019; Lindstrom & Gottschling, 2009; Smeal et al., 1996). However, these methods have yet to be successful at simultaneously ensuring both the quantity and purity of the isolated old yeast cells much less distinguishing old but living cells from dead ones. In addition, conventional bulk population analysis of aging yeast cells may likely obscure some specific features within sub‐populations due to the average effect (Zhang et al., 2018). Therefore, a systematic and quantitative investigation of yeast aging at the single‐cell and transcriptome level would be highly valuable. Here, we developed a single‐cell RNA‐seq approach to study the replicative aging of yeast and quantitatively assessed the heterogeneity between single yeast cells. Instead of partially purifying millions of old cells, exploiting single‐cell technologies enabled us to obtain novel insights into yeast aging from hundreds of single cells with precise ages. By profiling the transcriptomic landscapes of single yeast cells, we observed an increased cell‐to‐cell transcriptional variability and identified key functional biological processes or cellular components that were highly enriched during aging. We also found early heterogeneity during aging indicated by some iron transporter genes, and successfully characterized the distinctive temporal regulation of transcription between slow‐dividing and fast‐dividing age subgroups.
Single yeast cells from isogenic populations ultimately have different lifespans. In fact, this is a universal phenomenon of aging across species, albeit in different forms and ranges. And previous single‐cell imaging data of replicative aging in yeast have provided evidence of such heterogeneity. For example, when re‐analyzing the single cell imaging data from a previous microfluidic‐based yeast aging study (Video S1, Zhang et al., 2012), we can observe that as early as 8 h after birth, the distribution of generations of single yeast cells had already become dispersed, and the ranges of the distribution gradually increased at 12 and 16 h after birth (Figure S1a), showing that some cells always divided more rapidly than others ever since early in life. These early‐stage cell division dynamics in yeast seems closely associated with replicative age, with a positive correlation between the generations at early time points (8, 12, 16 h) after birth and the RLS at the single‐cell level (R = 0.46, 0.64, 0.73; p = 9.6 × 10−5, 7.7 × 10−9, 7.7 × 10−9; Figure S1b). This new finding is consistent with the previous report that the division time of single yeast cells early in life is negatively correlated with RLS, and the division time increases dramatically when approaching the end of life (Zhang et al., 2012). It was also reported previously that early in life, the gene expression level of HSP104, which encodes a molecular chaperone that maintains proteostasis in yeast, negatively correlates with RLS (Xie et al., 2012; Zhang et al., 2012). Accordingly, after re‐analyzing the single cell imaging data (Zhang et al., 2012), we observed a negative correlation between the generations at early time points during aging and the HSP104 gene expression level indicated by a GFP tag fused to this gene in single yeast cells (R = −0.43, −0.51, −0.56; p = 2.8 × 10−4, 8.4 × 10−6, 7.8 × 10−7; Figure S1c). Collectively, these single‐cell imaging data indicate an early heterogeneity of cell divisions during aging in yeast, and that the division dynamics early in life can predict lifespan. To probe more deeply into the mechanisms underlying this early heterogeneity revealed by single‐cell imaging, we further developed and applied scRNA‐seq for transcriptome profiling of yeast aging (Figure 1a; see Section 4). We first conducted a RLS assay by continually performed manual microdissection of single yeast cells on a solid agar plate (Steffen et al., 2009), resulting in an median lifespan of 23.0 (Figure S1d). In the meantime, we manually isolated single aging yeast cells from the plate at three different time points (2, 16, and 36 h after birth). We placed the cells individually and immediately into a single tube prefilled with lysis buffer containing an external RNA control consortium (ERCC) spike‐in for assessing technical noise. Then a refined Smart‐seq2‐based protocol (Picelli et al., 2014) was performed for yeast aging research (see Section 4).
In total, we collected 136 single yeast cells with precise age for sequencing. The time points of isolation and number of generations at that time were precisely recorded for each cell (Table S1). After filtering out the cells with a low number of genes detected, insufficient read counts and ERCC‐dominated samples, we finally retained scRNA‐seq data of 125 cells composed of 37, 43, and 45 single cells in the 2‐h (young), 16‐h (early age), and 36‐h (late age) age groups, respectively, for further analysis (Figure S2a–c; see Section 4). Our method yielded, on average 2202 genes detected per cell, which accounts for about one third of the coding genes in budding yeast S. cerevisiae (Table S1). According to the analysis of ERCC spike‐in molecules, we realized that the dynamic range spanned five orders of magnitude, and the detection rate was more than 90% for transcripts with an absolute copy number above 10 (Figure 1b,c). We compared our scRNA‐seq data to the bulk RNA‐seq data from cultures grown in similar condition, and our scRNA‐seq quantification can reproduce bulk RNA‐seq data with a correlation of 0.75 and P value <2.2 × 10−16 (Figure 1d). We also compared our scRNA‐seq with the scRNA‐seq datasets of S. cerevisiae growing in different conditions using different methods published recently (Gasch et al., 2017; Jariani et al., 2020; Nadal‐Ribelles et al., 2019). Overall, we found a good genome‐wide correlation between our scRNA‐seq dataset and three existing scRNA‐seq datasets of S. cerevisiae, respectively (Figure S2d). Our scRNA‐seq data has similar gene coverage compared with that from Gasch et al. (2017) using Fluidigm C1 system and Nadal‐Ribelles et al. (2019) using UMI strategy (Figure 1e). The dataset from Jariani et al. (2020) was generated using droplet‐based 10× Genomics Chromium system. It has a lower sensitivity compared with our dataset, but with the highest throughput, detecting a median of 1269 gene transcripts per cell from more than 6000 single cells (Figure 1e).
We sought to explore the cell‐to‐cell transcriptional variability within different age groups using scRNA‐seq data. Overall, we observed increased cell‐to‐cell transcriptional variability during aging in yeast based on a correlation analysis in which the transcriptional variability was measured as the biological noise over the technical noise (Enge et al., 2017) (Figure 2a; see Section 4). We verified this increase in cell‐to‐cell transcriptional variability alternatively using a quantitative statistical method (Brennecke et al., 2013) and, respectively, identified 145, 312 and 524 highly variable genes (HVGs) with coefficients of variation (CV) that were significantly higher than those of the ERCC spike‐in reference within each age group (Figure S3a; Table S2; see Section 4). The HVGs were not lowly expressed, therefore it's not likely to be a technical result (Figure S3b; Table S2). Interestingly, by Gene Ontology (GO) analysis of these HVGs using DAVID (Dennis G Jr et al., 2003), the biological processes of cellular iron ion homeostasis and siderophore transport were specifically found to be highly enriched in the 16‐h early age group with high statistical significance, implying an early heterogeneity during aging in yeast with regard to iron transport (Table S2). Because all of the aging single yeast cells analyzed did not have synchronized cell cycles, we wondered whether and to what extent the cell‐to‐cell transcriptional variability was associated with the cell cycle. We found that 19.3%, 12.8%, and 15.5% of HVGs, respectively, among the 3 age groups were regarded as cell‐cycle‐regulated periodic genes (Granovskaia et al., 2010; Figure S3c). These results are consistent with a recent report of scRNA‐seq in budding yeast that cell‐cycle‐regulated periodic genes were enriched in HVGs (Nadal‐Ribelles et al., 2019). However, the trend of increased cell‐to‐cell transcriptional variability during aging remained even when these cell‐cycle‐regulated periodic HVGs were removed from the 3 age groups (117, 272, and 443 HVGs remained, respectively; Figure S3c). And the remaining HVGs were also highly enriched in the same biological processes such as transport, cellular iron ion homeostasis and siderophore transport (Table S2). We further confirmed this trend by principal component analysis (PCA) and pseudotime analysis. Regardless of whether the cell‐cycle‐regulated periodic genes were included in the scRNA‐seq dataset used as input, the 3 age groups were always successfully separated along the axis of first PCA component and were increasingly dispersed (Figure 2b; Figure S3d); moreover, the top 30 genes based on the absolute loading values for the first PCA component always highly overlapped and were enriched in the biological process of cellular response to oxidative stress, which reflects aging itself rather than the cell cycle (Figure S3e; Table S3); finally, the pseudotime analysis using Monocle (Trapnell et al., 2014) revealed that while the young cells (2‐h) were still very concentrated, the cells of the early age group (16‐h) had already become scattered along the trajectory (Figure 2c; Figure S3f). The expression noise of a gene in the isogenic cell population is composed of intrinsic and extrinsic factors (Elowitz et al., 2002). A previous study of noise in gene expression coupled to different growth rates also has shown the regimes of expression dominated by either intrinsic factors (low expression) or extrinsic factors (high expression; Keren et al., 2015). To examine the sources of the global transcriptional changes in noise during aging, we plotted the mean and CV2 (squared coefficient of variation) of gene expression with linear fits of the data across different age groups. We found that there was a significant increase in noise during aging, either with or without the cell‐cycle‐regulated periodic genes expression included as input (Figure 2d). Interestingly, this increase in noise mainly occurred at higher gene expression (TPM > 100), suggesting that it's contributed by extrinsic factors.
The scRNA‐seq data also allow us to globally investigate the differential gene expression between age groups. Thus, we conducted a pairwise comparison among the 3 age groups using DESeq2 (Love et al., 2014; Figure S4a; see Section 4). Obviously, more differentially expressed genes were found between the 36‐h late age group and the 2‐h group (Figure S4a, right panel; Table S4). The biological processes of oxidation–reduction and the oxidative stress response (OSR) were highly enriched in the 36‐h group (75 and 26 out of 551 genes, respectively), while translation and ribosome biogenesis were highly enriched in the 2‐h group (50 and 38 out of 138 genes, respectively) based on the GO analysis of biological process using DAVID (Dennis et al., 2003; Figure 2e, right panel). Moreover, 145 out of 551 genes that were highly expressed in the 36‐h late age group compared with the 2‐h group were enriched in mitochondrion as revealed by the GO analysis of cellular components (Figure 2e, left panel; Table S4). The average normalized gene expression levels across age groups further demonstrated an age‐dependent increase in oxidation–reduction, OSR and mitochondrion as well as a decrease in translation and ribosome biogenesis (Figure 2f). Indeed, these transcriptome changes had already occurred in the 16‐h early age group. Although far fewer differentially expressed genes were found in the 16‐h early age group compared with the 2‐h group (Figure S4a, left panel), early signs of upregulation in oxidation–reduction and downregulation in ribosome biogenesis (15 out of 108 genes and 4 out of 10 genes, respectively) were observed (Figure S4b; Table S4). Notably, the global differentially expressed genes between age groups and their enriched GO categories from our scRNA‐seq data were found to coincide well with a recent report of transcriptome changes during aging in yeast (Hendrickson et al., 2018), and were even partially consistent with another proteome analysis of aging in C. elegans (Walther et al., 2015), although they were both based on bulk population analysis. These aging associated GO categories analyzed by DAVID were also confirmed by ClusterProfiler (Yu et al., 2012; Figure S5a‐f).
The number of genes detected per cell within age groups was found to be positively correlated with the generation, suggesting another facet to understand the heterogeneity of cell divisions during aging in yeast, and the 16‐ and 36‐h age groups were thus split by their respective mean generation into slow‐dividing (16‐h/S, 36‐h/S) and fast‐dividing (16‐h/F, 36‐h/F) age subgroups (Figure 3a,b; Table S1). Comparing the early age subgroups of 16‐h/S and 16‐h/F by DESeq2(Love et al., 2014) with stringent statistical filtering yielded 29 differentially expressed genes, with five highly expressed and 24 weakly expressed in 16‐h/S (Figure 3c; Table S5). FIT3 and HAC1 were highly expressed in 16‐h/S. FIT3, together with FIT2 and FIT1, as facilitators of iron transport in yeast, encodes a cell wall mannoprotein (Protchenko et al., 2001). These genes were reported to be induced upon iron deprivation or mitochondrial DNA loss (Veatch et al., 2009). HAC1 is a transcription factor that regulates the unfolded protein response (UPR), and interestingly, one of its regulatory targets is FIT3 (Cox & Walter, 1996; Hu et al., 2007). Indeed, FIT3 and HAC1 were not only highly expressed in 16‐h/S but also in 36‐h/S (Figure 3d,e). Moreover, the gene expression of FIT3 and HAC1 negatively correlated with the generation of single cells in the 16‐h age group (R = −0.55, −0.38; p = 1.3 × 10−4, 1.5 × 10−2) as well as the 36‐h age group (R = −0.62, −0.44; p = 5.6 × 10−6, 2.2 × 10−3; Figure 3f; Figure S6a; Table S5). Gene expression levels of several other iron transporters, including FIT2 and FET3 (Protchenko et al., 2001), were also found to be negatively correlated with the generation of single cells in the 16‐ and 36‐h age groups (Figure S6b,c; Table S5). Finally, as single‐gene deletions of FIT2 and FET3 were both reported to extend the lifespan in yeast (McCormick et al., 2015), we measured the RLS of yeast after deleting FIT3, and verified that this strain is long‐lived as well (Figure 3g). Collectively, these results reveal a molecular marker of iron transport that can indicate early heterogeneity during aging in yeast and quantitatively predict the lifespan. Interestingly, we also revealed that 11 out of 24 genes expressed at lower levels in 16‐h/S than in 16‐h/F were enriched in mitochondrion, and these genes were also expressed at lower levels in 36‐h/S than in 36‐h/F (Figure 3c–e; Table S5). This further suggests a relatively poor mitochondrial function in the slow‐dividing cells. Among these 11 weakly expressed mitochondrial genes (Figure 3c), COR1 is the core subunit of ubiquinol‐cytochrome c reductase which belongs to complexes III and COX4 is an important component of cytochrome c oxidase which belongs to complexes IV of the mitochondrial inner membrane electron transport chain. It has been reported that mutation of either COR1 or COX4 can cause a decrease in respiration, slow cell growth and even shorter lifespan (Allan et al., 2013; Herrmann & Funes, 2005; Marek & Korona, 2013). These 11 mitochondrial genes showed no overlap with the 145 mitochondrial genes that were globally upregulated during aging in yeast (Figures 2e and 3c, Tables S4 and S5); in contrast, no significant differential expression of those 145 mitochondrial genes was observed between the age subgroups (Figure 3e). These results successfully characterize divergent mitochondrial gene expression profiles between age groups and subgroups that would be masked in the bulk population analysis but can be identified by scRNA‐seq. We performed the correlation analysis between the gene expression and the generation of single cells in the 16‐h early age group. And ribosome biogenesis was found to be enriched (Figure S6d; Table S5). This suggests a downregulation of at least some ribosome biogenesis genes during early aging and it was mainly contributed by the cells from the slow‐dividing age subgroup, which were inclined to be short‐lived (Figure S6e). Meanwhile, genes enriched in translation, mitochondrial translation and glycolytic processes were positively correlated with generation in the 36‐h late age group (Figure S6f). This agrees with the differential gene expression analysis above, suggesting a relatively poor machinery of translation and mitochondrion in the slow‐dividing age subgroups. In summary, these results characterized early and late heterogeneity during aging in yeast at the single‐cell transcriptome level.
We further investigated the regulatory variation in transcription factors (TFs) between age subgroups, analyzing 634 overlapping TF targets (gene clusters) based on TF binding data of budding yeast (Gasch et al., 2017). To eliminate false‐positives, we performed stringent statistical analysis with three approaches (see Section 4). First, we conventionally compared the median TF targets expression between age subgroups. This led to 16 TF targets that were significantly activated in the 16‐h/F subgroups and 11 TF targets in 36‐h/F subgroups compared with their counterparts, respectively (Figure S7a,b; Table S6). Then, we ran a Wilcoxon rank sum test comparing normalized gene expression levels of each set of TF targets to that of all other detected genes for each cell, taking P < 0.0001 as the criterion, followed by intersection with TF targets derived from the conventional analysis. This led to 5 and 2 TF targets that were significantly activated in 16‐ and 36‐h/F, respectively (Figure 4a; Figure S7c; Table S6). Subsequently, we employed correlation analysis between TF target expression and the generation of single cells in the 16‐ and 36‐h age groups, taking p < 0.05 as the criterion (Figure S8a,b; Table S6), followed by intersection with TF targets derived from the former two approaches. Finally, YAP1 was found to be most significantly active in regulating the early age subgroup of 16‐h/F compared with 16‐h/S (Figure 4b,c), although the other 4 TFs of ABF1, REB1, INO4, and TYE7 demonstrated a similar trend with less statistical significance (Figure S7d,e). Moreover, two TF targets of RPN4 were found to be most highly regulated in the late age subgroup of 36‐h/F compared with 36‐h/S (Figure 4b,c). YAP1 is involved in activating the transcription of antioxidant genes in response to oxidative stress (Temple et al., 2005; Toone & Jones, 1999). The highly activated YAP1 targets (52 genes; Table S6) in 16‐h/F compared with 16‐h/S suggests that the rapidly dividing single cells, which inclined to be long‐lived, may have a better defense system against oxidative stress than the slow‐dividing cells during early age. RPN4 is a TF that stimulates proteasome biogenesis for the degradation of damaged proteins (Xie & Varshavsky, 2001). The other two highly activated RPN4 targets (67 and 191 genes, respectively; Table S6) in the 36‐h/F late age but rapidly dividing subgroup supports the idea that proteasome capacity is critical to maintain the vigor and proteostasis of yeast cells, especially when approaching the end of life, as elevated RPN4 expression is essential for extending the RLS in yeast (Undine et al., 2011). To verify the temporal regulatory variation of YAP1 and RPN4 between age subgroups, we employed another dataset of TF targets with simultaneous DNA binding and expression evidence (Monteiro et al., 2020), which may further imply TF function. Again, based on this dataset, YAP1 targets (505 genes; Table S6) were highly expressed in 16‐h/F compared with 16‐h/S early age subgroup and RPN4 targets (131 genes; Table S6) were highly expressed in 36‐h/F compared with 36‐h/S late age subgroup, both with statistical significance (Figure S9a,b). Altogether, these findings reveal early and late heterogeneity by distinctive temporal regulation of TFs during aging in yeast and combined with the aforementioned differential gene expression analysis between age groups and subgroups, we successfully depicted a landscape of aging in yeast with unprecedented detail at single‐cell resolution.
Although transcriptome changes during aging in yeast based on bulk population analysis have been reported (Hendrickson et al., 2018; Hu et al., 2014; Leupold et al., 2019; Lindstrom & Gottschling, 2009; Smeal et al., 1996), such analysis at the single‐cell level had not yet been performed. Here, we first identified an early heterogeneity of cell divisions during aging in yeast by single‐cell imaging. Then, we developed and applied scRNA‐seq for single‐cell transcriptome analysis during aging in yeast for the first time. Using scRNA‐seq technology, we overcame the difficulty of purifying the large number of old cells required for conventional transcriptome analysis during aging in yeast. Our results have unveiled an increased cell‐to‐cell transcriptional variability independent of cell cycle and identified an early heterogeneity during aging in yeast. This also coincides with recent reports of scRNA‐seq in mouse immune cells and human pancreatic cells during aging (Enge et al., 2017; Martinez‐Jimenez et al., 2017). No matter the cell‐cycle‐regulated periodic genes expression data was included as input or not, there was always a significant increase in noise during aging, implying that expect for cell cycle there were some other extrinsic factors contributing to the increase in noise during aging in yeast (Elowitz et al., 2002; Keren et al., 2015; Figure 2d). By single‐cell transcriptome analysis, we not only successfully recapitulated the results of the bulk population analysis but also teased out specific transcriptional features at the single‐cell resolution that would otherwise be masked in a bulk population. For example, by scRNA‐seq we revealed that while globally there were an age‐dependent upregulation of many mitochondrial genes between age groups, a small number of different but important mitochondrial genes were significantly downregulated in the slow‐dividing age subgroups compared with their fast‐dividing counterparts (Figure 3c–e). This provides novel and unprecedented insights into our understanding of the aging process. We also identified the gene expression of FIT3 together with several other iron transporter genes, such as FIT2 and FET3, had a negative correlation with the age of single yeast cells from both early and late timepoints. These iron transporter genes are known to be induced upon iron deprivation or mitochondrial DNA loss (Veatch et al., 2009). Moreover, these genes can all extend the RLS in yeast when deleted (McCormick et al., 2015; Figure 3g). These findings are consistent with a report published recently, showing age‐dependent heterogeneity via a FIT2 reporter that is correlated with vacuolar pH, mitochondrial function, and lifespan in sub‐populations of yeast cells (Chen et al., 2020). Although HAC1 was highly expressed in the slow‐dividing age subgroups of 16‐ and 36‐h/S compared with their fast‐dividing counterparts (Figure 3e), we did not see the same trend when comparing its targets gene expression between age subgroups (Figure S9c, Table S6). This implies that the highly expressed HAC1 total RNA from scRNAseq data may not reflect its active form of splicing. In the future, some new methods such as nanopore sequencing with long reads may help address this issue. Our scRNA‐seq dataset suggests a relatively poor mitochondrial function in the slow‐dividing cells of both early and late age subgroups (Figure 3c,e). This is in accord with the recent work about two aging modes in individual yeast cells: mode 1 with nucleolar decline which inclined to be long‐lived and mode 2 with mitochondrial decline which inclined to be short‐lived (Li et al., 2020). However, presently it remains challenging to disentangle the cause‐effect relationships between mitochondrial dysfunction and early heterogeneity during aging. We keep optimistic that these problems can be solved if the potential of modern single‐cell technologies integrated with other new methods are fully employed. Based on the scRNA‐seq data and knowledge of TF targets in the budding yeast Saccharomyces cerevisiae (Gasch et al., 2017; Monteiro et al., 2020), we also explored TF regulatory variation at the single cell level and found distinctive temporal regulation of TFs during aging in yeast. YAP1 is a key TF responding to oxidative stress (Temple et al., 2005; Toone & Jones, 1999), and it was highly activated in early age and fast‐dividing subgroup (16‐h/F) compared with its slow‐dividing counterpart (16‐h/S), implicating its vital role during early age, which in turn affects the overall lifespan. RPN4, the TF essential for proteasome biogenesis and RLS extension (Undine et al., 2011; Xie & Varshavsky, 2001), was prominently activated in late age and fast‐dividing subgroup (36‐h/F) compared with its slow‐dividing counterpart (36‐h/S), suggesting that the proteasome activity is essential for maintaining the vitality of yeast cells during late age (Figure 4b,c; Figure S9a,b). These aforementioned findings imply that both the mitochondrial dysfunction and the inability to respond to oxidative stress occurred earlier than the decline of proteostasis during aging in yeast, especially in the slow‐dividing age subgroups which inclined to be short‐lived (Figure 3c–e), although the detailed mechanism requires further investigation.
WT Saccharomyces cerevisiae in both BY4741 and BY4742 backgrounds were used for single‐cell imaging analysis. The strain of Hsp104‐GFP was derived from the standard GFP strain library in WT BY4741 background. WT BY4742 background was used in scRNA‐seq during aging. WT BY4741 background was used in the replicative lifespan assay of FIT3Δ. For single‐cell imaging, the cells were grown in the YPD liquid media before and after loading into the microfluidic chips. For scRNA‐seq during aging and replicative lifespan assay of FIT3Δ, the cells were grown on SD solid agar plates.
The approach for single‐cell imaging data analysis has been reported in detail elsewhere (Zhang et al., 2012). Yeast cell culture was grown in YPED at 30°C with OD600 of 0.5 before loading into the microfluidic device by a syringe connected to an automatically controlled peristaltic pump. The microfluidic device was mounted on a Nikon TE2000 time‐lapsed microscope by a customized holder. Bright field images were taken once every 10 min throughout the whole life, and fluorescent images were taken once every 2 or 4 h for measuring the HSP104‐GFP level. The images were processed by ImageJ and MATLAB.
We first inoculated WT yeast cells onto a solid agar plate with SD media for overnight and followed a standard protocol of replicative lifespan assay by continual (no storage in the 4°C fridge overnight) manual microdissection (Steffen et al., 2009). In detail, we selected relatively young and small sized cells from the yeast colonies and aligned them on the same agar plate. After one and a half hours, we dissected and discarded mother cells, retaining daughter cells as our initial age 0 cells. Then at 3 time points (2, 16 and 36 h after birth), single yeast aging cells from the plate were manually dissected and placed individually into a single tube prefilled with ~4 μl lysis buffer, containing 0.5%Triton, 2.5 μM oligo‐dT, 2.5 mM dNTP (Invitrogen, 1959189), 8000 molecules of external RNA control consortium (ERCC) spike in, 3 × 10−2 U/μl zymolyase (ZYMO, E1004‐A), 1 U/μl Recombinant RNase Inhibitor (TaKaRa, AI41189A). Zymolyase was added for efficiently digesting the cell wall and external RNA control consortium (ERCC) spike‐in for assessing technical noise. Then we immediately put lysis tube containing single yeast cell into liquid nitrogen and then stored in a −80°C freezer before the next steps for library preparation. Once we finished the sampling step, we should start the library preparation as soon as possible.
After collecting all the single yeast aging cells, we performed scRNA‐seq based on Smart‐seq2 (Picelli et al., 2014) with fine optimization. To efficiently lyse the single yeast aging cell and avoid possible mRNA degradation, we vigorously vortexed the lysis tubes (~4 μl) for 1 min and spin down in a cold room (4°C). Then we kept the lysis tubes at 30°C for 10 min, followed by 3 min at 72°C. Subsequently, we added the RT reaction mix (RT‐buffer and Invitrogen SuperScript II) for reverse transcription. Reverse transcription was carried out at 42°C for 90 min first, followed by 12 rounds of temperature cycling between 50 and 42°C with 2 min each. The reaction was heat inactivated at 70°C for 15 min and then cooled down to 4°C. The oligo‐dT and TSO primers used here were biotinylated to avoid potential production of excessive primer dimers and concatamers. After RT, the cDNA were amplified between 20 and 25 cycles using KAPA HiFi enzyme. After cDNA amplification, the samples were purified using Agencourt AMPure XP beads at 0.8× bead concentration and quantified using Qubit Hs Assay (Life Technologies). We also checked the samples by a fragment analyzer to confirm the clean peak at ~1 kb before subsequent processing. 1–2 ng of cDNA was subjected to a tagmentation‐based protocol (Vazyme TruePrep Kit) with 10 min at 55°C and dual index amplification for the library with 8–12 cycles. The final libraries were purified twice using AMPure XP beads at 0.8× bead concentration and resuspended in 15–20 μl elution buffer. Libraries were then quantified using Qubit Hs Assay before pooling for sequencing. Sequencing was performed in paired‐end mode using Illumina NextSeq.
Paired‐end reads were mapped to the S288c Saccharomyces cerevisiae genome R64 version (www.yeastgenome.org) with ERCC spike‐in sequences added using HISAT2 (version 2.1.0). Resulting bam files were sorted and indexed using samtools (version 1.1). Final read counts mapped to genes were extracted using FeatureCounts. Sequenced single yeast aging cells were removed from the analysis if they have <1000 genes detected and 40,000 total mapped reads per cell, or if the proportion of ERCC spike‐ins to total‐mapped reads was >0.74. After filtering, a scRNA‐seq data set with 125 single yeast aging cells was used for the subsequent analysis.
Unless noted, normalization of raw read counts was done using the DESeq2 (Love et al., 2014) package (v.1.22.2) in R. The size factor was computed by a formula embedded in DESeq2 for each cell based on the raw read counts matrix of all samples. Then these size factors were applied for normalizing different cells and finally the gene expression values are presented in the log2 space (log2NormCounts).
We used two methods to estimate the cell‐to‐cell transcriptional variability during aging in yeast. The first was a correlation‐based method modified from Enge et al. (2017), where the transcriptional noise was expressed as biological variation over technical variation. First, we calculated the biological variation b ij = 1‐cor(x ij , u i ), where u i was the mean gene expression vector for the single cells in age group of i (2, 16, and 36 h), and x ij was the gene expression vector of cell j in the age group of i. Next, we calculated the corresponding technical variation t ij = 1−cor(xijcontr, u contr) where xijcontr and u contr are the expression vector and mean expression vector of the ERCC spike‐in controls. Finally the measurement of b ij /t ij which reflected the biological noise as a fraction of technical noise for each cell was used for boxplot across different age groups as shown in Figure 1b. The second method was based on quantitative statistics reported previously (Nadal‐Ribelles et al., 2019; see Supplementary Note 6 of Brennecke et al. (2013) for details of the statistical model). Briefly, to infer the genes that were highly variable within each age group, a linear regression model was applied to fit the relationship between the squared coefficient of variation (CV2) and the mean expression of ERCC spike‐ins, and only genes with biological squared coefficient of variation >0.25 (CV2 > 0.25) and FDR < 0.1 after multiple testing correction were regarded as HVGs.
The differential gene expression analysis between pairwise age groups and subgroups was based on DESeq2 (Love et al., 2014) with default parameters, taking log2FC > 1 and adjusted p value <0.05 as significant. GO analysis of these differentially expressed genes was performed by functional annotation tool of DAVID (Dennis G Jr et al., 2003) that classify the ontology of each gene into biological process or cellular component. The GO term enrichment results derived from DAVID were further verified alternatively by the R package of ClusterProfiler (Yu et al., 2012).
To identify transcription factors with distinct regulation between age subgroups, three statistical approaches were applied stringently. The first one was to conventionally compare the median TF targets expression between age subgroups. We took log2FC (FoldChange) of the median TF targets expression between age subgroups >1 (log2FC > 1) and a welch t test p value <0.01 as significant, which resulted in 16 and 11 TF targets, respectively, that were significantly activated in the age subgroups of 16‐ and 36‐h/F compared with to their counterparts (Figure S7a,b; Table S6). The second one was to further run a Wilcoxon rank sum test for each single cell that compare internally the normalized gene expression levels of each set of TF targets to all other detected genes for that cell, taking p < 0.0001 as criterion (indicated as regulon activity “on”), followed by intersection with TF targets derived from the first approach. This approach was similar with that from Gasch et al. (2017). The last one was to correlate the TF targets expression with the generation of single cells in the age groups of 16‐ and 36‐h, respectively, taking p < 0.05 as criterion, followed by intersection with TF targets derived from the former two approaches to avoid potential false‐positive results.
Raw read counts matrix with or without cell‐cycle‐regulated periodic genes (Granovskaia et al., 2010) were used as inputs for PCA by Seurat (Butler et al., 2018). When the cell‐cycle‐regulated periodic genes were included, Seurat generates 631 common variable genes of all 125 single yeast aging cells, whose normalized read counts are further applied for PCA. When the cell‐cycle‐regulated periodic genes were excluded, Seurat generated 599 common variable genes of all 125 single yeast aging cells for PCA.
Y.Z. and Y.H. conceived and designed the project. Y.Z., J.W., Y.S., S.J., X.Z., and G.K.A. conducted the experiments. Y.Z., J.W., B.K., Q.L., J.W., X.Z. and Y.H. analyzed the data. Y.Z., J.W., B.K., X.Z., and Y.H. wrote the manuscript with the help from all other authors.
The authors declare no conflict of interest.
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PMC9649601 | 36225129 | Debasish Kumar Ghosh,Shruti Pande,Jeevan Kumar,Dhanya Yesodharan,Sheela Nampoothiri,Periyasamy Radhakrishnan,Chilakala Gangi Reddy,Akash Ranjan,Katta M. Girisha | The E262K mutation in Lamin A links nuclear proteostasis imbalance to laminopathy‐associated premature aging | 12-10-2022 | lamin A,laminopathy‐associated progeroid disorder,loss of DNA damage repair,nuclear proteostasis imbalance,protein aggregation,protein instability | Abstract Deleterious, mostly de novo, mutations in the lamin A (LMNA) gene cause spatio‐functional nuclear abnormalities that result in several laminopathy‐associated progeroid conditions. In this study, exome sequencing in a sixteen‐year‐old male with manifestations of premature aging led to the identification of a mutation, c.784G>A, in LMNA, resulting in a missense protein variant, p.Glu262Lys (E262K), that aggregates in nucleoplasm. While bioinformatic analyses reveal the instability and pathogenicity of LMNAE262K, local unfolding of the mutation‐harboring helical region drives the structural collapse of LMNAE262K into aggregates. The E262K mutation also disrupts SUMOylation of lysine residues by preventing UBE2I binding to LMNAE262K, thereby reducing LMNAE262K degradation, aggregated LMNAE262K sequesters nuclear chaperones, proteasomal proteins, and DNA repair proteins. Consequently, aggregates of LMNAE262K disrupt nuclear proteostasis and DNA repair response. Thus, we report a structure–function association of mutant LMNAE262K with toxicity, which is consistent with the concept that loss of nuclear proteostasis causes early aging in laminopathies. | The E262K mutation in Lamin A links nuclear proteostasis imbalance to laminopathy‐associated premature aging
Deleterious, mostly de novo, mutations in the lamin A (LMNA) gene cause spatio‐functional nuclear abnormalities that result in several laminopathy‐associated progeroid conditions. In this study, exome sequencing in a sixteen‐year‐old male with manifestations of premature aging led to the identification of a mutation, c.784G>A, in LMNA, resulting in a missense protein variant, p.Glu262Lys (E262K), that aggregates in nucleoplasm. While bioinformatic analyses reveal the instability and pathogenicity of LMNAE262K, local unfolding of the mutation‐harboring helical region drives the structural collapse of LMNAE262K into aggregates. The E262K mutation also disrupts SUMOylation of lysine residues by preventing UBE2I binding to LMNAE262K, thereby reducing LMNAE262K degradation, aggregated LMNAE262K sequesters nuclear chaperones, proteasomal proteins, and DNA repair proteins. Consequently, aggregates of LMNAE262K disrupt nuclear proteostasis and DNA repair response. Thus, we report a structure–function association of mutant LMNAE262K with toxicity, which is consistent with the concept that loss of nuclear proteostasis causes early aging in laminopathies.
Abbreviations DNA deoxyribonucleic acid HGPS Hutchinson‐Gilford progeria syndrome HSPA1A heat shock protein family A (Hsp70) member 1A LMNA lamin A MRE11 MRE11 homolog, double strand break repair nuclease mRNA messenger ribonucleic acid NAT10 N‐acetyltransferase 10 nM nanomolar nm nanometer PCR polymerase chain reaction PSMD8 proteasome 26S subunit, non‐ATPase 8 ORF open reading frame RanBP2 RAN binding protein 2 RMSD root mean square deviation RMSF root mean square fluctuation SASA solvent accessible surface area S.D. standard deviation SUMO2 small ubiquitin like modifier 2 UBE2I ubiquitin conjugating enzyme E2 I UV ultraviolet
The imbalance of proteome in the nucleus of eukaryotic cells is orchestrated by the accumulation of misfolded proteins or suboptimal protein quality control systems, leading to a systemic failure of nuclear homeostasis (Enam et al., 2018). Mutant proteins could evade the surveillance mechanisms of quality control systems, and their overload in the nucleus in the form of aggregates could potentially lead to intrinsic proteotoxicity (Morimoto, 2008). Usually, nuclear protein aggregates form specific structures such as inclusion bodies, promyelocytic leukemia bodies, and speckles (Matera et al., 2009). In this context, not only nucleoplasmic proteins but also mutants of nuclear envelope proteins, such as lamins, could also cause nuclear pathogenicity in various rare diseases such as laminopathies. (Rankin & Ellard, 2006). Laminopathies are a class of rare genetic disorders that are characterized by de novo heterozygous mutations in the lamin A (LMNA) gene (Kudlow et al., 2007). Clinically, many of the individuals with laminopathic disorders show several physiological symptoms of early aging, such as progeroid facial features, short stature with lower body weight, ectodermal tissue, and connective tissue defects etc. (Hennekam, 2006). Based on the mutation in LMNA, laminopathies are classified into typical and atypical forms (Hennekam, 2006). For example, Hutchinson–Gilford progeria syndrome (HGPS) is a typical laminopathy‐associated premature aging disorder. On the other hand, Malouf syndrome, mandibuloacral dysplasia and congenital muscular dystrophy are laminopathic disorders that show atypical features of early aging. Although the characteristics are similar, early‐onset laminopathies have more severe phenotypes than late‐onset laminopathies. In severe cases of laminopathies, lipoatrophy, cardiovascular problems (coronary occlusions), cerebrovascular occlusions, and stenosis are also observed (Hennekam, 2006). Lamins (LMNA, LMNB, and LMNC) are a class of nuclear proteins that form the core of the meshwork of the lamina of nuclear envelope (Gruenbaum & Foisner, 2015). Structurally, lamin proteins form a filamentous network in the lamina that defines the proper shape and morphology of the nucleus (Gruenbaum & Foisner, 2015). In addition, they also function in maintaining the elasticity of the matrix, proper positioning of nuclear receptors, and mechanotransduction (Dubik & Mai, 2020). The structure of LMNA protein comprises two N‐terminal helical rod domains, typically forming a coiled‐coil region, and a C‐terminal globular beta‐sheet domain flanked by a long‐disordered region (Ahn et al., 2019). LMNA is synthesized as prelamin A, which is C‐terminally isoprenylated and farnesylated. A proteolytic cleavage of C‐terminal eighteen amino acids of post‐translationally modified prelamin A forms the mature LMNA (Simon & Wilson, 2013). The rod domains are amphipathic helices, while the C‐terminal region resembles a low‐complexity region enriched with serine, histidine, and glycine residues. The N195 residue is reported to be required for nuclear translocation of LMNA (Dechat et al., 2010). Besides its function of mechanically supporting the nuclear lamina, LMNA interacts with various proteins to regulate transcription during fibroblast proliferation and myoblast differentiation (Burke & Stewart, 2013). Typical laminopathic cells show a deformed nuclear morphology with thick and lobular nuclear envelopes (Eriksson et al., 2003). Several of the mutations in LMNA result in this cellular phenotype. For example, a mutation in the 608th codon of LMNA creates a cryptic splice site that leads to a C‐terminally fifty amino acid truncated version of LMNA, called progerin (Eriksson et al., 2003). While progerin itself causes nuclear envelope disruption, its farnesylation exacerbates the condition (Yang et al., 2006). Some other truncation mutations of LMNA, such as Q432X (Yang et al., 2013), also lead to similar pathological features as progerin. A number of other studies have confirmed that the loss of binding of mutants of LMNA to nuclear envelope causes structural collapse of the nuclear lamina (Piekarowicz et al., 2017). Interestingly, most of these mutations are clustered in the C‐terminal globular domain of LMNA (Krimm et al., 2002), making this domain a mutational hotspot for laminopathic diseases. Although it is not known how the C‐terminal mutations render LMNA unable to tether to the nuclear envelope, an interplay of nesprin‐2 (Yang et al., 2013) and NAT10 (Larrieu et al., 2014) with LMNA may be a regulatory mechanism in this process. While many of the mutants of LMNA remain profusely distributed in the nucleoplasm (West et al., 2016), mutations in LMNA also lead to the protein's nucleoplasmic aggregation (Boudreau et al., 2012). Interestingly, mutations in both rod domains of LMNA can transform the protein into an aggregation‐prone entity, suggesting a common mechanism of aggregation of rod‐domain mutants of LMNA. Strikingly, a large number of disease‐associated mutations in LMNA involve substitutions of charged amino acids, suggesting that imbalance of charge distribution and hydrophobicity may lead to instability of LMNA under physiological conditions. However, a detailed mechanistic understanding and correlation between the mutations and the phenotypes of laminopathies are lacking. In this study, we report a p.Glu262Lys (E262K) mutation in the second rod domain of LMNA in an individual with atypical progeroid manifestations. Mutation of the conserved glutamic acid to lysine in LMNA leads to destabilization of the protein by inducing a helix‐to‐disorder structural transition of the mutation region, thus forming a hydrophobic patch that promotes aggregation of the protein in aqueous environments. The E262K mutation also prevents SUMOylation of LMNAE262K by preventing the binding of UBE2I to the mutant LMNA. Nucleoplasmic aggregates of LMNAE262K not only resist degradation but also sequester nuclear chaperones, proteasomal proteins, and DNA repair proteins to deregulate nuclear proteostasis and DNA repair pathways.
The proband is a second born male child of non‐consanguineous parents (Figure 1a). He had an uneventful antenatal period and born at term via normal vaginal delivery with a birth weight of 2.5 kg (−1.7 SD). His early growth and development were normal. Short stature, sparse hair, eyebrows and eyelashes, shallow orbits, narrow nasal bridge with broad nasal tip, craniofacial disproportion with micro‐retrognathia, and dental crowding were noted around sixteen years of age (Figure 1b). At this age, his weight was 21 kg (−2.6 SD), height was 141.5 cm (0.3 SD), and head circumference was 49 cm (−2.9 SD). While his fingernails were dark colored with longitudinal ridges, his hand radiographs were age appropriate without features of acroosteolysis (Figure 1b). The proband has high pitched voice, and he did not develop secondary sexual characters. Proband's echocardiography was unremarkable. Exome sequencing was performed from the genomic DNA of the blood cells of the proband. A heterozygous substitution at g.156134949G>A (corresponding to c.784G>A in mature mRNA) was identified in exon 4 of the LMNA gene of proband (Figure 1c and Tables S1, S2, Figure S1). Sanger sequencing in his parents did not show this variation (Figure 1c), confirming the de novo status of the variant in the proband. Interestingly, the c.784G>A base substitution in the LMNA gene, resulting in a non‐conservative E262K missense mutation in the LMNA protein (Figure 1d). The E262K mutation had also been previously reported in an individual with atypical progeroid syndrome and lipodystrophy (Yukina et al., 2021). The in silico algorithms predicted this mutation as damaging and pathogenic (Figure S2). The E262K mutation in LMNA occurs at the conserved sites (Figures 1e and S3). Therefore, this mutations would likely to generate unfavorable consequences on the structural and functional landscape of LMNA. Multiple sequence alignment of LMNA proteins of different organisms shows that the 262nd position of LMNA prefers acidic amino acids (Figure 1e). Substitution of this negatively charged amino acid with a basic amino acid, such as a lysine in LMNAE262K, would, as expected, affect the local stability of the region near the substitution. Several of the laminopathy‐associated sequence variations in LMNA are known to induce alternative splicing by introducing cryptic splice sites in LMNA pre‐mRNA, resulting in the formation of different isoforms of LMNA protein (Rodriguez et al., 2009). PCR of the reverse transcribed product of the open reading frame (ORF) of LMNA mRNA from fibroblasts showed no difference in the LMNA‐ORF length of the proband compared to the control (Figure 1f). This observation showed that the c.784G>A base substitution did not generate a cryptic splice site in the mutant LMNA mRNA. Thus, LMNAE262K protein predictably had the same length as the wild‐type LMNA protein. In addition, quantification of the mature mRNA of LMNA by qPCR showed no significant difference in the expression of the LMNA gene in the proband compared with control (Figure 1g). Therefore, the effect of the c.784G>A mutation in the LMNA gene on laminopathy‐associated progeroid manifestation is not related to the formation of alternative isoforms of LMNA nor to the differences in the expression of LMNAE262K and LMNA. Alternatively, the early aging in the proband could be due to an aberrant structure–function association of LMNAE262K.
We examined the expression and localization of LMNA and LMNAE262K in the control and proband fibroblasts. Strikingly, mutant LMNA formed nuclear aggregates in a significant number of fibroblasts of proband and showed a loss of its localization in the nuclear envelope (Figure 2a,b). In addition, LMNAE262K also showed a higher accumulation in cell (Figure 2c,d). LMNAE262K showed SDS insoluble aggregates which were observed as the higher molecular weight complexes in the immunoblot. Since the transcript level of LMNAE262K in the proband was not significantly different from the LMNA transcript in the control, the higher level of LMNAE262K in the proband compared to LMNA in the control may be due to decreased degradation of the former protein in the proband. LMNA contains two N‐terminal helical rod domains and a C‐terminal globular domain of beta‐sheets. Although the globular domain is a hotspot for laminopathy‐associated mutations, structural destabilization of LMNA is also driven by mutations in the rod domains of the protein. There is strong evidence that multiple mutations in the rod domains cause aggregation of LMNA (Boudreau et al., 2012; Piekarowicz et al., 2017), although the mechanism of this phenomenon is not clear. Having established that LMNAE262K aggregates in proband fibroblasts, we speculated that the E262K mutation contributes critically to the aggregation of LMNA, probably by modulating the stability of the second rod domain of the protein. To test this hypothesis, we purified bacterially expressed recombinant LMNA, LMNAE262K, and their individual second rod domains—LMNA‐rod2 and LMNAE262K‐rod2 (residues 226–387), and tested their ability to aggregate. Both LMNAE262K and LMNAE262K‐rod2 formed aggregates in a concentration‐dependent manner, whereas LMNA and LMNA‐rod2 did not form aggregates even at higher concentrations (Figure 2e–h). Aggregates of LMNAE262K and LMNAE262K‐rod2 formed in solution were probed by dynamic light scattering. At a higher concentration (10 μM), LMNAE262K and LMNAE262K‐rod2 formed soluble aggregates with the most abundant particulate diameters in the range of 192–342 nm and 458–1106 nm, respectively (Figure 2e,f). The formation of soluble aggregates of LMNAE262K and LMNAE262K‐rod2 was insensitive to salt concentrations, suggesting that aggregation of the rod2 domain of LMNAE262K is driven by hydrophobic interactions. Ex situ measurements of protein particles' height distribution by atomic force microscopy also revealed large aggregates of LMNAE262K and LMNAE262K‐rod2 at higher concentrations (Figure 2g,h). The aggregates of LMNAE262K and LMNAE262K‐rod2 were not fibrillar but appeared as liquid droplets, suggesting that the aggregates were probably formed by phase separation in a mechanism similar to that of other aggregation‐prone proteins such as MAPT (also known as tau; Kanaan et al., 2020). On the other hand, LMNA did not form large particulates in solution. At higher concentration (10 μM), LMNA formed most abundant particulates with a diameter of 7.5–92 nm, while LMNA‐rod2 showed two distinct particle populations with diameters ranging from 6.5–15 to 68–164 nm (Figure 2e,f). In atomic force microscopy, LMNA and LMNA‐rod2 did not show large droplets but smaller oligomeric organization (Figure 2g,h). At lower concentration (100 nM), LMNA and LMNA‐rod2 showed particles with diameter of <10 nm. These results indicate that the second rod domain of LMNA is intrinsically prone to oligomerization, possibly due to its coiled‐coil structural form. However, the E262K mutation transforms the rod2 domain into a structure which is more prone to aggregation. To understand whether the E262K mutation alters the structural properties of LMNAE262K compared to LMNA, we analyzed the secondary structure components of LMNA and LMNAE262K. Slow thermal denaturation during circular dichroism spectroscopy at 222 nm light wavelength revealed faster melting of LMNAE262K than wild‐type LMNA (Figure 2i), suggesting that LMNAE262K contains a lower proportion of secondary structures than wild‐type LMNA. Furthermore, scanning at far UV wavelengths in circular dichroism spectroscopy clearly confirmed a reduction in helical structures and an increase in the disordered region of LMNAE262K compared with wild‐type LMNA (Figure 2j). Thus, aggregation of LMNAE262K not only required the second rod domain but is also coupled with unfolding of one or more regions of the protein.
To understand how unfolding relates to the aggregation of LMNAE262K‐rod2 in a real‐time setting, we performed unconstrained molecular dynamics simulations of the LMNA‐rod2 and LMNAE262K‐rod2 structures. The simulation data showed that the LMNAE262K‐rod2 is more unstable than the LMNA‐rod2. The higher root mean square deviation (RMSD) values of the backbone of LMNAE262K‐rod2 indicated greater instability of the protein compared to LMNA‐rod2 (Figure 3a). The higher root mean square fluctuation (RMSF) values of K262 and the neighboring residues in LMNAE262K‐rod2 compared with E262 and the neighboring residues of LMNA‐rod2 (Figure 3b) indicated specific instability of the N‐terminal region of LMNAE262K‐rod2. Accordingly, the overall structure of LMNAE262K‐rod2 became more rigid than the structure of LMNA over time, as evidenced by a smaller radius of gyration (Rg) of LMNAE262K‐rod2 than LMNA‐rod2 (Figure 3c). The K262 and its upstream helical regions (residues 242–268) in LMNAE262K‐rod2 were rapidly unfolded into an unstructured region (Figures 3d and S4), and the nascent unfolded region reorganized such that several of the hydrophobic residues (A242, A244, L245, L248, A250, V256, and L263) in this region were brought closer together to form a topology of two continuous hydrophobic patches (Figure 3d). In contrast, E262 and the upstream region remained as an intact helix (Figure 3d), and the hydrophobic residues in the 242–268 amino acid region of LMNA‐rod2 did not undergo proximal positioning (Figure 3d). Moreover, the solvent‐accessible surface area (SASA) of many hydrophobic residues in the two patches of LMNAE262K‐rod2 was higher than that SASA of the corresponding hydrophobic residues of LMNA‐rod2 (Figure 3e). Since the exposure of the hydrophobic segments to the aqueous environment is thermodynamically unfavorable, the two solvent‐exposed hydrophobic patches of LMNAE262K‐rod2 exposed to the solvent also contributed to the decrease in the energetic stability of the protein. Analysis of the equilibrium data revealed the aggregation index of the different regions of LMNA‐rod2 and LMNAE262K‐rod2 over the simulation period. While both structures contained an aggregation‐prone region in the C‐terminal side of the rod domain (Figure 3f), the temporal unfolding‐coupled clustering of the nonpolar residues in the two hydrophobic patches near the mutation of LMNAE262K‐rod2 also resulted in an aggregation propensity in these residues (Figure 3f). The extended hydrophobicity of the patches appeared to correlate with the aggregation properties of the mutation region of LMNAE262K‐rod2. In contrast, the hydrophobic residues near E262 of LMNA‐rod2 did not induce aggregation properties (Figure 3f). Because the hydrophobic residues near E262 of LMNA‐rod2 were scattered and surrounded by polar and charged residues, they were more in equilibrium with water and did not have much aggregation potential. Taken together, these results indicate that local unfolding of helical structures near the E262K mutation of LMNAE262K facilitates juxta‐positioning of hydrophobic residues that act as aggregation‐prone patches in aqueous environments.
Having established the mechanism of aggregation of LMNAE262K, we sought to determine whether there were differences in clearance of LMNAE262K in proband fibroblasts compared to LMNA in control fibroblasts and whether modulation of posttranslational modifications of LMNAE262K affected its differential degradation. Immunoblotting against LMNA from nuclear lysate of proband and control fibroblasts clearly showed higher accumulation of LMNAE262K in proband fibroblasts (Figure 4a,b). Because transcriptional expression of the LMNA gene was not significantly different in proband compared with control, the data suggest that LMNAE262K protein is more resistant to degradation than LMNA. To further validate the lower intracellular degradation and higher half‐life of LMNAE262K compared to LMNA, we checked the level of wild‐type and mutant LMNA in control and proband fibroblasts that were treated cycloheximide (translation inhibitor). While the level of LMNA temporally decreased in the cycloheximide‐treated control fibroblasts (Figure 4a,b), the level of LMNAE262K did not reduce significantly over time in cycloheximide‐treated proband fibroblasts (Figure 4a,b), indicating that LMNAE262K is more resistant to degradation in cellular system. SUMOylation regulates the LMNA degradation potential under various physiological and stressed conditions (Zhang & Sarge, 2008). SUMOylation of nuclear LMNA during DNA damage and replication stress facilitates nucleophagy (Li et al., 2019), whereas cardiomyopathy‐related mutants of LMNA, such as K201R, E203G, and E203K, exhibit loss of SUMOylation (Zhang & Sarge, 2008). Several lysine residues of LMNA are targets of SUMOylation by SUMO2 (Hendriks et al., 2017), and we tested whether the E262K mutation can abolish SUMOylation of the lysine residues of LMNAE262K. Denaturing immunoprecipitation of LMNA and LMNAE262K from the nuclear lysates of fibroblasts from Proband and control, followed by immunoblotting against SUMO2 and LMNA, showed lower SUMOylation of LMNAE262K of proband compared with LMNA of control (Figure 4c). Similarly, LMNAE262K of proband did not show as strong colocalization with SUMO2 in the nucleus as colocalization of SUMO2 with LMNA of control (Figure 4d,e). The lack of SUMOylation of aggregated LMNA mutant has been reported previously (Zhang & Sarge, 2008), and our finding of the loss of SUMOylated LMNAE262K in laminopathy‐associated progeroid condition supports the idea that aggregated nuclear LMNA restricts its SUMOylation by one or more mechanisms. UBE2I is a ubiquitous E2 ligase known to SUMOylate LMNA (Li et al., 2019). To determine the possible reason for the lack of SUMOylation of LMNAE262K, we focused on the effects of the E262K mutation on the binding of UBE2I to LMNAE262K. Sequence analysis revealed a consensus UBE2I binding site (ΨKxE, Ψ is a large hydrophobic amino acid, (Bernier‐Villamor et al., 2002)) at 259YKKE262 of LMNA (Figure 4f), which is also the region mutated in LMNAE262K. Because the E262K mutation disrupts the consensus binding site of UBE2I in LMNAE262K (the sequence is 259YKKK262 in LMNAE262K), we suspected that UBE2I would not bind to LMNAE262K. Indeed, UBE2I showed significantly less colocalization with LMNAE262K aggregates in proband fibroblasts compared with its colocalization with LMNA in control fibroblasts (Figure 4g,h). Since UBE2I remains in a multisubunit complex of RanBP2/RanGAP1‐ SUMO/UBE2I (Werner et al., 2012), we tested whether LMNA interacts directly with UBE2I or via other subunits of the E3 ligase complex. Isothermal titration calorimetry showed a high binding affinity of recombinant UBE2I to LMNA but not to LMNAE262K (Figure 4i), indicating a direct association of UBE2I with the 259YKKE262 region of LMNA and an inability to interact with the 259YKKK262 of LMNAE262K. Based on the above observation, we checked whether the absence of SUMOylation of LMNAE262K mediated by UBE2I was responsible for its decreased degradation in proband. Overexpression of UBE2I in control fibroblasts decreased the nuclear concentration of LMNA, but the same effect was not observed in UBE2I‐overexpressed proband fibroblasts (Figure 4j), proving that the increased accumulation of LMNAE262K and its aggregates in proband fibroblasts is due to the lack of SUMOylation of LMNAE262K due to the nonbinding of UBE2I to LMNAE262K.
To understand the mechanisms linking aggregation of LMNAE262K to proteotoxicity, we investigated whether accumulation of LMNAE262K aggregates globally disrupt nuclear proteostasis. Because phase‐separated aggregates nonspecifically sequester other proteins (Yang & Hu, 2016), LMNAE262K aggregates could attract and sequester essential proteostasis‐maintaining proteins. Based on co‐immunostaining of LMNA and HSPA1A (chaperone protein HSP70, member 1A) and LMNA and PSMD8 (proteasomal protein), followed by fluorescence analysis in proband fibroblasts, significant colocalization of HSPA1A and PSMD8 with nuclear aggregates of LMNAE262K was evident (Figure 5a–d), implying sequestration of chaperones and proteasomal proteins by LMNAE262K aggregates. We found that sequestration of chaperones such as HSPA1A by LMNAE262K aggregates promoted the formation of nuclear aggregates, as evidenced by positive staining of these aggregates with Proteostat dye in the nucleus of proband fibroblasts (Figure 5e,f), whereas the nucleus of control fibroblasts did not show a significant amount of protein aggregates (Figure 5e,f). Interestingly, Proteostat not only stained LMNAE262K aggregates, but there was also an abundance of non‐LMNAE262K aggregates in the nucleus (Figure 5g). These data suggest that sequestration of HSPA1A (and probably other nuclear chaperones) by LMNAE262K aggregates reduced the pool of active chaperones in the nucleoplasm, a phenomenon that correlated with a global failure of nuclear proteostasis, leading to the formation of aggregates of various proteins in the nucleus. Using ubiquitin staining, we found that LMNAE262K‐induced proteotoxicity not only formed nuclear protein aggregates, but that these aggregates were progressively ubiquitinated (Figure 5h,i). However, because of possible inactivation of proteasomes sequestered by LMNAE262K, the ubiquitinated proteins were not optimally degraded, and their successive accumulation led to reorganization of the ubiquitinated protein aggregates in the form of large spheres (Figure 5j,k). A combination of the above data is consistent with the phenotypes of nuclear proteotoxicity and suggests an active role of LMNAE262K in triggering nuclear stress through the formation of heterogeneous protein aggregates.
Aggregation of proteins in the nucleus has emerged as one of the major DNA‐damaging stressors in nucleopathies (Gruenbaum & Foisner, 2015). Immunocytochemistry against the DNA damage marker phosphor‐serine‐139‐H2A.X (pS15‐H2A.X, also known as γ‐H2A.X) showed a higher number of γ‐H2A.X‐positive foci in a significant number of proband fibroblasts compared with control fibroblasts (Figure 6a,b), representing a higher level of DNA damage in the proband cells. However, the damaged LMNAE262K aggregates did not exhibit coaccumulation of damaged DNA foci, as indicated by the very low colocalization of LMNAE262K with γ‐H2A.X (Figure 6c,d). The lack of physical association of LMNAE262K with damaged DNA sites suggests a passive mechanism that blocks DNA repair by LMNAE262K aggregates. Previous studies have shown that proteomic stress perturbs DNA repair pathways and associated signaling mechanisms (McAdam et al., 2016; Squier, 2001). As with HSPA1A and PSMD8, LMNAE262K aggregates were observed to sequester many of the essential DNA damage repair proteins such as MRE11 and XRCC5 (also known as KU80; Figure 6e,f). While these DNA repair proteins were diffusely distributed in the nucleus of control fibroblasts, they were highly enriched in LMNAE262K aggregates (Figure 6e,f). Therefore, it was evident that proteotoxic stress in nucleus modulated the decreased DNA repair process in proband fibroblasts. Progeroid cells and DNA damage are known to trigger cellular senescence (d'Adda di Fagagna, 2008; Wheaton et al., 2017). Loss of nuclear lamins is also observed in aging cells undergoing senescence (Matias et al., 2022). Therefore, we further investigated the extent to which loss of LMNAE262K from the nuclear envelope and DNA damage induce senescence in the proband fibroblasts. Because increased expression of p16INK4a and p21WAF1/Cip1 are hallmarks of senescent cells (Kohli et al., 2021; Matias et al., 2022), we checked the levels of these proteins in control and proband fibroblasts. Compared with the control fibroblasts, the proband fibroblasts showed significantly increased expression of p16INK4a and p21WAF1/Cip1 (Figure 6g–j), indicating that the proband fibroblasts were undergoing senescence. From these observations, we concluded that sequestration of DNA damage repair proteins by LMNAE262K aggregates induces critical genotoxicity, effectively leading to deregulation of DNA damage repair pathways and cellular senescence in proband cells.
Premature aging is a class of developmental disorders that are characterized by genetic mutations and hallmarked by proteomic imbalance in the cell (Morimoto & Cuervo, 2014). The shift in proteostasis during normal aging overloads the cellular protein quality control system with nonfunctional and toxic protein forms, such as misfolded and aggregated proteins, resulting in proteotoxicity (David, 2012). While in normal aging, the effectiveness of chaperones and proteolytic mechanisms is gradually reduced by various molecular mechanisms, in premature aging diseases, particularly in various types of progeria and neurodevelopmental disorders, the components of the protein quality control system are overwhelmed by the accumulation of one or more mutant protein aggregates, thereby modulating organellar homeostasis and cell survival signaling pathways (Dreesen, 2020). However, whereas normal age‐related proteotoxicity causes endoplasmic reticulum and cytosolic stress, certain types of progeria, such as HGPS, exhibit nuclear proteotoxicity (Kubben & Misteli, 2017). There are at least eleven phenotypically distinct single gene disorders, including both autosomal recessive and autosomal dominant disorders, due to genomic alterations in LMNA. To date, most progeroid‐associated LMNA mutations have been reported to be found primarily in the C‐terminal globular domain. A few scattered mutations in the rod‐1 and rod‐2 domains also result in a similar phenotype. The C‐terminal mutations of LMNA, such as C‐terminally truncated LMNA and G608S, do not lead to mislocalization of the protein (Kubben & Misteli, 2017). Instead, binding of these mutant LMNA proteins to the envelope results in irregularly shaped nuclei (Kubben & Misteli, 2017). Farnesylation of the C‐terminal residues of mutant LMNA may contribute to this process. Mutations such as A57P, L140R, etc. in the rod‐1 domain preclude dimerization of LMNA monomers, resulting in diffuse nucleoplasmic localization of LMNA (Casasola et al., 2016). However, some of the mutations such as S143P, E161K, etc. in the rod‐1 domain generate aggregation‐prone LMNA structures (West et al., 2016). On the other hand, the functions of the second rod domain of LMNA are unclear, and the effects of mutations in this region in respect to laminopathic disorders are not well characterized, although such mutations, such as D300G (Kane et al., 2013), have more severe effects on progeria. Our data show that an E262K mutation in the rod‐2 domain collapses the helical structure in the mutation region. Because the conserved E262 residue is involved in a large number of inter‐residue interactions, this residue could be considered as an important node in the protein structure network of wild‐type LMNA. The E262K mutation in LMNAE262K is destabilizing, possibly due to repulsive interactions of K262 with the similarly charged neighboring lysine residues (K260, K261, and K265). The repulsive and steric effects of the E262K mutation lead to a loss of interaction of K262 with the neighboring residues, allowing the region to undergo a transition from helix to disorder. Interestingly, the unfolded region reorganizes temporally such that several of the hydrophobic residues near the mutation are proximal to each other, forming two contiguous hydrophobic patches. The energetically unfavorable solvent‐exposed hydrophobic patches near the mutation of LMNAE262K possibly undergo hydrophobic patch collapse and form aggregates. However, in wild‐type LMNA, these hydrophobic residues are dispersedly distributed by intermittent charged and polar residues. Given the energetic constraints, E262 of the wild‐type LMNA can only be replaced by an aspartic acid to maintain the essential salt bridges with K260, K261, and K265. Global level analysis revealed that substitution of this residue with another amino acid would essentially destabilize this region. Disordered regions in multiple proteins, such as in TDP‐43, amyloid beta peptides, etc., have been reported to cause aggregation of the respective proteins (Uemura et al., 2018). While hydrophobic residues play a crucial role in this process (Fink, 1998), charged residues can also trigger aggregation through electrostatic interactions in some proteins, such as in FUS (Shelkovnikova et al., 2014). Aggregation of LMNAE262K follows the former model of disorder and hydrophobicity for aggregation. This model of aggregation can be extrapolated to other LMNA mutants. For example, disruption of the helix by introduction of the helix‐breaking proline and glycine residues in certain mutations, such as in A57P, R60G, S143P, and D300G, could potentially cause destabilization and aggregation of LMNA in a manner similar to E262K. In contrast, mutations in the globular beta‐sheets or in the C‐terminal ‘SHG‐rich’ region have not been shown to cause aggregation. Although the liquid–liquid phase separation of LMNAE262K in the nucleoplasm is evident, further studies on its amyloidogenic properties and nucleation steps could shed light on the generalized aggregation mechanisms of the rod domain‐associated mutations of LMNA. Mutations in the rod domains of LMNA are known to induce aggregation properties of the protein. However, different mutations transform LMNA into different types of aggregation‐prone entities. For example, mutation of a basic or acidic amino acid to uncharged amino acids, such as D192G, H222P, R249W, and D446V, forms mild and smaller aggregates of the mutant LMNA. In contrast, mutation of residues to charged amino acids, such as L85R, E161K, E262K, and R386K, results in large nucleoplasmic aggregates of mutant LMNA. Moreover, some aggregate‐forming LMNA mutants remain in the nuclear lamina, whereas other aggregation‐prone LMNA mutants are completely mislocalized to the nucleoplasm. Interestingly, the aggregates of the different LMNA mutants are morphologically different. While some of the mutants form smaller, punctate aggregates, others form filamentous and globular aggregates. It is likely that specific mutations in LMNA trigger aggregation of the protein by different mechanisms. Although not much is known about the nucleation process of LMNA mutants, the aggregation of the phosphorylation‐deficient mutant (S143P) and the charged‐to‐nonpolar mutants demonstrate the importance of specific charged residues in maintaining the stability of LMNA. Loss of these charged residues could lead to a local change in hydrophobicity, resulting in aggregation of LMNA mutants in the aqueous nucleoplasm. Long‐distance electrostatic interactions may also play an enhancing role in the aggregation of the mutant LMNA proteins. Our study shows that the formation of disorder region and hydrophobic patches near the E262K mutation causes phase separation and aggregation of LMNAE262K. Although not deciphered, phase‐separated aggregates of LMNAL85R and LMNAR386K may form in the nucleoplasm by a mechanism similar to that of LMNAE262K. However, the formation of intermediate filaments of LMNAE161K and nuclear speckles of LMNAH222P could occur by different mechanisms. Nevertheless, aggregate‐forming LMNA mutants, with the exception of LMNAE262K, are involved in the development of dilated cardiomyopathy and Emery–Dreifuss muscular dystrophy 2. A previous study and we show that LMNAE262K causes atypical progeria. The aggregation characteristics and clinical manifestations of LMNA mutant aggregates are summarized in Table 1. Posttranslational modifications of various residues of LMNA occur in response to cell stage or stress. LMNA is phosphorylated at several serine and threonine residues during mitotic division (Olsen et al., 2010), whereas several of the lysine residues are SUMOylated upon DNA damage and replication stress (Hendriks et al., 2015). SUMOylation of nuclear proteins, including LMNA, is redundantly mediated by the E2 SUMO ligase UBE2I. The E262K mutation in LMNA abolishes the consensus binding site of UBE2I (259YKKE262 to 259YKKK262). Surprisingly, this mutation completely destroys the binding potential of UBE2I to LMNAE262K, although there is another UBE2I binding site at 200MKEE203. Surprisingly, the 200MKEE203 region in rod‐1 is located exactly opposite to the 259YKKE262 region of rod‐2. Therefore, the UBE2I in the heterotrimeric complex of RanBP2/RanGAP1‐ SUMO/UBE2I could bind alternatively to both regions without being released from LMNA. Unfolding of the E262K mutation region potentially affects binding of the UBE2I complex to LMNAE262K in a manner that also diminishes the binding potential of UBE2I to the 200MKEE203 region, resulting in possible loss of SUMOylation of lysine residues such as K201 and K260. The very low level of SUMOylation of LMNAE262K may be due to the activity of an uncharacterized SUMO ligase of LMNA. The function of SUMOylation of LMNA is elusive. It is possible that SUMOylation complements ubiquitination in terms of modulating the stability of LMNA. Our results indicate that loss of SUMOylation of LMNAE262K prevents its degradation, which may also contribute to aggregation of the protein. A previous report suggests that SUMOylation of LMNA drives its degradation during nucleophagy (Li et al., 2019). Loss of SUMOylation at K201 also leads to the accumulation of LMNAK201R in nucleoplasmic aggregates (Zhang & Sarge, 2008), suggesting that SUMOylation is not only required for the localization of LMNA in the nuclear envelope, but that loss of this modification also regulates the unusual accumulation of mutant LMNA under pathological conditions. SUMOylation of the LMNA tail is also impaired in partial lipodystrophy‐causing mutations (Simon et al., 2013). Consistent with these results, our finding highlights the need for proper SUMOylation of LMNA with respect to the localization specificity and degradation capacity of this protein. Additionally, the interplay of ubiquitination and SUMOylation may be an interesting aspect for understanding the spatiotemporal clearance of LMNA in normal and diseased conditions. Mutation of LMNA as a cause of several premature aging disorders has long been known, although the molecular mechanisms underlining the link between mutant LMNA and progeria are not clear. Because many reports cite an imbalance in cellular proteostasis as a driver of aging in the organism, it was interesting to understand whether deregulated nuclear proteostasis due to mutant LMNA is a cause of early aging. Indeed, we observed a fundamental link between the laminopathy‐associated E262K mutation of LMNA and the induction of nuclear proteotoxicity. Aggregates of LMNAE262K included essential chaperones, proteasomal proteins, etc., a phenomenon that not only disrupted protein folding but also thwarted the elimination of misfolded proteins and eventually generated further aggregates of nuclear proteins. Although nuclear protein aggregates were ubiquitinated, they were not efficiently degraded because of proteasome inactivation. Previously, aggregates of LMNAQ432X were shown to sequester the transcription factor SREBP1 (Yang et al., 2013). In addition, a high‐throughput screening of interactors of different LMNA mutants revealed enrichment of several transcription factors such as zinc‐finger transcription factors (e.g., ZNF69, ZNF569, ZNF440, etc.) and CREB (Dittmer et al., 2014), indicating a definitive role of LMNA mutants in triggering transcriptional deregulation. While aberrant transcription in laminopathies would result in qualitative and quantitative alteration of the proteome, we note that nuclear proteotoxicity represents an additional stress that could act at the levels of protein folding and degradation. The lethal effects of LMNAE262K extend beyond proteotoxicity to impairment of DNA damage repair pathways. Like chaperones and proteasomal proteins, aggregates of LMNAE262K sequester DNA damage repair proteins. Sequestration of DNA damage repair proteins by such aggregates would gradually reduce repair of spontaneously occurring DNA damage, leading to accumulation of extensive damaged DNA foci over time. Based on the observation that LMNAE262K aggregates do not colocalize with DNA damage sites, we rule out the possibility that aggregated LMNAE262K binds directly to DNA to cause the damage. Similar to LMNAE262K, condensed chromatin was previously observed in cells expressing LMNAS143P, although LMNAS143P aggregates also did not bind to chromatin (West et al., 2016). Thus, the modulation of chromatin structure and impairment of the DNA repair process would be a passive effect due to the proteotoxicity of the aggregates of mutant LMNA. In summary, we identified that E262K mutation in the rod‐2 domain of LMNA leads to early aging and that structural unfolding‐induced aggregation of the mutant LMNA causes severe proteotoxicity and failure of DNA damage repair, thus elucidating the mechanism of early aging due to an imbalance in nuclear proteostasis.
Details of all methods and materials are provided in the Supporting Information.
DKG: Conceptualization, Methodology, Resource acquisition, Investigation, Formal analysis, Validation, Data curation, Writing original draft. SP: Methodology, Investigation, Formal analysis, Writing original draft. JK: Methodology, Formal analysis. DY: Clinical evaluation. PR: Methodology. SN: Clinical evaluation. CGR: Methodology, Software, Validation, Writing original draft. AR: Resource acquisition, Formal analysis, Validation. KMG: Conceptualization, Resource acquisition, Clinical evaluation, Formal analysis, Validation, Project administration, Funding acquisition and overall supervision.
KMG is founder and director of Suma Genomics Private Limited, interested in rare disease diagnosis. Other authors report no conflict of interest.
This work was supported by the DBT/Wellcome Trust India Alliance grant [Grant number: IA/CRC/20/1/600002] awarded to KMG. Shruti Pande is supported by the Nurturing Clinical Scientist fellowship (Grant number: HRD/Head NCS‐2019‐03) from Indian Council for Medical Research, New Delhi.
The patient and his parents provided written informed consent to the study.
Click here for additional data file. |
PMC9649602 | 36088658 | Elissa Tjahjono,Daniel R. Kirienko,Natalia V. Kirienko | The emergent role of mitochondrial surveillance in cellular health | 11-09-2022 | aging,mitochondria,mitochondrial membrane transport proteins,mitophagy,physiological stress,reactive oxygen species,surveillance | Abstract Mitochondrial dysfunction is one of the primary causatives for many pathologies, including neurodegenerative diseases, cancer, metabolic disorders, and aging. Decline in mitochondrial functions leads to the loss of proteostasis, accumulation of ROS, and mitochondrial DNA damage, which further exacerbates mitochondrial deterioration in a vicious cycle. Surveillance mechanisms, in which mitochondrial functions are closely monitored for any sign of perturbations, exist to anticipate possible havoc within these multifunctional organelles with primitive origin. Various indicators of unhealthy mitochondria, including halted protein import, dissipated membrane potential, and increased loads of oxidative damage, are on the top of the lists for close monitoring. Recent research also indicates a possibility of reductive stress being monitored as part of a mitochondrial surveillance program. Upon detection of mitochondrial stress, multiple mitochondrial stress‐responsive pathways are activated to promote the transcription of numerous nuclear genes to ameliorate mitochondrial damage and restore compromised cellular functions. Co‐expression occurs through functionalization of transcription factors, allowing their binding to promoter elements to initiate transcription of target genes. This review provides a comprehensive summary of the intricacy of mitochondrial surveillance programs and highlights their roles in our cellular life. Ultimately, a better understanding of these surveillance mechanisms is expected to improve healthspan. | The emergent role of mitochondrial surveillance in cellular health
Mitochondrial dysfunction is one of the primary causatives for many pathologies, including neurodegenerative diseases, cancer, metabolic disorders, and aging. Decline in mitochondrial functions leads to the loss of proteostasis, accumulation of ROS, and mitochondrial DNA damage, which further exacerbates mitochondrial deterioration in a vicious cycle. Surveillance mechanisms, in which mitochondrial functions are closely monitored for any sign of perturbations, exist to anticipate possible havoc within these multifunctional organelles with primitive origin. Various indicators of unhealthy mitochondria, including halted protein import, dissipated membrane potential, and increased loads of oxidative damage, are on the top of the lists for close monitoring. Recent research also indicates a possibility of reductive stress being monitored as part of a mitochondrial surveillance program. Upon detection of mitochondrial stress, multiple mitochondrial stress‐responsive pathways are activated to promote the transcription of numerous nuclear genes to ameliorate mitochondrial damage and restore compromised cellular functions. Co‐expression occurs through functionalization of transcription factors, allowing their binding to promoter elements to initiate transcription of target genes. This review provides a comprehensive summary of the intricacy of mitochondrial surveillance programs and highlights their roles in our cellular life. Ultimately, a better understanding of these surveillance mechanisms is expected to improve healthspan.
The field of mitochondrial surveillance has burgeoned within the last 20 years as recognition of the contribution of mitochondrial dysfunction to chronic health issues has increased. In retrospect, it seems apparent that at least three factors would drive intense cellular scrutiny of mitochondria. First, they are responsible for the generation of much of the ATP in the cell, along with β‐oxidation of fatty acids, lipid metabolism, and amino acid catabolism (Spinelli & Haigis, 2018). They are also a central regulator of apoptosis. Disruption of any of these events is likely to be lethal for the cell. Second, mitochondrial biochemistry produces reactive oxygen species (ROS) (Nissanka & Moraes, 2017) and other toxic metabolic intermediates like methylmalonate or propionate (Fernandez‐Gomez et al., 2005). Finally, mitochondria originated as free‐living bacteria that became engulfed within other cells (Gray, 2012). Although these uneasy bedfellows eventually navigated their way toward a symbiotic relationship, it likely required them to steer a course through a transition period of close contact despite potential danger. Due to these three factors, mitochondria and cells underwent massive changes. First, most of the genes encoding the mitochondrial proteome migrated from this organelle to the nuclear genome (Anderson et al., 1981; Li et al., 2009; Taanman, 1999). This reduction of mitochondrial genome content was a major adaptation event during the transition from an independent bacterium into an endosymbiotic organelle. The smaller genome size provides replication or survival advantage for the organelle and gives the host additional flexibility in regulating their expression. However, this gene transfer limits the organelles' capability to live outside their hosts and the deleterious effect of mutant mitochondrial DNA (mtDNA) propagation. Second, mitochondrial signaling functions to recognize damage‐associated molecular patterns (mtDAMPs), including molecular motifs from their prokaryotic origin (e.g., N‐formyl peptides and mtDNA) and various mitochondrial metabolites such as cytochrome c, ATP, and cardiolipin, among others, released during organellar stress or damage (Grazioli & Pugin, 2018). These molecules induce inflammatory responses and ROS production (Hazeldine et al., 2015; Oka et al., 2012; Raoof et al., 2010). Finally, cells develop surveillance mechanisms that are the molecular equivalent of the Cold War‐era disarmament policy “trust, but verify”. Mitochondria are heavily monitored to limit potential damage and to preserve functions. Mitochondrial surveillance pathways orchestrate expression of tens to hundreds of genes via mitochondria‐to‐nuclear communication, also known as mitochondrial retrograde signaling. Curiously, despite the importance of mitochondrial quality control mechanisms, this biological phenomenon is relatively understudied. Although several pathways were found to respond to mitochondrial damage, only one, the mitochondrial unfolded protein response (UPRmt) (Haynes et al., 2013; Haynes & Ron, 2010; Naresh & Haynes, 2019), has been extensively studied, while the others have only recently been published. Many of these pathways monitor compromised mitochondrial protein import. Others monitor membrane potential, redox imbalance, mitochondrial bioenergetics, ceramide, mevalonate, and lipid biosynthesis. The next sections will discuss the regulations of these mitochondrial surveillance programs.
Typical modern mitochondrial genome consists only of a group of rRNAs, tRNAs, and a small handful of proteins involved in the electron transport chain (ETC) (Anderson et al., 1981). This is inconvenient, as ~ 99% of the proteins required for normal mitochondrial function are now encoded in the nuclear genome (Anderson et al., 1981; Li et al., 2009; Taanman, 1999), while other components are still in the mitochondrial genome. ETC complex subunits are required in proper stoichiometric ratios to avoid the assembly of non‐functional complexes, some of which may have dominant‐negative function (i.e., assembly of incomplete complexes can interact with substrates but not carry out function). As such, the cell requires careful coordination between the two genomes to ensure that this does not happen. Relocation of mitochondrial protein genes to the nucleus means that most of the proteins for mitochondrial function must be trafficked to and imported into mitochondria. These barrier‐crossing processes may require unfolding of proteins facilitated by mtHsp70 and refolding upon arrival in the matrix (Avendaño‐Monsalve et al., 2020; Bykov et al., 2020; Sato et al., 2019). Essentially all these materials utilize the well‐understood TOM (translocase of outer membrane) and TIM (translocase of inner membrane) complexes (Wasilewski et al., 2017; Wiedemann & Pfanner, 2017), which leverage the electrochemical proton gradient generated by ETC to facilitate import. This process is complicated and energetically intensive. Cellular stresses could limit import and stalled polypeptides will accumulate in the mitochondrial membrane, disturbing mitochondrial protein homeostasis (proteostasis). For all these reasons, mitochondrial import represents a valuable target used by several different surveillance systems (Figures 1, 2). Several of these surveillance systems restore proteostasis by reducing protein synthesis. These pathways include the UPRmt, the integrated stress response (ISR), and the yeast UPRam (UPRmt activated by mistargeting of proteins). Arguably, the most widely known of these is the UPRmt, an evolutionarily conserved response to aberrations in mitochondrial transport. Initially, the UPRmt was discovered due to the introduction of a mutated, improperly folding mitochondrial matrix protein, to specifically induce the mitochondrial chaperones Cpn60, Cpn10, and several other mitochondrial resident proteins (Martinus et al., 1996; Zhao et al., 2002). Deletion or depletion of mtDNA also had this effect. Promoter analysis identified a CHOP (C/EBP homology protein [TGCAATC])‐binding element in these genes, and a heterodimer of CHOP and C/EBP‐β was shown to be responsible for their regulation (Zhao et al., 2002). Further upstream, mitochondrial stress activates c‐Jun (through JNK signaling) to bind the AP‐1 element found in the promoter of CHOP and C/EBP‐β (Horibe & Hoogenraad, 2007). This pathway has been more comprehensively researched in Caenorhabditis elegans. One of the central actors in the nematode's UPRmt network is the bZIP‐family transcription factor ATFS‐1. Due to the presence of an N‐terminal mitochondrial localization sequence, ATFS‐1 is trafficked to mitochondria where it is imported and rapidly degraded by matrix‐resident proteases (Haynes et al., 2013; Haynes & Ron, 2010; Nargund et al., 2012). This process can be disrupted if mitochondrial chaperones (such as HSP‐6 and HSP‐60) are overwhelmed by excess of unfolded proteins (Yoneda, 2004). In this case, the resident proteases, particularly CLPP‐1, will begin to cleave the misfolded proteins, whose peptide fragments are then exported by HAF‐1, a mitochondrial ABC transporter. It is also speculated that charged peptides exported by HAF‐1 may affect mitochondrial membrane potential (Rolland et al., 2019). Combination of these events compromises import efficiency, leading to the accumulation of ATFS‐1 in the cytoplasm. This allows a secondary, weaker, nuclear localization signal in ATFS‐1 to reroute the transcription factor to the nucleus (Haynes et al., 2010; Haynes & Ron, 2010; Nargund et al., 2012). Once in the nucleus, ATFS‐1 works with DVE‐1, LIN‐65, MET‐2, and UBL‐5 to drive expression of mitochondrial chaperones and other repair machinery (~500 genes in total) to promote longevity and stress tolerance and restore proteostasis (Benedetti et al., 2006; Haynes et al., 2007; Haynes et al., 2010; Haynes et al., 2013). The UPRmt also modulates multiple metabolic enzymes, immune regulators, and additional transcription factors, including the key factor SKN‐1 (Wu et al., 2018). Importantly, ATF5 (the mammalian homolog of ATFS‐1) has been shown to regulate mammalian UPRmt in a similar manner to ATFS‐1, indicating significant functional conservation between worms and humans (Fiorese et al., 2016; Qureshi et al., 2017). One interesting question prompted by these studies is how UPRmt chaperones can be efficiently imported to resolve the stress conditions, when that failed process is what drives their production in the first place. Recent work by multiple laboratories has begun to address this question (Rolland et al., 2019; Shpilka et al., 2021; Xin et al., 2022) and indicates that the comparatively weak mitochondrial targeting sequence of ATFS‐1, at least compared to other proteins, causes its redirection. The UPRmt collaborates with the ISR pathway to reduce general protein translation rate and, consequently, the incoming load of mitochondrial proteins. The ISR (Harding et al., 1999; Harding et al., 2003) is an elaborate adaptive response that involves specialized kinases to promote the phosphorylation of eukaryotic translation initiation factor 2 (eIF2α). Phosphorylated eIF2α blocks the formation of the 43S pre‐initiation complex, inhibiting protein synthesis, but activating the transcription of certain effectors (such as ATF4) to promote cell survival (Harding et al., 2003). The ISR responds to many different stimuli, including ROS generation due to dysfunctional mitochondria which induces GCN‐2‐dependent eIF2α phosphorylation (Baker et al., 2012). The ISR functions in cooperation with the ATFS‐1‐mediated response to help restore protein folding. GCN‐2 activity is required for lifespan extension due to mild mitochondrial dysfunction (Baker et al., 2012). Interestingly, GCN‐2 is not the only kinase that responds to mitochondrial perturbation in the context of the ISR. A novel pathway, called the OMA1‐DELE1‐HRI pathway, was recently found in mammalian cells to relay mitochondrial stress to the cytosol (Guo et al., 2020). This pathway must first be activated to activate ATF4 of the ISR to handle mitochondrial stress (Guo et al., 2020). OMA1 is a protease that cleaves DELE1, an inner mitochondrial membrane‐associated protein, that is released to the cytosol and in turn interact with HRI, a kinase that will phosphorylate eIF2α, leading to the translation of ATF4 (Guo et al., 2020). In contrast to the GCN‐2‐dependent ISR pathway, the OMA1‐DELE1‐HRI pathway has opposing effects on cell survival depending on the type of mitochondrial stress. A different ISR pathway, mediated by DELE1 and HRI (but independent of OMA1), was also found to be activated due to stalled protein import during iron starvation (Sekine et al., 2022). DELE1 stabilization on the outer mitochondrial membrane allows for interaction with the kinase HRI, activating the ISR. This illustrates the utility of monitoring iron sensing via mitochondrial transport. Similarly, in Saccharomyces cerevisiae, disturbances in proteostasis activate a UPRmt‐related stress response pathway called the UPRam. The UPRam detects the accumulation of precursor proteins in the cytosol (Wrobel et al., 2015). Activation reduces protein synthesis to reduce the workload of the protein import system in an effort to restore proteostatic homeostasis. Global changes in transcription profiles to decrease mitochondrial protein load, such as repression of mitochondrial oxidative phosphorylation machinery gene expression, are also achieved by inactivating the HAP complex (CCAAT box‐containing proteins) (Boos et al., 2019). Unexpectedly, Tom70 was found to regulate both the transcription and import of mitochondrial proteins as well (Liu et al., 2022). Tom70 overexpression increases the abundance of mitochondrial proteins and mtDNA, suggesting that Tom70‐mediated mitochondrial protein import may regulate the biogenesis of mitochondrial proteins. This effect is relayed by multiple pathways. For example, knockout of the Forkhead family of transcription factor Fkh1/2 or the addition of the ROS scavenger N‐acetylcysteine partially reduce the effect of Tom70 overexpression (Liu et al., 2022). The UPRam also functions to increase proteasomal activity, and so are other import‐sensitive pathways in yeast, such as the mitochondrial compromised protein import response (mitoCPR) (Weidberg & Amon, 2018) and the novel mitochondrial protein translocation‐associated degradation (mitoTAD) pathway (Figure 2). Clogging the protein import system immediately activates Hsf1, possibly due to the depletion of the pool of free chaperones, and induces the transcription of Rpn4 (Boos et al., 2019), a regulator of the proteasome system of the UPRam. In turn, Rpn4 regulates the transcription of Pdr3 of the mitoCPR system (Weidberg & Amon, 2018). Accumulation of proteins in the TOM/TIM channel activates Pdr3, which initiates the transcription of mitoCPR target genes Cis1 and Msp1, among others. This role is specific to Pdr3, which additionally functions somewhat redundantly with Pdr1 in the multidrug response to various xenobiotic toxins (Moye‐Rowley, 2003). Cis1 interacts with Tom70 as a scaffold to recruit Msp1 and the proteasome. Msp1 is an AAA ATPase that removes the stuck proteins, allowing their proteasomal degradation (Basch et al., 2020; Weidberg & Amon, 2018). It is worth noting that disruptions to phospholipid biogenesis can also trigger mitochondrial import stress and activate this pathway (Sam et al., 2021). Meanwhile, the mitoTAD pathway directly monitors the TOM channel for clogging (Mårtensson et al., 2019). Upon detection of clogging, this pathway imports Ubx2 into the mitochondria, which recruits Cdc48 (an AAA ATPase) to remove precursor proteins clogged in the import channel, ensuring that mitochondrial protein import continues at full capacity. More interestingly, the accumulation of protein aggregates in the mitochondria also activates an early branch of the UPRmt in yeast that is mediated by the transcription factor Rox1 (Poveda‐Huertes et al., 2020). This pathway is activated very early in the response, with the apparent goal of maintaining mitochondrial membrane potential, protein import, and protein translation to promote cell survival. In contrast to ATFS‐1, Rox1 is normally a nuclear transcription factor. When precursor protein aggregation is recognized, Rox1 relocates to the mitochondrial matrix, where it regulates mtDNA expression. This is thought to circumvent the need for processing, increasing the speed of the response. In contrary, when the damage is irreparable, the cells activate pathways design to recycle mitochondria and/or limit damage. One of these pathways is mitophagy (mitochondrial autophagy, a clearance pathway for damaged mitochondria) (Pickrell & Youle, 2015). The serine–threonine kinase PINK‐1, a well‐known regulator of mitophagy, is likewise sensitive to mitochondrial import disturbance. Much like ATFS‐1, PINK‐1 is constitutively expressed, trafficked to mitochondria, and rapidly degraded in both C. elegans and mammals. Unlike ATFS‐1, PINK‐1 stays at mitochondria when import is compromised, whether by disruptions of the mitochondrial membrane potential or blockage of the TOM/TIM complex. PINK‐1 accumulates on the outside of the mitochondrial membrane, dimerizes and cross‐phosphorylates, activating the protein and allowing it to phosphorylate its targets, such as the E3 ubiquitin ligase Parkin (Kane et al., 2014; Kazlauskaite et al., 2014). This triggers polyubiquitination of its substrates, allowing them to be recognized as targets for mitophagy (Bertolin et al., 2013; Mouton‐Liger et al., 2017; Narendra et al., 2008; Narendra et al., 2010; Pickrell & Youle, 2015). Interestingly, when alterations (e.g., mutated PINK1 or the loss of Tom7) were introduced that allowed PINK1 to be imported into mitochondria despite the loss of membrane potential, the kinase is cleaved by OMA1 (Sekine et al., 2019), the same protease that is involved in the ISR. Upon cleavage, PINK1 is degraded by the proteasome. OMA1 suppression, however, cancels PINK1 import into the mitochondria and activates mitophagy, and therefore is considered as a potential therapy to stimulate mitophagy for neurodegenerative diseases. In yeast, a novel mitochondria‐dependent cell death program, called the mPOS (mitochondrial precursor over‐accumulation stress) (Wang & Chen, 2015), is also activated by defects in mitochondrial import; specifically, the accumulation of precursor proteins in the cytosol. This pathway can also be activated by increased heteroplasmy, protein misfolding, or reduced mitochondrial membrane potential (Coyne & Chen, 2018). Several genes were identified to suppress mPOS, including portions of the TOR pathway, mRNA turnover, reduced protein translation, and tRNA methylation (Wang & Chen, 2015). Like UPRmt and UPRam, the suppressors of mPOS are targeted toward recovery of homeostasis, rather than directly activating cell death pathways. The heavy reliance mitochondria have on protein import requires close observation and immediate response to possible dysfunction, especially as mitochondrial precursor proteins are prone to aggregation (Nowicka et al., 2021). In mammals and C. elegans, the UPRmt plays a prominent role to ensure that mitochondrial proteostasis is restored. In yeast, multiple pathways have been identified within the last decade for resolving problems in protein import. The UPRam and mitoCPR work harmoniously with the proteasomal system to remove problematic precursor proteins from the clogged import systems. Recently discovered pathways, such as the OMA1‐DELE1‐HRI, iron‐sensing DELE1‐HRI, early UPRmt, and mitoTAD pathways, represent the wide variety of surveillance targets in the mitochondrial protein import systems. It remains to be determined whether these pathways also activate mitophagy and programmed cell death pathways like their more well‐understood cousins, but it is an area of considerable interest.
ATP generation in mitochondria involving the ETC comes with a downside: The system is leaky, allowing electrons to escape from different carriers (e.g., NADH, FADH2, and coenzyme Q) and reduce O2 into superoxide (O2 •−) (Quinlan et al., 2013), making mitochondria the largest single source (~90%) of ROS in the cell (Nissanka & Moraes, 2017). Once generated, ROS can damage most biomacromolecules, including proteins, lipids, and nucleic acids (Checa & Aran, 2020). Predictions of intracellular ROS were made as early as 1956 (Harman, 1956) and were supported by the discovery of superoxide dismutase (McCord & Fridovich, 1969), which converts superoxide into hydrogen peroxide. Fascinatingly, increased mitochondrial superoxide due to mitochondrial ETC knockdown or mitochondrial superoxide dismutase deletion increases lifespan in C. elegans (Schaar et al., 2015; Van Raamsdonk & Hekimi, 2009). Similar effects were also observed in mice (Lapointe et al., 2012) and yeast (Pan et al., 2011). This indicates that ROS are not merely a toxic byproduct that needs to be eliminated. Instead, the production of mitochondrial ROS is critical for cell signaling and immune responses (Moldogazieva et al., 2018; Pinegin et al., 2018). Mitochondrial ROS are known to activate the Nrf2 oxidative stress response pathway (Kasai et al., 2020) and the TOR pathway (Schieber & Chandel, 2014), a nutrient‐sensing pathway for cell growth and proliferation. Metabolic adaptations occurring due to the activation of these pathways are implicated in lifespan extension. The Nrf2 pathway may provide its beneficial effects by maintaining mitochondrial homeostasis, such as the expression of antioxidant and mitochondrial quality control genes. Similarly, TOR signaling senses mitochondrial ROS released by transient exposure to hypoxia, leading to the expression of detoxification genes, such as glutathione S‐transferases (Schieber & Chandel, 2014). Further, increased hydrogen peroxide production by mitochondria is known to stabilize the hypoxia‐inducible transcription factors (HIF) during hypoxia. This response is regulated by HIF‐1 and AMP‐activated protein kinase (AMPK) in a feedback regulation manner. HIF regulates transcription of genes encoding cell cycle regulators, innate immune effectors, and other key factors (Hamanaka & Chandel, 2010; Hwang et al., 2014). The modulation of ROS levels is known to determine physiological outcomes. For example, low levels of ROS can activate the production of antioxidants to repair homeostasis, a process often known as mitochondrial hormesis (mitohormesis) (Hekimi et al., 2011; Ristow & Zarse, 2010). Elevated mitochondrial ROS production, known as a respiratory or oxidative burst, is also used as a cellular defense mechanism after pathogen engulfment or invasion. This response has both bactericidal (West et al., 2011) and long‐range signaling properties, for example, to promote wound repair (Xu & Chisholm, 2014), but high levels of ROS are detrimental to cellular survival.
The biphasic effect of mitochondrial ROS suggests that cells possess surveillance systems that track cellular redox status to provide protection for the cell. Cells maintain pools of redox pairs (e.g., NADH/NAD+ or GSH/GSSG) to help mitigate ROS, but excessive ROS depletes the reductive member of these pairs, a condition called oxidative stress. Depletion of these pools causes accumulation of ROS and damage to mtDNA and proteins, accelerating mitochondrial dysfunction. Mitochondrial ROS may promote calcium release from the endoplasmic reticulum (ER) and trigger additional ROS production from surrounding mitochondria (Bertero & Maack, 2018). Ultimately, the overload of oxidative agents can trigger the opening of the mitochondrial permeability transition pore, energetic collapse, cytochrome c release, and cell death (Jacobson & Duchen, 2002). Although oxidative stress has been a focus of many studies, it is not the only consequence of mitochondrial disruption. An abnormal buildup of reducing equivalents, especially NADH, NADPH, or GSH, leads to a state called reductive stress. This can occur when Complex I of the ETC is disrupted, preventing NADH oxidation. Paradoxically, reductive stress also leads to production of ROS, as molecular oxygen is reduced (yielding superoxide) when more typical electron acceptors are absent (Korge et al., 2015; Zhang et al., 2012). Production of ROS from either oxidative or reductive stresses is dangerous to cells (Brewer et al., 2013; Xiao & Loscalzo, 2020). Moderate induction of reductive stress, however, drives mitochondrial hormesis to prepare for defense against oxidative stress (Singh et al., 2015; Spanidis et al., 2018). Mitochondrial surveillance pathways that are responsive to ROS and/or redox stress have received increasing attention over the last decade (Figure 3). One of the first ROS‐responsive pathways discovered was the mitochondrial‐associated protein degradation (MAD) pathway, first identified in yeast (Heo et al., 2010). Upon detection of oxidative stress, Vms1, which is highly conserved among eukaryotes, translocates from the cytosol to the outer mitochondrial membrane. Once there, it recruits and interacts with the ribosomal quality control complex (comprised of Rqc1, Rqc2, Ltn1, Cdc48, Ufd1, and Npl4) (Verma et al., 2018). Vms1 also binds the 60S ribosome and facilitates release of stalled translation of mitochondrial proteins (Izawa et al., 2017). The cytosolic 26S proteasome was speculated to be redirected to the mitochondria to help with the degradation of these proteins (Segref et al., 2014). The importance of this pathway is shown in Vms1 loss‐of‐function mutants, which display reduced cellular viability and mitochondrial function and increased sensitivity to oxidative stress (Heo et al., 2010). C. elegans UPRmt has also been reported to respond to ETC disruptions that cause ROS (Runkel et al., 2013). Interestingly, this response is different from the regular UPRmt, as it involves neither the transporter HAF‐1 nor peptide efflux. Induction of this response can be suppressed by the mutation of over 50 genes, including regulatory subunits of the proteasome, ribosomal components, chaperones, and the transcription factors ATFS‐1 and ELT‐2. Most of these genes are also associated with the cellular surveillance activated detoxification and defenses (cSADD) program that monitors disruption of basic cellular functions (Melo & Ruvkun, 2012; Runkel et al., 2013). The authors speculated that the repression by cSADD may indicate that cells can temporarily repress the UPRmt in order to focus on resolving more immediate threats or to temporarily increase local ROS levels for an “active burst” immune response as part of a defense strategy (Runkel et al., 2013). Another key cellular response activated by ROS is the ESRE network. Initially identified by its activation after acute ethanol exposure, the ESRE pathway was named for an 11‐nucleotide motif (TCTGCGTCTCT), known as the ethanol and stress response element (ESRE), that is present in the promoter region of responsive genes (Kwon et al., 2004). Interestingly, the ESRE motif has since then been independently discovered at least seven times in studies of stress responses in C. elegans and in mammals, and has been shown to be activated in response to hypoxia, ethanol, heat, and oxidative stress (Gaudet et al., 2004; GuhaThakurta et al., 2002; Kirienko & Fay, 2010; Kwon et al., 2004; Munkácsy et al., 2016; Pignataro et al., 2007; Ruvinsky et al., 2007). In many of these instances, the removal or mutation of ESRE motif(s) from the promoter of responsive genes abolishes their expression (Gaudet et al., 2004; GuhaThakurta et al., 2002; Kwon et al., 2004; Pignataro et al., 2007; Tjahjono & Kirienko, 2017). Later work by our group showed that the ESRE network also responds to mitochondrial damage inflicted by the removal of iron by either a bacterial siderophore or by a chemical iron chelator (Kang et al., 2018; Tjahjono & Kirienko, 2017). We anticipate that activation of the ESRE network in each of these cases results from mitochondrial damage triggering the production of superoxide anions. It is worth nothing that exposure to a broad variety of poisons that damage the ETC activates the ESRE response. One example is rotenone, which prevents electron transfer from NADH and causes it to accumulate, inducing reductive stress. As might be expected, adding N‐acetylcysteine (a well‐known antioxidant) increases the reductive stress and amplifies the ESRE response (Tjahjono et al., 2020). The ESRE network is not restricted to nematodes, but is broadly evolutionarily conserved (Kirienko & Fay, 2010). Importantly, genes regulated by the ESRE motif in C. elegans typically retain the motif across large evolutionary distances (i.e., between C. elegans and humans), are often orthologous between humans and nematodes, and frequently are involved in stress responses (Kirienko & Fay, 2010). The ESRE motifs are also found in the promoter of atfs‐1 and bec‐1/Beclin, regulators of UPRmt and autophagy pathways in C. elegans, respectively. As was seen for other genes, deletion of the ESRE motif from the promoter of atfs‐1 reduced the expression of this gene (Tjahjono et al., 2020), affirming ESRE's role in the regulation of important pro‐mitochondrial health pathways. As such, considerable attention has been given to understanding how superoxide is detected and how this drives transcriptional activity. The most obvious explanation is that one (or more) transcription factor(s) bind to the ESRE site, which is upstream of predicted transcriptional start sites. However, attempts to identify candidate proteins (via targeted RNAi screens or biochemical purification of transcription factors) have thus far been unsuccessful ([Kuzmanov et al., 2014], N. V. Kirienko, personal communication). Despite this, we have shown that at least four C/EBP bZip family transcription factors (ZIP‐2, ZIP‐4/CEBPβ, CEBP‐1, and CEBP‐2/CEBPγ) play roles in ESRE gene regulation (Tjahjono & Kirienko, 2017). The nematode‐specific Zn‐finger transcription factor SLR‐2 also regulates ESRE gene expression (Kirienko & Fay, 2010), but ESRE gene activation was seen in strains carrying mutations predicted to have strong loss‐of‐function alleles in all of these transcription factors, suggesting that ESRE expression only partially depends on any of these genes. To date, no single transcription factor has been shown to be indispensable for ESRE activity. ESRE gene expression also requires the PBAF chromatin remodeling complex (Kuzmanov et al., 2014), which recognizes highly acetylated chromatin (Ho et al., 2019). Elements of the PBAF complex (SWSN‐4/BRG1/BRM and SWSN‐1/BAF170/BAF155) appear to bind to the promoters of ESRE‐containing genes, even in the absence of stress (Riedel et al., 2013), while other elements (SWSN‐7 and PBRM‐1) are stress‐inducible (Kuzmanov et al., 2014). Overexpression of the stress‐inducible portions of the PBAF complex increased expression of ESRE genes, even in the absence of stress, and increased stress resistance. Interestingly, the PBAF complex was only recruited to intact ESRE sites; removal of the ESRE motif abolished binding by the nucleosome remodeling complex, indicating that the site itself is necessary for recruitment. Another recent study identified box C/D snoRNA (small nucleolar RNA) core proteins (snoRNPs) as ESRE interactors (Tjahjono et al., 2022). Box C/D snoRNPs are comprised of FIB‐1/Fibrillarin (the catalytic methyltransferase), NOL‐56/Nop56, NOL‐58/Nop58, and M28.5/SNU13. Box C/D snoRNPs 2’‐O‐methylate RNAs, especially rRNA, in a sequence‐dependent fashion, using snoRNAs for targeting and sequence recognition (Ojha et al., 2020). Multiple members of this protein complex were identified as directly binding ESRE element in an oligo pull‐down experiment. Based on follow‐up experiments, authors proposed a model where box C/D snoRNP machinery may function as a “switch” of the cell's activity between mitochondrial surveillance and innate immune activation, as mutations in these genes resulted in decreased mitochondrial function and upregulation of innate immune pathways (Tjahjono et al., 2022). Another factor involved in ESRE gene expression is an enzyme called JMJC‐1/RIOX1/NO66 (Kirienko & Fay, 2010). JMJC‐1/RIOX1/NO66 is a member of the Jumonji family of proteins, which contains over 30 members, most of which have demonstrated histone demethylase activity (Franci et al., 2014). The molecular function of RIOX1 is less clear, but has been very capably reviewed (Bundred et al., 2018). It has been convincingly demonstrated to transfer a hydroxyl group to a histidine in the ribosomal protein Rpl8 (Ge et al., 2012; Williams et al., 2014) and there is some evidence that it may have histone demethylase activity (Bundred et al., 2018; Sinha et al., 2010; Zhou et al., 2012), although this activity is controversial as it could not be recapitulated by other groups (Wang et al., 2015; Williams et al., 2014). While demethylation has an obvious mechanism for regulating gene expression (i.e., the conversion of chromatin to a more readable state), ribosomal modification is less clear. One careful structural study indicates that the transfer helps stabilize the local conformation of the 28S rRNA and the peptidyl transfer center, and has been proposed to enable translational efficacy (Yanshina et al., 2015). Parsing out these functions in vivo is difficult as both functions utilize the same chemistry, coordinated by the same amino acid residues. This remains an active area of study. Disrupting the mitochondrial ETC has recently been shown to activate several other responses as well. For example, RNAi knockdown of cox‐6c, a component of Complex IV, caused dephosphorylation of HSF‐1 by LET‐92 (Williams et al., 2020). Interestingly, overexpression of let‐92 supported proteostatic health and limited aggregation‐induced paralysis in worms carrying a glutamate‐repeat protein. Dephosphorylated HSF‐1, at least in these conditions, primarily drove the expression of small, ATP‐independent heat shock proteins that are thought to sequester misfolded proteins while waiting for an ATP‐dependent chaperone to refold them. HSF‐1 dephosphorylated in this fashion also upregulates two HSP70 family members, HSP‐70 and HSP‐70B, that have this function, even though their function is likely to be limited while the ETC is disrupted. The authors hypothesized that this upregulation poises the system to recover quickly once ATP has begun to be produced (Williams et al., 2020). A subsequent study showed that exposing C. elegans to a variety of compounds (including acivicin, cadmium, or acetaminophen) disrupted the balance of cellular redox compounds by depleting the pool of thiols (Gusarov et al., 2021). This study also demonstrated that excess consumption of antioxidants, such as N‐acetylcysteine, can similarly disrupt cellular redox balance and, at least in C. elegans, shorten lifespan. Another important mechanism of maintaining ETC function is turnover of its damaged components by matrix‐resident proteases, especially the AAA protease SPG‐7/SPG7 (Arlt et al., 1996). RNAi‐mediated disruption of spg‐7 has been clearly shown to activate several mitochondrial surveillance pathways (Munkácsy et al., 2016; Yoneda, 2004), including the UPRmt and, if atfs‐1 is compromised, the ESRE network as a compensatory mechanism (Tjahjono et al., 2020). Interestingly, atfs‐1 mutants also showed upregulation of a second pathway, which was dependent upon a DLK‐1/SEK‐3/PMK‐3 MAPK pathway (Munkácsy et al., 2016). Using a transcriptional reporter for tbb‐6, one of the most highly upregulated genes after disruption of spg‐7, they showed that the MAPKmt system is activated by a variety of mitochondrial bioenergetic perturbations. Disruption of the MAD pathway reduced expression of the Ptbb‐6::GFP reporter, indicating that the MAPKmt system may be activated downstream of the MAD pathway, possibly through cytosolic signaling that was stabilized upon reduction of the ubiquitin/proteosome system activity. Much like the ESRE network, expression of MAPKmt target genes appears to at least partially depend on C/EBP family transcription factors; removal of C/EBP‐like binding motifs in the tbb‐6 promoter abolished induction of the reporter (Munkácsy et al., 2016). As the core component of the mitochondria, it is far from surprising that multiple pathways are dedicated to surveilling the integrity of the ETC. The MAD, the UPRmt, and the ESRE pathways directly respond to shifts in redox balance, while the others may monitor other damaged sites. Careful modulation of ROS production and redox conditions is crucial to potentiate ROS as signaling molecules. As ROS have myriad functions in cell signaling, discovering novel signaling pathways dependent on ROS and redox conditions may help to leverage mitohormesis to improve organismal fitness.
Mitochondrial functions are tightly linked with lipid metabolism and signaling. For example, most β‐oxidation of fatty acids takes place in the matrix. Other mitochondrial metabolic activities also generate signaling lipids that play roles in mitophagy, autophagy, and apoptosis (Crimi & Esposti, 2011; Dall'Armi et al., 2013; Nielson & Rutter, 2018). Unsurprisingly, a genome‐wide screen identified RNAi of lipid biosynthesis genes to trigger the activation of the mitochondrial chaperone HSP‐6 (Liu et al., 2014). This screen also identified a wide variety of genes, including known components of the UPRmt, nuclear pore and transport machinery, and kinases and phosphatases. Most of these knockdowns also activated reporters for xenobiotic detoxification and pathogen response, which led them to conclude that C. elegans interprets mitochondrial dysfunction as a xenobiotic exposure or a pathogen attack. This is consistent with their findings that a wide range of bacteria encountered by C. elegans in their natural environment damage host mitochondria. Their data also indicated that mitochondrial surveillance in these circumstances required SPTL‐1, a key protein in sphingolipid biosynthesis, and the mevalonate biosynthesis protein HMGS‐1. Moreover, they showed that supplementation with a 24‐carbon ceramide, a downstream product of SPTL‐1, rescued mitochondrial surveillance. Previous reports indicated that loss‐of‐function of mutation of HYL‐2, the protein that synthesizes 24‐ and 26‐carbon ceramides, triggered autophagy in C. elegans (Mosbech et al., 2013), possibly due to failures in mitochondrial surveillance. It should be noted that HSP‐6, the mitochondrial chaperone used in that study, occupies a rather unique space in C. elegans. For example, a hypomorphic allele of hsp‐6 activates a xenobiotic response through MED‐15 and NHR‐45, two proteins associated with lipid metabolism (Mao et al., 2019). Additionally, Kim et al. observed that disruption of hsp‐6, which activates the UPRmt, also activates the heat shock response in the cytoplasm, a phenomenon they named the mitochondrial‐to‐cytosolic stress response, or MCSR (Kim et al., 2016; Figure 4). In contrast, RNAi targeting other known mitochondrial chaperones (e.g., hsp‐60 and dnj‐10) did not have a similar effect. The reason for this difference remains unknown. Induction of the MCSR after hsp‐6(RNAi) was blocked if either pod‐2 or fasn‐1, two genes early in the biosynthetic pathway for saturated fatty acids, were knocked down with hsp‐6. Lipid profiling demonstrated that the MCSR was associated with decreased ceramide biosynthesis and increased cardiolipin, two lipid groups whose concentrations are often inversely correlated. Interestingly, they observed that merely feeding exogenous cardiolipin to worms was sufficient to trigger mild activation of the heat shock response and activate HSP‐6, and that cris‐1(RNAi), which knocks down the cardiolipin synthase gene, blocked MCSR. Both observations further link lipid biology to this stress response. This effect may have been indirect, however, as their work indicated that the absence of ceramide may actually be more important to the activation of the MCSR than the presence of cardiolipin (Kim et al., 2016). While studying the MCSR, they observed that hsp‐6(RNAi) also triggered the accumulation of lipid droplets (Papsdorf & Brunet, 2019). The formation of these bodies has itself been described as a stress response, a mechanism to minimize lipotoxicity. For example, increased autophagic activity in mammalian cells, especially during starvation, upregulates lipid metabolism. This results in the production of large pools of acylcarnitines, a class of lipids that are responsible for the transport of fatty acids into mitochondria for β‐oxidation. However, high concentrations of acylcarnitines have been linked with lipotoxicity and mitochondrial dysfunction (McCoin et al., 2015; Son et al., 2010; Wajner & Amaral, 2015). This effect can be limited by the action of the lipid metabolism gene, DGAT1, which converts acylcarnitines into more easily‐stored triglycerides, which are then packed into lipid droplets (Nguyen et al., 2017). DGAT1 is upregulated during autophagy, and its absence leads to considerable mortality during starvation when autophagy is activated (Nguyen et al., 2017). Although these data clearly indicate a relationship between lipid metabolism and mitochondrial surveillance, it should be noted that these data present two possibilities. First, it is possible that altered lipid metabolism directly disrupts homeostasis for mitochondria, the ER, or some other organelles. For example, fatty acids were shown to alter mitochondrial membranes permeability and inhibit ETC complexes (Penzo et al., 2004; Schönfeld & Wojtczak, 2008). Inappropriate lipid metabolism may also physically disrupt organelles by acting as membrane detergents, or the failed production of lipid droplets may prevent the removal of inappropriate fatty acid species from the ER or other organelles (Roberts & Olzmann, 2020). A second alternative is that one or more lipids serves as a signal for mitochondrial health. This signal could take the form of either an “all clear” signal that stops being produced during stress conditions or a danger signal that is produced, or accumulates, during mitochondrial disruption. For example, mitochondrial damage has been shown to trigger the relocalization of cardiolipin from the inner mitochondrial membrane to the outer membrane, allowing the phospholipid to facilitate recognition of mitochondria as an autophagosomal target (Chu et al., 2013). In any case, it is increasingly clear that the roles of lipids in mitochondrial surveillance demand further attention.
Artificial perturbation of the mitochondrial environment, for example, by knocking down resident proteins or chemically inhibiting ETC complexes, led to the discovery of many of the mitochondrial surveillance mechanisms described above. While studies under these conditions have provided mechanistic insight, a discussion of how these pathways function in more natural contexts, such as aging and immunity, is warranted. Artificial perturbations that activate mitochondrial surveillance generally recapitulate the environment in aging cells. For example, accumulation of mitochondrial ROS and downstream oxidative stress‐modified molecules are common biomarkers of aging and aging‐related diseases (Frijhoff et al., 2015). Furthermore, declines in mitochondrial quality have been increasingly recognized to contribute to aging and the development of aging‐associated and other chronic diseases, including cardiovascular diseases, diabetes, and obesity. MtDNA deletions and rearrangements are increased among elderly individuals and are also primary cause of the Kearns–Sayre syndrome, POLG‐related disorders, and multiple sclerosis in which symptoms resemble premature aging (Corral‐Debrinski et al., 1992; Poulton et al., 1994; Rygiel et al., 2016). In aging heart, oxidative phosphorylation and beta‐oxidation are reduced, leading to reduced ATP production but increased lipid and ROS (Lesnefsky et al., 2016). These abnormalities result in inflammation and degenerated functions of affected tissues, which are the primary hallmarks of aging. As such, it is important to understand the substantial roles of the mitochondrial surveillance pathways in aging and immunity. The study of mitochondrial surveillance resulted in a fine observation that careful modulation of mitochondrial perturbation could be beneficial to the cell. Research in C. elegans repeatedly showed that moderate mitochondrial ETC inhibition extends lifespan (Dillin et al., 2002; Lee et al., 2003; Rea et al., 2007). Prolonged perturbation, however, causes cell damage or death and the release of mtDAMPs that rapidly triggers immune responses. Thus, mitochondrial dysfunction is one of the hormetic phenomena in aging (López‐Otín et al., 2013). We have now understood that this antagonistic characteristic of mitochondrial perturbation outcomes is determined by mitochondrial quality control mechanisms. Mitochondrial surveillance pathways constantly monitor mitochondria status indicators to prepare for appropriate response upon detection of abnormality. For example, the inhibition of mitochondrial ETC increases ROS production, leading to the induction of various mitochondrial surveillance pathways that further activate detoxification systems and stress responses. The induction of surveillance pathways enables early detection of damage and makes appropriate decision for homeostasis restoration effort. When these efforts seem to be futile, mitochondria may undergo self‐degradation to limit the propagation of sick mitochondria and to recycle their components. The importance of mitochondrial surveillance pathways in longevity and aging‐related diseases is evident as the loss of these pathways often results in repressed lifespan extension phenotype and/or reduced survival during stress. For example, ATFS‐1 of the UPRmt and PMK‐3 of the MAPKmt pathway are required for the long lifespan observed in C. elegans Mit mutants (Munkácsy et al., 2016; Wu et al., 2018). The UPRmt has also recently been linked to mitochondrial recovery upon starvation (Naresh et al., 2022) and its activation restored mitochondrial protein homeostasis in multiple Parkinson's disease models (Hu et al., 2021). The induction of MCSR and mitophagy improve proteostasis as shown in the accumulation of fewer aggregates in a Huntington's disease model in both C. elegans and mammalian cells (Kim et al., 2016; Tjahjono et al., 2021). The PINK‐1/Parkin mitophagy pathway has long been implicated in neurodegenerative diseases, especially Parkinson's disease (extensive review in [Mouton‐Liger et al., 2017]). These surveillance pathways have equivalently profound roles in innate immune activation. The UPRmt plays a protective role in response to pathogen exposure as expression of innate immune genes (e.g., lysozymes and anti‐microbial peptides) is orchestrated by ATFS‐1 (Pellegrino et al., 2014). Similarly, knockdown of genes belonging to the ESRE network reduce survival in a pyoverdine‐dependent Pseudomonas aeruginosa pathogenesis assay (Tjahjono & Kirienko, 2017). Finally, activation of the p38 MAPK immune pathway due to rotenone exposure confers neuroprotection through the activation of mitophagy, establishing a relationship between the two (Chikka et al., 2016). It is also important to note that the surveillance programs do not act individually. Repression of the MAPKmt by the UPRmt and repression of the UPRmt by cSADDs, presence of ESRE motif in the atfs‐1 promoter, for example, suggest extensive crosstalk and illustrate the complexity of stress and surveillance regulations. These interactions extend beyond mitochondria; for instance, the canonical p38 MAPK immune signaling pathway is also involved in the increased resistance of the Mit mutants to pathogens (Campos et al., 2021). As with all surveillance pathways, the protective effects and lifespan extension occurred with mild induction of these retrograde response systems, while the opposite occurred with chronic pathway activation (Hsu et al., 2003; Labunskyy et al., 2014; Rea et al., 2007). Finally, many questions remain on the roles of mitochondrial surveillance in promoting healthy aging and immunity. For example, how do mitochondrial surveillance pathways ameliorate proteostatic defects in the context of degenerative diseases? How does impaired mitochondrial surveillance lead to oncogenesis? Therefore, future research regarding the roles of signaling molecules, pathway modulations, and crosstalk in surveillance systems is necessary. This is not only crucial for understanding cell biology and aging regulations but may also have a huge potential for the development of novel therapeutic systems for healthy aging.
ET involved in investigation, writing, review and editing, and visualization. DRK involved in writing, review and editing, and visualization. NVK involved in writing, review and editing, supervision, and funding acquisition.
The authors have declared that no competing interests exist. |
PMC9649603 | 36181246 | Armen Yerevanian,Luke M. Murphy,Sinclair Emans,Yifei Zhou,Fasih M. Ahsan,Daniel Baker,Sainan Li,Adebanjo Adedoja,Lucydalila Cedillo,Nicole L. Stuhr,Einstein Gnanatheepam,Khoi Dao,Mohit Jain,Sean P. Curran,Irene Georgakoudi,Alexander A. Soukas | Riboflavin depletion promotes longevity and metabolic hormesis in Caenorhabditis elegans | 30-09-2022 | AMPK,C. elegans,dietary restriction,FOXO,longevity,rft‐1 riboflavin transporter,riboflavin,UPRmt | Abstract Riboflavin is an essential cofactor in many enzymatic processes and in the production of flavin adenine dinucleotide (FAD). Here, we report that the partial depletion of riboflavin through knockdown of the C. elegans riboflavin transporter 1 (rft‐1) promotes metabolic health by reducing intracellular flavin concentrations. Knockdown of rft‐1 significantly increases lifespan in a manner dependent upon AMP‐activated protein kinase (AMPK)/aak‐2, the mitochondrial unfolded protein response, and FOXO/daf‐16. Riboflavin depletion promotes altered energetic and redox states and increases adiposity, independent of lifespan genetic dependencies. Riboflavin‐depleted animals also exhibit the activation of caloric restriction reporters without any reduction in caloric intake. Our findings indicate that riboflavin depletion activates an integrated hormetic response that promotes lifespan and healthspan in C. elegans. | Riboflavin depletion promotes longevity and metabolic hormesis in Caenorhabditis elegans
Riboflavin is an essential cofactor in many enzymatic processes and in the production of flavin adenine dinucleotide (FAD). Here, we report that the partial depletion of riboflavin through knockdown of the C. elegans riboflavin transporter 1 (rft‐1) promotes metabolic health by reducing intracellular flavin concentrations. Knockdown of rft‐1 significantly increases lifespan in a manner dependent upon AMP‐activated protein kinase (AMPK)/aak‐2, the mitochondrial unfolded protein response, and FOXO/daf‐16. Riboflavin depletion promotes altered energetic and redox states and increases adiposity, independent of lifespan genetic dependencies. Riboflavin‐depleted animals also exhibit the activation of caloric restriction reporters without any reduction in caloric intake. Our findings indicate that riboflavin depletion activates an integrated hormetic response that promotes lifespan and healthspan in C. elegans.
Abbreviations AMPK AMP‐activated protein kinase ATP adenosine tri‐phosphate ETC electron transport chain EV empty vector FAD flavin adenine dinucleotide FLIM fluorescence lifetime imaging FMN flavin mononucleotide FOXA forkhead box A FOXO forkhead box Group O GC/MS gas chromatography/mass spectrometry LB Luria Broth LC/MS liquid chromatography/mass spectrometry NAD(P)H nicotinamide adenine dinucleotide phosphate NGM nematode growth medium QPCR quantitative PCR RNAi RNA interference SRS stimulated Raman scattering TOR target of rapamycin TORC1 TOR complex 1 TORC2 TOR complex 2 TPEF two‐photon excited fluorescence UPRmt mitochondrial unfolded protein response
Healthy mitochondrial function requires the coordination of multiple cellular inputs including sufficient energetic substrates, amino acids, and micronutrients. Vitamin cofactors are key to metabolic processes such as the citric acid cycle, electron transport chain, and energy shuttling into the cytosol. One family of vitamins that participate in mitochondrial physiology are the flavins (Mansoorabadi et al., 2007). The flavin co‐factors include flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) and are essential for redox chemistry and electron shuttling (Massey, 1995). FAD is classically known to serve as an electron acceptor in the conversion of succinate to fumarate by succinate dehydrogenase in the citric acid cycle, as well as an electron donor to complex II of the electron transport chain. The flavins are also cofactors for multiple enzyme classes including the oxidoreductases and the fatty acid dehydrogenases (Lienhart et al., 2013). They are derived from riboflavin, a water soluble ribitol derivative also known as vitamin B2. Riboflavin is an essential nutrient for all animals and must be acquired either from food sources or from commensal gut flora (Powers, 2003). The animal kingdom has evolved specific transporters to import riboflavin from the gut lumen and to transport them intracellularly (Moriyama, 2011). In humans and mice, three isoforms of these transporters are expressed by three distinct genes: Slc52A1, Slc52A2, and Slc52A3 (Subramanian et al., 2011; Yamamoto et al., 2009; Yonezawa et al., 2008). Disruption in the function of these transporters is known to induce pathology through cellular riboflavin depletion (Nabokina et al., 2012). Congenital deficiency in these transporters is associated with clinical syndromes in humans including Brown‐Vialetto‐Van Laere syndrome, where patients experience progressive neurologic deficits, and is treated successfully with extremely large doses of riboflavin (Dipti et al., 2005; Spagnoli & De Sousa, 2012). The C. elegans genome harbors two riboflavin transporter orthologs, rft‐1 and rft‐2 (Biswas et al., 2013). Previous work has shown that the riboflavin transporters have differential expression and phenotypes based on knockdown. (Biswas et al., 2013). rft‐1 expression is localized to the intestine and knockdown leads to a complete elimination of brood size. rft‐2 expression occurs in the pharynx and intestine and is associated with reduced brood size. While the loss of function of C. elegans orthologs of riboflavin transporters is known to lead to embryonic lethality, it is not known whether the depletion of riboflavin after early embryonic and larval development has deleterious consequences. Based upon published literature, riboflavin deficiency is predicted to have broad metabolic impacts on the organism, including reducing cellular respiration by impacting the citric acid cycle and mitochondrial electron transport activity, as well as reducing the enzymatic function of a wide variety of oxidoreductase enzymes important for anabolic activity. It remains unknown whether the impact of such broad metabolic perturbations following development are deleterious, or whether there are advantageous aspects based upon the activation of energetic stress responses under conditions of low riboflavin and FAD levels. Hormetic responses and extended lifespan due to energetic disruption are a well described phenomenon in C. elegans (Dillin et al., 2002; Feng et al., 2001). The traditional focus on cofactor biology has been that increasing micronutrient intake provides beneficial effects to the organism. We took the contrarian view that like macronutrient restriction, micronutrient restriction, under the right circumstances, could produce beneficial effects by creating energetic stress or through other means, with riboflavin being an obvious candidate given its prominent role in metabolism. In the present study, we ask whether flavin co‐factor depletion via disrupting the normal uptake of riboflavin can promote advantageous, metabolic stress defenses such as those activated by dietary restriction and mitochondrial energetic stress. We determine that physiologic riboflavin depletion alters cellular energetics and activates key longevity factors AMPK, FOXO, and the mitochondrial unfolded protein response (UPRmt). Riboflavin depletion via riboflavin transporter knockdown extends lifespan and promotes healthspan in C. elegans, dependent upon an AMPK‐UPRmt‐FOXO signaling relay. These data suggest that riboflavin depletion provides metabolic benefits and by leveraging factors mobilized by caloric restriction and energetic stress without actual reductions in caloric intake. Further, despite the deleterious impacts of monogenic diseases in riboflavin transporters, selective knockdown of transporters, and alteration of riboflavin physiology may provide translational opportunities to manage energetic states and thus metabolic diseases of aging.
The lack of brood and prominent intestinal expression suggested that rft‐1 had the most prominent metabolic phenotype, so, we examined consequences of its knockdown (Gandhimathi et al., 2015). rft‐1 depletion via RNA interference (RNAi) prompts a significant, 25% increase in lifespan (Figure 1a). RNAi knockdown of rft‐1 reduces the transporter's mRNA by approximately 70%, suggesting that partial riboflavin transporter deficiency rather than complete knockdown induces this phenotype. (Figure S1a). mRNA encoding the paralogous riboflavin transporter rft‐2 trends non‐significantly upward with rft‐1 RNAi, which indicates that there is not significant compensation for rft‐1 knockdown by rft‐2, and confirms the specificity of the RNAi‐based knockdown of rft‐1 (Figure S1a) To confirm that the longevity phenotype of the rft‐1 knockdown animals is due to reduced riboflavin uptake rather than a non‐canonical effect of the transporter, we administered high dose riboflavin supplementation to attempt to overcome the deficit in transport. As expected, high doses of riboflavin abrogate the lifespan increase attributable to rft‐1 RNAi (Figure 1a). This strongly suggests that riboflavin is the etiologic factor in the rft‐1 RNAi phenotype, and further that the depletion of riboflavin due to transporter deficiency is the source of lifespan extension. Worm lysates of young adult worms treated with rft‐1 RNAi exhibit marked reductions in riboflavin, FMN and FAD levels as assessed by quantitative liquid chromatography/mass spectrometry (LC/MS) (Figure 1b). In parallel, we evaluated the expression of enzymes key to flavin co‐enzyme synthesis including riboflavin kinase (rfk‐1) and FAD synthetase (flad‐1). rft‐1 RNAi was not associated with reductions in expression of flad‐1 and rfk‐1 which produce FAD and FMN, respectively, suggesting stoichiometric depletion of FMN/FAD rather than reductions in the enzymes that govern production (Figure S1a). Previous descriptions of brood size deficits in rft‐1 knockdown animals suggests that the germline may play a role in the phenotype (Biswas et al., 2013; Qi et al., 2017; Yen et al., 2020). We utilized rrf‐1 (somatic RNAi blunted, germline RNAi competent) and ppw‐1 (somatic RNAi competent, germline RNAi incompetent) mutants and examined brood size and lifespan on rft‐1 RNAi. The loss of brood size is dependent on somatic action of rft‐1 (i.e., normalized in rrf‐1 mutants which do not efficiently conduct somatic RNAi). This suggests that a somatic process is altering metabolic and reproductive capacity (Figure 1c). Lifespan was also dependent on somatic action of rft‐1, as rrf‐1 mutants did not exhibit lifespan extension on rft‐1 RNAi (Figure 1d). Germline loss of RNAi extended lifespan further on rft‐1, suggesting that preserved germline uptake of riboflavin accentuates somatic depletion of riboflavin and potentiates the lifespan extension (Figure 1e). Lifespan extension with rft‐1 RNAi in a long‐lived glp‐1 mutant lacking germline stem cells is significantly blunted, suggesting that the germline is required to act as a riboflavin sink for beneficial effects of rft‐1 RNAi to manifest (Figure 1f). The reduction in brood size raised the question of whether germ line stem cells and oocyte production are altered by riboflavin depletion. We examined the presence of the germline stem cells and oocytes via DAPI staining, which revealed an intact germline and oocyte production (Figure S1b). Animals treated with rft‐1 RNAi exhibit normal developmental timing (Figure 1g), indicating that delays in development do not play a role in lifespan extension with rft‐1 knockdown. In order to quantify the temporal dynamics of somatic riboflavin‐related gene expression as a proxy for flavin biology across the lifespan, we examined the expression of riboflavin transporter and FAD synthetic enzymes in germlineless glp‐4 mutants. Somatic rft‐1 and rft‐2 mRNAs increase in glp‐4 animals at day 4 but return back to normal levels through aging on day 10. Rfk and flad‐1 expression increase through the life of animals (Figure S1c). In spite of this, rft‐1 RNAi‐prompted lifespan extension is dependent upon whole life RNAi, as post‐developmental RNAi fails to extend lifespan (Figure S1d). The most probable explanation for this observation is that rft‐1 knockdown very likely needs to be manifest during L4 to young adult development when rapid germline expansion is operative to effectively “steal” and deplete somatic riboflavin levels. Riboflavin deficiency is associated with neurologic sequelae in mammals, and we wanted to verify that feeding behavior and motility is unchanged compared to control animals (Qi et al., 2017). The knockdown of rft‐1 does not affect food intake as measured up to adult day 3 by a food disappearance assay (Figure 1h). Pharyngeal pumping rates are also unchanged. (Figure S1e). Max crawling speed increases with rft‐1 RNAi (vs. vector control) in L4 larvae but is similar between adult day 1 and adult day 3 animals (Figure 1i). Average crawling speed shows similar patterns (Figure S1f). Animals spend equivalent amounts of time on food at L4 and adult day 1 when treated with vector vs. rft‐1 RNAi (Figure S1g). Animal length and width were also similar across the lifespan. (Figure S1h) These findings suggest that animals treated with rft‐1 knockdown exhibit normal developmental rate to adulthood, robust eating and size, and healthy activity levels in adulthood suggesting that the nutritional depletion of riboflavin does not reduce healthspan to achieve lifespan extension.
FOXO is known to act genetically downstream of nutrient‐sending manipulations that extend lifespan (Greer et al., 2007; Lee et al., 2003) and we hypothesized that it may be activated in the context of riboflavin depletion. Indeed, the loss of function in the sole C. elegans FOXO ortholog, daf‐16, is epistatic to lifespan extension with rft‐1 RNAi, with or without the presence of supplemental riboflavin (Figure 2a). A DAF‐16::GFP translational reporter demonstrates greater nuclear localization at adult day 1 and day 3 with riboflavin depletion, suggesting that rft‐1 RNAi activates DAF‐16 (Figure 2b and S2a). Transgenic DAF‐16::GFP worms treated with empty vector and rft‐1 RNAi as synchronous L1 were subsequently transferred at adult day 1 to plates with and without riboflavin. By adult day 3, additional riboflavin completely abrogates the DAF‐16 nuclear localization evident in in rft‐1 RNAi treated animals (Figure S2b). This indicates that the consequences of riboflavin depletion on daf‐16 nuclear localization are reversible in adulthood. Confirming increased transcriptional activity of DAF‐16 in the setting of riboflavin depletion, RNAi of rft‐1 leads to the upregulation of a sod‐3p::GFP reporter (Figure 2c). This activation is present through adult day 7, indicating consistent FOXO activation post‐developmentally (Figure S2c). The activation of FOXO suggests either suppression of insulin‐like/PI‐3 kinase signaling (canonical FOXO activation) or activation via another mechanism. We examined whether insulin signaling was playing a role by examining the effect of rft‐1 RNAi on akt‐1 and pdk‐1 gain of function mutants (Paradis & Ruvkun, 1998). These animals are short lived due to constitutive inhibition of FOXO. The akt‐1 gain‐of‐function mutation abrogates lifespan extension attributable to rft‐1 RNAi (Figure 2d). The pdk‐1 gain‐of‐function mutant on rft‐1 RNAi, conversely, still exhibits lifespan extension (Figure S2d). This indicates that the suppression of DAF‐16 activity via augmented Akt signaling abrogates lifespan extension prompted by riboflavin deficiency.
We suspected that AMPK may be similarly activated by energetic stress with riboflavin deficiency, and further that AMPK activation may be mechanistically linked to lifespan extension with rft‐1 RNAi. Knockdown of rft‐1 fails to promote longevity with loss of function in the AMPKα catalytic subunit aak‐2 (Figure 3a), and this pattern is not altered by the addition of riboflavin (Figure 3b). Lifespan is still extended in long‐lived aak‐2oe animals with rft‐1 RNAi (versus the same animals on vector RNAi control), suggesting that aak‐2 is necessary but not sufficient for lifespan extension (Figure 3c). Consistent with AMPK activation by riboflavin depletion, phospho‐AMPKT172 levels are increased in rft‐1 RNAi treated animals, and this effect is abrogated by the addition of riboflavin (Figure 3d). In order to determine whether AMPK is required for the activation of DAF‐16 under riboflavin depletion, we examined DAF‐16::GFP nuclear localization with and without functional aak‐2. Under rft‐1 RNAi conditions, DAF‐16 nuclear localization still increases in the absence of aak‐2 (Figure S3a). The activation of AMPK suggests that modulation of cellular energetics might play a role in the longevity phenotype seen with the rft‐1 knockdown. We hypothesized that reductions in flavin cofactor (FAD, FMN) concentrations induce mitochondrial stress responses due to the changes in organellar energetics by altering redox state. We examined the impact of riboflavin depletion on the redox ratio utilizing label‐free multiphoton microscopy and fluorescence lifetime imaging (FLIM) of intestinal cells in control and rft‐1 RNAi treated animals (Figure S3b,c). Animals treated with rft‐1 RNAi versus empty vector control‐treated animals exhibit a significant decrease in the optical redox ratio, defined as the ratio of FAD/(NAD[P]H + FAD) calculated based on the autofluorescence signatures of the corresponding co‐enzymes. There is also an increase in mitochondrial clustering, suggesting altered mitochondrial energetics in an oxidized state and morphologic changes to the mitochondrial network (Figure 3e) (Liu et al., 2018). A significant decrease in the NAD(P)H protein bound fraction suggests decreased levels of glutaminolysis and enhanced utilization of the glutathione pathway (Alonzo et al., 2016). An increase in the FAD bound fraction suggested that overall FAD depletion was causing aggressive capture of flavin cofactors by enzymatic machinery (Figure 3e). LC/MS metabolomics of rft‐1 RNAi treated animals indicates significant changes in multiple metabolic pathways. Increases in purine catabolism metabolites are present, including xanthine, hypoxanthine, and guanosine (Figure 3f). Pathway enrichment analysis reveals that other than riboflavin metabolism, riboflavin deficiency leads to significant impact on metabolites in the glutathione and purine metabolic pathways (Figure S3d). Components of the citric acid cycle including citrate, isocitrate, and α‐ketoglutarate, as well as ATP, are reduced by riboflavin depletion. Glutamate and glutamine levels are also reduced suggestive of disruptions in glutamine synthesis (Figure 3f).
The frank changes in energetic status, altered redox ratio, and presence of mitochondrial clustering all suggested that mitochondrial stress responses may also be contributing to the longevity response to riboflavin deficiency. Indeed, the UPRmt is activated by rft‐1 knockdown, as evidenced by induction of hsp‐6p::GFP (Kimura et al., 2007) on days 1 and 3 of adulthood, and this effect is abrogated by the addition of riboflavin (Figure 4a). Full activation of the UPRmt is known to require the transcription factor ATFS‐1, which translocates to the nucleus to activate stress response pathways (Wu et al., 2018). Established target genes of ATFS‐1, including cdr‐2, hrg‐9, and C07G1.7 (Soo & Van Raamsdonk, 2021) are upregulated with rft‐1 knockdown, with the previously undescribed ATFS‐1 target gene C07G1.7 exhibiting a 2000‐fold increase (Figure 4b). The UPRmt activation is necessary for lifespan extension, as atfs‐1 loss of function animals, which have lower lifespans than wild‐type at baseline, do not exhibit lifespan extension with rft‐1 knockdown (Figure 4c). In the setting of gain‐of‐function mutations in atfs‐1, which lead to shortened lifespan (Bennett et al., 2014), rft‐1 knockdown still promotes significant extension of lifespan (Figure 4d). This indicates that the UPRmt response is necessary but not sufficient for the riboflavin depletion longevity phenotype. We wished to evaluate the relationship between the UPRmt and DAF‐16 and examined nuclear localization of DAF‐16 in atfs‐1 mutants. rft‐1 RNAi in atfs‐1;DAF‐16::GFP animals revealed abrogated nuclear localization, suggesting that DAF‐16 activation is partially dependent on atfs‐1 and the UPRmt (Figure 4e). We evaluated sod‐3 and hsp‐6 expression in aak‐2 and atfs‐1 animals to determine if the transcriptional responses to daf‐16 and atfs‐1 were present in these mutant strains with riboflavin depletion. QPCR revealed that the upregulation of hsp‐6 and sod‐3 with rft‐1 RNAi are both dependent upon both atfs‐1 and aak‐2 (Figure 4f).
The long‐lived phenotype of riboflavin depletion and the role of flavin cofactors in beta‐oxidation suggests that changes in lipid composition may be manifest following rft‐1 knockdown. We hypothesized that the changes in lipid metabolism occur upstream of or in parallel to FOXO activation, due to changes in enzymatic function (such as reduced activity of lipid dehydrogenases). RNAi of rft‐1 induces significant increases in fat mass in both the intestine and the germline in adult day 1 worms, as exhibited by fixative‐based oil‐red‐O and nile red staining (Figure 5a, S5a). We examined the epistatic relationships of the UPRmt, FOXO, and AMPK with regard to this high lipid phenotype. We also evaluated whether key lipid regulating pathways such as target of rapamycin (TOR) and nhr‐49 play an important role in the lipid biology of riboflavin depletion. Epistasis analysis indicates that rft‐1 RNAi increases fat mass in daf‐16, aak‐2, nhr‐49, and raga‐1 animals but not in atfs‐1 or rict‐1 animals by fixative‐based nile red staining (Figure 5b). Post‐developmental rft‐1 RNAi beginning at young adult stage in enhanced RNAi eri‐1 animals also increases fat mass, indicating that riboflavin deficiency does not impact lipid metabolism exclusively through a developmental pleiotropy (Figure 5b). Confirming these observations and further delineating the nature of the lipids increased in abundance following rft‐1 RNAi, stimulated Raman scattering (SRS) analysis of live adult day 1 animals indicates increased total signal of unsaturated fatty acids and the unsaturated to total lipid ratio in riboflavin deficiency (Figure S5b) (Nieva et al., 2012; Potcoava et al., 2014). By gas chromatography/mass spectrometry (GC/MS) of triglyceride and phospholipid fractions separated by solid phase extraction, global triglyceride stores increase by 40% in both young adult and adult day 1 rft‐1 RNAi‐treated animals, consistent with the spectroscopic imaging and fixative‐based lipid staining (Figure 5c). While only small changes are evident by young adulthood (Figure S5c), by adult day 1 animals exhibit significant differences in their lipid composition, with increases in unsaturated and branched chain fatty acids, and reductions in cyclopropyl fatty acids in both triglyceride and phospholipid fractions (Figure 5d, S5c). Due to its role in lipid oxidation, we examined whether rft‐1 lifespan extension was dependent upon nhr‐49. rft‐1 RNAi significantly extends the lifespan of nhr‐49 mutants (Figure 5e). Due to increases in branched chain fatty acid synthesis, we examined the expression of acdh‐1, which is a known branched chain dehydrogenase in C. elegans and that has been previously reported as a dietary sensor (Watson et al., 2013). An acdh‐1 promoter GFP reporter is significantly increased ~70% with rft‐1 RNAi at adult day 1 (Figure S5d). In order to begin to determine whether unsaturated fatty acids are elevated in riboflavin deficiency owing to increased production vs utilization, we examined the expression of the fatty acid desaturase fat‐7 (Han et al., 2017). Riboflavin depletion does not promote changes in fat‐7 expression early in life but preserves it with aging (Figure S5e).
The long‐lived phenotype of riboflavin depletion, concomitant with decreases in energetics, AMPK activation, and impairment in lipid beta‐oxidation, suggested to us that riboflavin depletion mimics some features of a dietary restriction‐like phenotype. The acs‐2 and bigr‐1 genes are well established to be transcriptionally upregulated during periods of caloric restriction in C. elegans (Van Gilst et al., 2005; Wu et al., 2016). rft‐1 RNAi induced bigr‐1::RFP and acs‐2p::GFP expression with age (Figure 6a). We sought to assess whether other canonical caloric restriction factors and processes were involved in riboflavin depletion. We examined eat‐2 animals, which have extended lifespan owing to defective pharyngeal pumping, and noted that the animals experience further lifespan extension with rft‐1 RNAi (Figure 6b) We also examined the C. elegans forkhead box A (FOXA) homolog pha‐4, known to be epistatic to caloric restriction‐mediated longevity (Panowski et al., 2007). Lifespan extension with rft‐1 RNAi is dependent on pha‐4/FOXA (Figure 6c). Inhibition of TOR signaling is also important in the response to dietary restriction. Thus, we examined whether mutants in the TOR complex 1 (TORC1) and TOR complex 2 (TORC2) pathways exhibit longevity with riboflavin depletion. RAGA/raga‐1 mutants, which have defects in TORC1 activation, experience lifespan extension on rft‐1 RNAi (Figure 6d). To further determine whether altered TORC1 activity is required for the hormetic effects of riboflavin depletion, we used a strain of C. elegans that contains a knock‐in, humanized S6K, which permits immunoblotting for phospho‐S6K to determine the activity of TORC1. No difference is evident in phospho‐S6K between rft‐1 RNAi and empty vector control, suggesting that altered TORC1 signaling is not essential to the biological response to riboflavin depletion (Figure 6e). In contrast, the loss of function mutations in the essential TORC2 subunit rict‐1 experience no lifespan extension with rft‐1 RNAi, indicating that riboflavin depletion requires TORC2 activity to exact its favorable effects (Figure 6f).
Vitamins, as essential cofactors for life, have traditionally been viewed as highly beneficial entities independent of their concentrations. This is particularly true of the B‐vitamins, which are water soluble and do not exhibit significant toxicities at moderate supraphysiologic doses. Our work in C. elegans counters the notion that more is always better, as depletion of key enzymatic co‐factor riboflavin can trigger metabolic and physiologic stress responses that are hormetic in nature and extend lifespan. We were initially concerned that the reduction of the flavin cofactors such as FAD and FMN would be frankly toxic to the organism. This was particularly true when LC/MS revealed that FAD levels in the rft‐1 treated animals were 80%–90% below normal. We anticipated that the loss of FAD to this level would prevent the function of succinate dehydrogenase and the ability of the electron transport chain (ETC) to absorb electrons produced by the citric acid cycle. Our data does indeed suggest disruptions in mitochondrial respiration, with decreased redox ratios, mitochondrial clustering, reduced ATP production, and activation of the UPRmt. Unexpectedly, however, riboflavin depletion has lifespan dependencies that differ from of classic ETC disruption, such as in cco‐1 and frh‐1 (frataxin) knockdowns (Durieux et al., 2011; Schiavi et al., 2013). Previous examinations of cco‐1 and frh‐1 RNAi have shown that lifespan extension with ETC disruption is AMPK and FOXO independent (Durieux et al., 2011, Schiavi et al., 2013) and atfs‐1 independent (Bennett et al., 2014). These epistatic relationships to lifespan extension suggest a different “flavor” of UPRmt, and that riboflavin depletion does not represent a solitary poisoning of the ETC as seen through complex I–IV knockout or knockdown. Riboflavin depletion is likely having pleiotropic effects leading to alternative ways of activating UPRmt (i.e., enzymatic disruption), and that the function of the UPRmt under these circumstances may be managing stress responses that are not purely energetic in origin. The central role of AMPK in the lifespan extension suggests that energetic perturbation is still relevant to riboflavin depletion‐mediated longevity. Riboflavin depleted animals exhibit normal food consumption, normal TORC1 activation, and elevated triglyceride stores. Despite evidence for ample macronutrient availability, the animal experiences apparent energetic deficits with activation of AMPK during rft‐1 RNAi. This begged the question as to whether the animal is activating dietary restriction (DR) pathways independent of true DR. The lifespan dependencies on FOXO/daf‐16 and FOXA/pha‐4, as well as activation of canonical “starvation” reporters acs‐2 and bigr‐1, provide evidence that the animal is utilizing these pathways in the context of micronutrient depletion only. Amino acid sensing and dietary restriction via essential nutrients such as methionine have been previously described to extend lifespan (Cabreiro et al., 2013). The depletion of canonical vitamin co‐factors, however, has proven deleterious in previous investigations. Depletion of biotin, B12 derivatives, and folate has previously shown to shorten lifespan (Austin et al., 2010; Bito et al., 2013). To the best of our knowledge, our work is the first to show that the depletion of a vitamin cofactor can mimic features of dietary restriction and extend lifespan using shared molecular mechanisms. We anticipated that the relationships between the UPRmt, AMPK activity and daf‐16 activation would be epistatic based on the hypothesized most proximal impact of having mitochondrial dysfunction. We noted, however, that FOXO nuclear localization occurs independent of AMPK but partially dependent on atfs‐1, and that upregulation of sod‐3 is also dependent on atfs‐1. Transcriptional activation of hsp‐6 is unexpectedly abrogated in aak‐2 mutants during riboflavin depletion, suggesting that AMPK activity is necessary for potentiation of the UPRmt . This complex interplay of these dependencies hints that there is a concerted cellular response to reductions of the flavin cofactors. This creates the exciting opportunity to explore for flavin sensing molecular systems that converge on these key stress factors either upstream or downstream and may provide new avenues to activate pro‐longevity paradigms (Figure 6g). The lipid phenotypes provide some clues to the nature of flavin sensing. Elevated triglyceride stores following riboflavin depletion is independent of canonical lifespan regulating pathways such as FOXO, AMPK, and TORC1. This decoupling of fat mass and lifespan suggests that the lipid phenotype may be regulated by enzymatic processing of lipids upstream of the energetic stress axes. The exceptions to this were the atfs‐1 and rict‐1 mutants. Phenotypes associated with UPRmt activation are known to induce lipid accumulation (Kim, Grant et al. 2016, Yang, Li et al. 2022). Recent work has identified NHR‐80 as a key regulator of citrate sensing and lipid accumulation in the UPRmt phenotype (Yang et al., 2022). The relevance of atfs‐1 activity to dve‐1 and ubl‐5 function suggests that the UPRmt may be instrumental in the communication of flavin depletion and related citric acid cycle disruptions on organismal energetics. The lack of fat mass increase in rict‐1 mutants, which at baseline exhibit higher lipid content, suggests either a dependency or inability for riboflavin depletion to overcome the excess lipid accumulation associated with TORC2 knockout. TORC2 has been described as a nutritional sensor that regulates lipid biogenesis (Soukas et al., 2009), and it is entirely plausible that there are distinct inputs in mitochondrial energetics, mito‐stress, and TORC2 activation that are governed by flavin biology. Further investigation would be beneficial to identify whether TORC2 can directly sense changes in flavin levels, as this would have significant implications for the nutritional regulation of anabolic signals in senescence and cancer. rft‐1 RNAi does not impact developmental rate and metabolic phenotypes manifest most impressively at the young adult to adult day 1 transition. This is in parallel to the growth and development of the germline and the oocytes. This pattern suggests either that the larval stages are relatively resistant to riboflavin depletion, or, more likely, contain and accumulate sufficient flavin cofactors at time of egg lay and during early larval development (prior to rft‐1 knockdown) to proceed through development normally. We surmise that somatic growth dilutes endogenous flavin cofactors, subsequently inducing the favorable effects of riboflavin deficiency uniquely in adulthood. Further, the development of the germline and riboflavin shunting into oocytes in late larval and early adult stages may prompt further, rapid riboflavin depletion, inducing the metabolic stress required to induce the phenotype identified in this work. This was particularly reflected in the germline RNAi deficient animals which revealed more pronounced lifespan extension compared with total body RNAi. It is worthy of mention that riboflavin deficiency leads to sterility, but this sterility is not accompanied by a decrease in germline stem cell numbers. Thus, effects on the germline are unlikely to be responsible for the shifts in lifespan and fat mass evident in riboflavin deficiency. The presence of a post‐developmental fat increase with depletion of riboflavin suggests that acute depletion in adulthood has important impacts that are likely different from depletion starting at larval stages. The most likely etiology for these post‐developmental changes are alterations in enzymatic activity due to loss of flavin cofactors. The flavin cofactors are important for a wide variety of enzymes particularly those associated with oxido‐reductase functions including the fatty acid dehydrogenases. The “flavoproteome” is an established set of enzymes requiring FAD, FMN, or riboflavin to function (Lienhart et al., 2013). The impact of riboflavin depletion globally on the proteome is likely to produce stoichiometric shifts in key metabolites that will alter global physiology. Differential utilization of different dehydrogenases (branched chain vs. long chain) may also explain the unique lipid phenotype that is produced with riboflavin depletion. Using metabolomics, we identified other examples of likely enzymatic effects, with evidence of reductions in purine catabolism likely due to loss‐of‐function in xanthine dehydrogenase. Alterations in xanthine metabolism have been previously described as beneficial and lifespan extending (Gioran et al., 2019). The impact of riboflavin depletion on enzymatic processes is complex and there are likely to be both hormetic and harmful impacts of this process. Identifying the enzymatic pathways where riboflavin depletion provides beneficial versus detrimental impacts will provide new insights into mechanistic targets promoting longevity. We suggest that further investigations into the functions of the flavoproteome and flavin biology will serve to identify new therapeutic and investigational targets for the metabolism of aging and aging associated diseases.
Strains were maintained at 20°C on nematode growth medium (NGM) plates seeded with E. coli OP50. All experiments were conducted at 20°C unless otherwise specified. The following strains were utilized: wild type (N2 Bristol ancestral), NL3511 ppw‐1(pk1425), NL2098 rrf‐1(pk1417), daf‐16(mgDF47), TJ356 zls356[daf‐16p::daf‐16a/bGFP+rol‐6(su1006), CF1553 muls84[(pAD76)sod‐3p::GFP + rol‐6(su1006)], GR1318 pdk‐1(mg142gf), GR1310 akt‐1(mg144gf), RB754 aak‐2(ok524), VC3201 atfs‐1(gk3094), QC118 atfs‐1(et18), SJ4100 zcls13[hsp‐6p::GFP + lin‐15(+)], DMS303 nls590[fat‐7p::fat‐7::GFP + lin15(+)], VL749 wwls24[acdh‐1p::GFP + unc‐119(+)] MGH266 rict‐1(mg451), VC222 raga‐1 (ok386), MGH559 aak‐2(ok754);zls356[daf‐16p::daf‐16a/b::GFP + rol‐6(su1006)], MGH249 alxls19 [bigr‐1::bigr‐1::mRFP3‐HA;myo‐2p::GFP], WBM392 Is[Pacs‐2::GFP + rol‐6(su1006)], MGH430 rsks‐1(alx48 humanized S6K hydrophobic motif). MGH113 nhr‐49(nr2041), CB4037 glp‐1(e2141), DA465 eat‐2(ad465), WBM60 uthls248[aak‐2p::aak‐2(genomic aa1‐321)::GFP::unc‐54 3′UTR + myo‐2p::tdTOMATO], MGH600 atfs‐1(gk3094);zls356[daf‐16p::daf‐16a/b::GFP+ rol‐6(su1006)].
Non‐RNAi experiments were all conducted on NGM plates containing E. coli OP50‐1 (CGC) as the food source and used 3–7 days after seeding. Cultures of E. coli OP50 were grown in Luria Broth (LB) for 15 h. at 37°C without shaking and seeded directly onto NGM plates. RNA interference experiments were conducted using E. coli HT115(DE3) bacteria (Ahringer library) as the food source. Clones were isolated from the primary RNAi library and plated on ampicillin/tetracycline plates. Individual clones were grown in LB broth for 15 h with shaking. Cultures were concentrated 1:5 and seeded directly onto NGM plates containing 5 mM isopropyl‐B‐D‐thiogalactopyranoside and 200 mg/ml carbenicillin. Plates were used 1–5 days after seeding. All RNAi clones were sequence verified prior to utilization. Riboflavin solution or vehicle was applied to the plate and allowed to dry for at least 30 min prior to seeding with animals.
Culture grade riboflavin (Sigma‐Aldrich) was dissolved in 50 mM NaOH to a concentration of 13.3 mM (5 mg/ml). Fully seeded plates were treated with 500ul riboflavin solution (final concentration 665 μM) and allowed to dry on the plate prior to worm placement. Vehicle plates were treated with 500 μl 50 mM NaOH solution.
Lifespan analysis was conducted at 20°C except where indicated. Gravid adults were grown on NGM plates and isolated eggs were incubated overnight in M9 solution for hatching. Synchronized L1 animals were seeded unto RNAi plates and allowed to grow until the adult stage. Adult animals were subsequently transferred to fresh RNAi plates every other day until post‐reproductive stage where they were maintained on a single plate. Dead worms were counted daily or every other day. Statistical analysis for survival curves was performed using OASIS2 software (Han et al., 2016).
Synchronized L1s were prepared via bleach prep and plated on RNAi plates containing empty vector or rft‐1 RNAi grown at 20°C. Larvae were examined every 2 h starting 41 h after drop and scored for their transition to adulthood by the appearance of the vulvar slit.
Fifty synchronized L1 animals of each strain were dropped on EV and rft‐1 RNAi treated plates. Two days later, 2 young adult animals from each condition were dropped onto new EV and rft‐1 RNAi plates, respectively. These animals were transferred every 2 days until the two adults from each condition became post‐reproductive. All animals on residual plates were counted once they reached L4/Young Adult to calculate brood size.
Pumping rate was determined using a Sony camera attached to a Nikon SMZ1500 microscope that recorded 10 well fed day 1 adult animals per genotype. Pharyngeal contractions in 15 s time periods for 4 technical replicates were counted (by slowing video playback speed by 4×) for each animal using OpenShot and pumping rates per minute were calculated.
Food intake experiments were adapted from (Stuhr & Curran, 2020). Food intake was assessed in RNAi liquid media without antibiotics in flat‐bottom, optically clear 96‐well plates with 150 μl total volume. Age‐synchronized nematodes were seeded as L1 larvae and grown at 20°. 5‐Fluoro‐2′‐deoxyuridine (FUDR) was added 48 h after seeding at a final concentration of 0.12 mM. OD600 of each well was measured using a plate reader every 24 h starting at L1 stage and ending at day 5 of adulthood (168 h after dropping L1s). The fraction of animals alive was scored microscopically every day until the last day of the assay. Food intake per worms was calculated as bacterial clearance divided by worm number in well. Measurements were then normalized to the L4 to day 1 adult clearance rate for each condition. Lawn avoidance assays were conducted as described in (Stuhr & Curran, 2020). Bacteria were grown overnight in liquid culture of LB with corresponding antibiotics. The next day, bacteria were collected at the log phase, seeded onto RNAi plates at 5× concentration, dried, and allowed to grow overnight at 20°. L1s were dropped onto RNAi plates with each. Plates were checked 48 h later at the L4 stage, and the number of worms on and off food were counted. For size and movement assays, 30–50 worms were washed off of a plate in 50 μl of M9 with a M9 + triton coated P1000 tip and dropped onto an unseeded RNAi plate. The M9 was allowed to absorb and worms roamed on the unseeded plate for 1.5 h before imaging crawling. Crawling was imaged with the MBF Bioscience WormLab microscope and analysis was performed with WormLab version 2022. Worm crawling on the plate was imaged for 1 min for each condition at 7.5 ms. Worm crawling was analyzed with the software and only worms that moved for at least 90% of the time were included in the analysis.
Lipid staining protocol was adapted from Escorcia et al. (2018). Adult day 1 animals were collected via washing and washed twice with M9 solution. Animals were then fixed with 40% isopropanol for 3 min with shaking. For oil‐red‐O staining, working solution of oil‐red‐O was generated from stock solution and fixed animals were stained in working solution for two hours. Animals were subsequently placed in M9 solution to remove excess stain and were imaged on a Leica Thunder multichannel microscope to generate composite images. For Nile red staining, Nile red working solution was generated by mixing 6 μl/Nile red stock solution in 1 ml isopropanol. Animals were stained in working solution for 2 h followed by 30 min of wash in M9 solution. Nile red imaging was performed on the Leica Thunder GFP setting with 10 ms exposure at 5× magnification.
Worms were isolated by washing with M9 buffer and centrifuged into a pellet. Worm lysates were prepared by adding RIPA buffer and proteinase inhibitor cocktail (Roche) followed by water bath sonication in a Diagenode Bioruptor XL 4 at maximum strength for 15 min. Lysates were cleared of debris via centrifugation at 21,000 g for 15 min at 4°C and supernatant was collected. Protein concentration as measured using the Pierce BCA Assay (Thermo Fisher). Lysate was subsequently mixed with 4X Laemmli buffer (Bio‐Rad) and boiled for 10 min. Samples were run on SDS‐PAGE protocol (Bio‐Rad) and transferred to nitrocellulose membrane via wet transfer at 100 V for 1 h. Immunoblotting was performed according to primary antibody manufacturer's protocols. Secondary antibody treatment utilized goat ‐anti‐rabbit HRP conjugate or goat‐anti‐mouse‐HRP conjugate (GE Healthcare) at 1:10,000 and 1:5000 dilutions, respectively. HRP chemiluminescence was detected via West‐Pico substrate (Thermo Pierce). The Western blot results shown are representative of at least two experiments. Primary antibodies used were the following: Rabbit monoclonal anti‐Phospho‐AMPKα (Thr172), Cell Signaling Technology. Rabbit monoclonal anti‐p70 Phospho‐S6 Kinase (Thr389), Cell Signaling Technology. Mouse monoclonal anti‐Actin, Abcam.
Worms samples were flash frozen in liquid nitrogen and kept in −80°C until RNA preparation. Samples were lysed through the use of metal beads and the Tissuelyser (Qiagen) Total RNA was extracted using RNAzol RT (Molecular Research Center) according to manufacturer instructions. RNA was treated with RNAse free DNAse prior to reverse transcription with the Quantitect reverse transcription kit (Qiagen). Quantitative RT‐PCR was conducted in triplicate using a Quantitect SYBR Green PCR reagent (Qiagen) following manufacturer instructions on a Bio‐Rad CFX96 Real‐Time PCR system (Bio‐Rad) Expression levels of tested genes were presented as normalized fold changes to the mRNA abundance of control genes indicated in the figures by the δδCt method. The primers used for the qPCR are as follows: rft‐1 forward: GCTATTGTTCAGATCGCGTGC. rft‐1 reverse: CAGAGACCCAATTGACAAATACATGC. rft‐2 forward: CGGGAGTTGTTCAGATCGCT. rft‐2 reverse: GAGTCCCAGTTGACAACAGCA. rfk forward: TGTTGGAAAAAGAAACGAAAGAA. rfk reverse: TCGATTAAAATTCGGTAACAACG. flad‐1 forward: TGCCTGGAGTTCCAAAATTC. flad‐1 reverse: GAAGGGCTGGGTGTTTTACA. C07G1.7 forward: GCTGAAGAAGCTTCAACCGTAG. C07G1.7 reverse: TCTCGTGTCAATTCCGGTCT. hrg‐9 forward: TGGAATATTGAGTGGCGTTG. hrg‐9 reverse: CCTCCTCTACTTGGTGCATGT. cdr‐2 forward: CGAGCCTCATTTGGAAAGAA. cdr‐2 reverse: GCATCTGCCGCTGTAACTTT. sod‐3 forward: GCAATCTACTGCTCGCACTG. sod‐3 reverse: TGCATGATTTCATGGCTGAT. hsp‐6 forward: CGAAGACCCAGAGGTTCAAA. hsp‐6 reverse: AATGCTCCAACCTGAGATGG.
Lipid extraction and GC/MS of extracted, acid‐methanol derivatized lipids were performed as described previously (Pino & Soukas, 2020). Briefly, 10,000 synchronous mid‐L4 animals were sonicated with a probe sonicator on high intensity in a microfuge tube in 100–250 μl total volume. Following sonication, lipids were extracted in 3:1 methanol: methylene chloride following the addition of acetyl chloride in sealed borosilicate glass tubes, which were then incubated in a 75°C water bath for 1 h. Derivatized fatty acids and fatty alcohols were neutralized with 7% potassium carbonate, extracted with hexane, and washed with acetonitrile prior to evaporation under nitrogen. Lipids were resuspended in 200 μl of hexane and analyzed on an Agilent GC/MS equipped with a Supelcowax‐10 column as previously described (Pino et al., 2013). Fatty acids were indicated as the normalized peak area of the total of derivatized fatty acids detected in the sample, normalized by recovery of spiked‐in, standard phospholipid and triglyceride.
Four biologic replicates of adult day 1 wild‐type worms treated either with empty vector or rft‐1 RNAi were collected with M9 wash and frozen by liquid nitrogen into a worm pellet. Polar metabolites of homogenized worms were analyzed using a Thermo QExactive orbitrap mass spectrometer coupled to a Thermo Vanquish UPLC system, as previously described (Garratt et al., 2018). Bioactive lipids metabolites were profiled on the same system, as previous described (Lagerborg et al., 2019). Collected data were imported into the mzMine 2 software suite for analysis (version 2.53). Metabolites were annotated by using an in‐house library of commercially available standards. Please see supplemental methods for detailed methods. All mass integration values, normalized abundance values, significance testing scores, and pathway enrichment scores are included in this manuscript as Table S2.
All Western blotting quantifications were conducted on Bio‐Rad Image Lab. Intensity analysis for fluorescent images was performed on ImageJ. Statistical analyses were performed using Prism (GraphPad Software). The statistical differences between control and experimental groups were determined by two‐tailed students t‐test (two groups), one‐way ANOVA (more than two groups), two‐way ANOVA (two independent experimental variables), or log‐rank (survival analyses) as indicated in each figure legend, with numbers of samples indicated and corrected p values < 0.05 considered significant.
DIC, brightfield and fluorescence Imaging of animals was performed utilizing the Leica Thunder microscopy system. Animals were picked onto a slide containing agar and 2.5 mM levimasole solution. Imaging was performed within 5 min of slide placement. GFP and RFP Images were taken at 10 ms exposure at 30% FIM and at 5X magnification, unless otherwise specified. Fluorescence intensity for quantification was calculated utilizing ImageJ software. For signal intensity experiments, quantification was performed on 20 worms (10 worms of two biological replicates).
Wild type and mutant C. elegans were immobilized for fluorescence imaging using a previously proposed protocol (Kim et al., 2013). Endogenous two‐photon excited fluorescence (TPEF) images of C. elegans were acquired using a laser scanning microscope (Leica TCS SP8, Wetzlar, Germany) equipped with a femtosecond laser (Insight Deep See, Spectra Physics, Mountain View, CA). Fluorescence lifetime images (512 × 512 pixels) of C. elegans were acquired using the same excitation and emission settings as for intensity NAD(P)H and FAD images and a PicoHarp 300 time‐correlated single photon counting unit (PicoQuant, Berlin, Germany) integrated in the Leica SP8 system. Please see Supplemental methods for further details on methods and analysis.
Stimulated Raman scattering (SRS) images of C. elegans were acquired using a laser scanning confocal microscope (Leica TCS SP8, Wetzlar, Germany) equipped with a picosecond NIR laser (picoEmerald, APE, Berlin, Germany). SRS images were acquired in the wavenumber range of 2798 to 3103 cm−1 with an interval of 6 cm−1. The Nd:VAN 1064.2 nm output was used as the SRS Stokes beam and the OPO beam tuned from 800 nm to 820 nm with step size of 0.4 nm was used as the pump laser. SRS images (620 × 620 microns × 51 wavenumbers, 512 × 512 pixels × 51 wavenumbers, 0.75 zoom) were acquired using a water immersion objective HCX IRAPO L 25×/0.95 NA with pixel dwell time of 4.9 μs. The pixels corresponding to regions occupied by C. elegans were identified by implementing a global threshold of 300 (intensities ranged from 0 to 800). The SRS spectrum of each remaining pixel was normalized by the maximum SRS value of the entire field spectral image. To estimate the relative unsaturation level of the lipids in C. elegans, a ratio metric approach was adapted (Freudiger et al., 2011; Nieva et al., 2012; Potcoava et al., 2014; Verma & Wallach, 1977). Specifically, the ratio of the area under the SRS spectrum for wavenumbers spanning 2991 and 3022 cm−1 and wavenumbers spanning 2830 and 2870 cm‐1was estimated to represent the relative unsaturation levels (Freudiger et al., 2011, Nieva et al., 2012, Potcoava et al., 2014, Verma & Wallach, 1977). Both fluorescence and SRS images were calibrated for laser power before analysis.
AY, LM, DB, and AAS conceptualized the study. AY, LM, DB, and SE performed methodology. AY, LM, DB, and SE were involved in validation. AY, LM, SE, DB, SL, YZ, AA, LC, NS, and AAS contributed to investigation. AY, EG, KD, MJ, IG, and AAS performed formal analyses. AY and AAS were involved in writing. AY, FA, SE, LM, YZ, SL, LC, AA, DB, NS, EG, MJ, IG, and AAS were involved in review and editing. AY, IG, SPC, and AAS contributed to funding.
The authors report no competing interests.
Click here for additional data file. Click here for additional data file. Click here for additional data file. |
PMC9649605 | 36259256 | Eleni‐Dimitra Papanagnou,Sentiljana Gumeni,Aimilia D. Sklirou,Alexandra Rafeletou,Evangelos Terpos,Kleoniki Keklikoglou,Efstathios Kastritis,Kimon Stamatelopoulos,Gerasimos P. Sykiotis,Meletios A. Dimopoulos,Ioannis P. Trougakos | Autophagy activation can partially rescue proteasome dysfunction‐mediated cardiac toxicity | 19-10-2022 | autophagy,cardiotoxicity,metformin,mitostasis,proteasome inhibitor,proteostasis | Abstract The ubiquitin–proteasome pathway and its functional interplay with other proteostatic and/or mitostatic modules are crucial for cell viability, especially in post‐mitotic cells like cardiomyocytes, which are constantly exposed to proteotoxic, metabolic, and mechanical stress. Consistently, treatment of multiple myeloma patients with therapeutic proteasome inhibitors may induce cardiac failure; yet the effects promoted by heart‐targeted proteasome dysfunction are not completely understood. We report here that heart‐targeted proteasome knockdown in the fly experimental model results in increased proteome instability and defective mitostasis, leading to disrupted cardiac activity, systemic toxicity, and reduced longevity. These phenotypes were partially rescued by either heart targeted‐ or by dietary restriction‐mediated activation of autophagy. Supportively, activation of autophagy by Rapamycin or Metformin administration in flies treated with proteasome inhibitors reduced proteome instability, partially restored mitochondrial function, mitigated cardiotoxicity, and improved flies' longevity. These findings suggest that autophagic inducers represent a novel promising intervention against proteasome inhibitor‐induced cardiovascular complications. | Autophagy activation can partially rescue proteasome dysfunction‐mediated cardiac toxicity
The ubiquitin–proteasome pathway and its functional interplay with other proteostatic and/or mitostatic modules are crucial for cell viability, especially in post‐mitotic cells like cardiomyocytes, which are constantly exposed to proteotoxic, metabolic, and mechanical stress. Consistently, treatment of multiple myeloma patients with therapeutic proteasome inhibitors may induce cardiac failure; yet the effects promoted by heart‐targeted proteasome dysfunction are not completely understood. We report here that heart‐targeted proteasome knockdown in the fly experimental model results in increased proteome instability and defective mitostasis, leading to disrupted cardiac activity, systemic toxicity, and reduced longevity. These phenotypes were partially rescued by either heart targeted‐ or by dietary restriction‐mediated activation of autophagy. Supportively, activation of autophagy by Rapamycin or Metformin administration in flies treated with proteasome inhibitors reduced proteome instability, partially restored mitochondrial function, mitigated cardiotoxicity, and improved flies' longevity. These findings suggest that autophagic inducers represent a novel promising intervention against proteasome inhibitor‐induced cardiovascular complications.
Abbreviations ALP autophagy lysosome pathway Atg autophagy‐related gene BTZ bortezomib CFZ carfilzomib C‐L/LLE caspase‐like peptidase activity CT‐L/LLVY chymotrypsin‐like peptidase activity ER endoplasmic reticulum MET metformin PI proteasome inhibitor PN proteostasis network PR protein restriction RAP rapamycin ROS reactive oxygen species SQSTM1 sequestosome 1 UPP ubiquitin proteasome pathway
Protein quality control maintains proteome homeodynamics (proteostasis) and is critical for cellular functionality and viability. Cell proteostasis is maintained by the action of a wired highly integrated compartment‐specific system, namely the proteostasis network (PN) (Labbadia & Morimoto, 2015). Major components of the PN are the protein synthesis and sorting/trafficking machineries, the endoplasmic reticulum (ER) unfolded protein response (UPRER), the molecular chaperones, and the two main degradation machineries, namely the autophagy‐lysosome (ALP) and the ubiquitin–proteasome (UPP) pathways (Pohl & Dikic, 2019). The autophagy‐lysosome is a conserved degradation process that includes microautophagy, chaperone‐mediated autophagy, and macroautophagy (Klionsky et al., 2016). Macroautophagy involves the formation of double‐membrane vesicles (autophagosomes), which, by the participation of autophagy‐related (Atg) proteins, sequester cytoplasmic portions, damaged polypeptides or organelles and transfer them to lysosome for degradation. Atg8 (along with its lipidated form) is a major driver for autophagosome maturation, and in mammals, the Atg8 family consists of six members divided into the LC3 and GABARAP subfamilies (Schaaf et al., 2016). ALP can also degrade ubiquitinated polypeptides and protein aggregates or ubiquitin decorated organelles (e.g., mitochondria) via the direct binding of SQSTM1 (sequestosome 1, also known as p62) to ubiquitinated substrates (Klionsky et al., 2016). On the contrary, UPP degrades normal short‐lived ubiquitinated proteins during their physiological recycling and non‐repairable unfolded or misfolded polypeptides (Tsakiri & Trougakos, 2015). The 26S eukaryotic proteasome is a complicated protein machine that comprises a 20S core particle (CP) bound to 19S regulatory particles (RP). The 20S CP consists of four stacked heptameric rings (two α surrounding two of β type) that form a barrel‐like structure; the caspase (C‐L), trypsin (T‐L), and chymotrypsin (CT‐L) like peptidase activities are located at the beta 1, beta 2, and beta 5 (known as Prosβ1, Prosβ2, and Prosβ5 in Drosophila) proteasomal subunits, respectively (Livneh et al., 2016). As we and others have shown, loss of proteostasis including declined proteasome activity are major hallmarks of aging (López‐Otín et al., 2013; Tsakiri & Trougakos, 2015). On the contrary, aberrant proteasome activation is found in advanced tumors (Sklirou et al., 2018), and thus, proteasome inhibition provides a promising novel anti‐tumor therapy (Manasanch & Orlowski, 2017). Consistently, several selective proteasome inhibitors (PIs), including Bortezomib (BTZ, a slowly reversible PI) and Carfilzomib (CFZ, binds irreversibly to proteasome), have demonstrated clinical efficacy in the treatment of hematologic malignancies, for example, multiple myeloma (MM), and are being evaluated for the treatment of other types of cancer (Dimopoulos et al., 2015). Nonetheless, and despite the fact that therapeutic PIs have revolutionized MM treatment, the emergence of severe adverse effects (AEs) such as peripheral neuropathy and/or cardiotoxicity remain a significant limitation in the clinic (Cornell et al., 2019; Dimopoulos et al., 2016; Kastritis et al., 2021). Cardiomyocytes are terminally differentiated post‐mitotic cells exhibiting very limited regenerative capacity; also, they are constantly exposed to proteotoxic, metabolic, and mechanical stress and are thus susceptible to reduced proteasome functionality (Patterson et al., 2007). Hence, therapeutic PI‐mediated proteasome dysfunction is likely a causative factor of cardiac malfunction in MM patients (Hasinoff et al., 2017). In support, cardiomyocyte‐restricted genetic inhibition of proteasome CT‐L activity in a mouse model resulted in increased (vs. controls) cardiomyocyte apoptosis and ischemia/reperfusion injury (Tian et al., 2012), as well as in cardiac malfunction during systolic overload (Ranek et al., 2015); proteasome malfunction in this model induced myocardial macroautophagy via the calcineurin‐TFEB‐p62/SQSTM1 pathway (Pan et al., 2020). Despite these interesting findings, the mechanistic details and downstream effects of heart‐targeted proteasome loss of function remain poorly understood at in vivo experimental settings. By exploiting the Drosophila in vivo model, we recently mapped the extensive functional crosstalk of proteostatic and mitostatic modules (Gumeni et al., 2019; Tsakiri, Gumeni, Iliaki, et al., 2019; Tsakiri, Gumeni, Vougas, et al., 2019). Also, our preliminary analyses showed that systemic administration of either CFZ or BTZ in young flies led to disruption of proteostasis and caused perturbation of cardiac functionality (Tsakiri et al., 2017). Drosophila is particularly suitable for studying cardiac AEs of therapeutic PIs before moving to the far more complex and time‐consuming mammalian models, due to its powerful genetics, the fact that flies' proteasomes structurally resemble those of mammals (Tsakiri & Trougakos, 2015), and also because the fruit fly is the only major invertebrate model organism that contains a beating heart tube and a circulatory system with developmental and functional homologies to the vertebrate heart (Piazza & Wessells, 2011). Moreover, the fly and mammalian heart/cardiomyocytes share many similar traits in conditions of declined proteostasis (e.g., during aging), including systolic and diastolic dysfunction, increased arrhythmia, and decreased metabolic fitness (Blice‐Baum et al., 2019). In support, as we showed recently, findings in the fly model (Gumeni et al., 2019; Tsakiri et al., 2017; Tsakiri, Gumeni, Iliaki, et al., 2019; Tsakiri, Gumeni, Vougas, et al., 2019) can be readily translated in mice (Efentakis et al., 2019) and in informative clinical studies (Kastritis et al., 2021; Papanagnou et al., 2018). We report here that heart‐specific partial loss of proteasome activity results in increased proteotoxic and energetic stress in the heart, leading to disrupted cardiac functionality, systemic toxicity, and reduced longevity. These phenotypes can be partially rescued by heart targeted or by systemic pharmacological activation of autophagy.
Initially, we investigated the age‐related effects on proteasome functionality specifically in the fly heart. We found a significant reduction of the rate limiting for protein breakdown CT‐L proteasomal activity in isolated fly hearts from aged (52–55 days old) vs. young (7–10 days old) flies (Figure S1A). Furthermore, staining and immunoblotting analysis of isolated heart tissues for ref(2)P (the fly ortholog of mammalian SQSTM1/p62, a well‐characterized autophagosome substrate) expression revealed its accumulation in aged hearts (Figure S1B), possibly suggesting increased proteome instability (Lim et al., 2015; Tsakiri, Gumeni, Vougas, et al., 2019) and reduced autophagic flux (Klionsky et al., 2016). Furthermore, LysoTracker tissue staining and lysosomal associated protein 1 (Lamp1, lysosomal marker) expression analysis showed a significant reduction of lysosomes and Lamp1 expression (Figure S1C), along with a moderate decrease of cathepsins activity (Figure S1D), in aged flies heart tissue. Also, aged flies displayed a deterioration of cardiac functionality, being evident by bradycardia, that is, reduced number of heart beats (Figure S1E,F). To study the effects of unbalanced proteostasis on heart functionality, we then induced siRNA‐mediated knockdown (KD) of the Prosβ5 proteasomal gene (Figure 1a) (CT‐L activity) in the cardiac tissue [Gal4ΤinCΔ4 driver (Lo & Frasch, 2001)] of young flies. We found that Prosβ5 KD suppressed mostly the targeted CT‐L (but also the C‐L) proteasome activity in the heart tissue (Figure 1b), increased ROS levels (Figure 1c), and induced the accumulation of ubiquitinated and carbonylated proteins (Figure 1d). Moreover, it reduced lysosomes number and Lamp1 expression (Figure 1e), and it also suppressed cathepsins activity (Figure 1f). Heart‐targeted Prosβ5 KD also led to decreased mitochondria number in young flies' heart tube (Figure 1g); this readout was combined with downregulation of mitophagy‐related genes (Pink1, park) and of the mitochondrial biogenesis master regulator gene srl [(spargel) also known as PGC1‐a, the fly ortholog of mammalian PPARGC1A; peroxisome proliferator‐activated receptor gamma coactivator 1‐alpha] (Figure 1h). Thus, proteasome dysfunction in young flies' heart induces extended collapse of proteostatic and mitostatic modules causing (among others) proteome instability. Cardiac dysfunction in young flies due to targeted loss of proteasome functionality was manifested by reduced number of heart beats (bradycardia) (Figure 2a) and arrhythmia (arrhythmic heart rate) (Figure 2b, Videos S1 and S2). Specifically, although control flies presented a relatively stable number of beats ranging from 3 to 3.6 beats/sec, proteasome dysfunction in the heart resulted in significant irregularity with 0.2–4 beats/sec (Figure 2b). Interestingly, despite non‐significant inhibition of the CT‐L proteasome activity in other than cardiac tissues after Prosβ5 KD (Figure S2A), we found that heart‐specific proteasome KD tended to trigger mitochondrial respiratory deficiency at the whole organism level (Figure 2c), indicating systemic energetic stress. The systemic effects were also evident by disrupted developmental processes, since Prosβ5 KD in the heart significantly reduced pupation and flies' hatching rates (Figure 2d). Also, heart‐specific Prosβ5 KD is associated with reduced size of both larvae and adults (Figure 2e), a phenotype indicative of severe systemic metabolic stress. Finally, heart‐targeted proteasome KD accelerated age‐related phenotypes, as it caused early defective locomotion (Figure 2f) and reduced longevity (Figure 2g, Table S1). Similar effects, albeit less intense, were found after Prosβ1 (C‐L activity) KD (not shown); all reported phenotypes were specific to Prosβ5 KD as none was evident in a mCherry RNAi transgenic line (not shown). Taken together, these findings highlight the cardiac tissue dependence on proper proteasome functionality; also, they suggest that heart‐specific disruption of proteasome function in young flies induces systemic effects and accelerates aging.
The severe impact of proteasome dysfunction due to reduced CT‐L activity on heart functionality could also explain the cardiotoxicity seen in the clinic by the CT‐L‐specific therapeutic PIs (e.g., CFZ) used for MM treatment (Dimopoulos et al., 2016; Kastritis et al., 2021). In line with our previous findings showing enhanced ubiquitination of dysfunctional proteasomes (Tsakiri et al., 2013), we found increased co‐localization of proteasomes with Atg8a (Figure S2B1) in dissected hearts expressing Prosβ5 RNAi; this observation indicates increased targeting of unstructured and/or dysfunctional proteasomes to proteaphagy (data to be presented elsewhere). We also observed the upregulation of other proteasome proteins, that is, 26Sα (20S complex) and P54/Rpn10 (19S complex) levels after Prosβ5 KD, suggesting a possible feedback loop mechanism aiming to restore normal proteasome functionality (Figure S2B). Interestingly, treatment of isolated semi‐intact hearts after Prosβ5 KD with bafilomycin A1 (BAF; Chang et al., 2020) revealed no further accumulation of the lipidated Atg8a/GABARAP form suggesting (in this experimental setting) a likely block of autophagic flux (Figure S3A). In support to reduced autophagic flux, we found that Prosβ5 KD resulted in accumulating ref(2)P‐GFP [not amenable to transcriptional regulation (Klionsky et al., 2016)] (Figure S3B,C) and Atg8a [Figure S3B; as reported (Jacomin et al., 2020), Atg8a also showed nuclear localization] in larvae muscle tissue, as well as, that Prosβ5 RNAi reduced GFP‐Lamp1 and lysotracker co‐localization in larvae heart tissues (Figure S3D), indicating loss of lysosomes acidification (Johnson et al., 2016). Thus, prolonged Prosβ5 KD also suppresses the autophagic machinery. We thus initially investigated whether the toxic effects of CT‐L inhibition in the heart and systemically can be alleviated by genetically activating autophagy. To this end, we overexpressed Atg8a in the Prosβ5 RNAi background [this intervention did not affect Prosβ5 downregulation (Figure S4A)], and found that, although Atg8a upregulation did not increase proteasome activity in the cardiac tissues (Figure 3a) and it did not suppress ROS accumulation (Figure 3b), it restored normal cathepsins activity (Figure 3c) and enhanced lysosomes number (Figure 3d). Consistently to cathepsins increased activity, which is indicative of activated autophagy (Bullón et al., 2018; Tatti et al., 2013; Xu et al., 2021), Atg8a overexpression (OE) in the Prosβ5 RNAi background seemingly enhanced autophagic flux in flies' heart and whole body (Figure S4B) and decreased ubiquitinated and carbonylated proteins (Figure S4C). Also, it normalized mitochondrial number as compared to Prosβ5 RNAi flies (Figure 3e), upregulated mitostatic genes expression, including the mitochondrial biogenesis regulator srl/PGC1‐a, (Figure 3f), and tended to improve mitochondria respiration rates (Figure 3g). Additionally, Atg8a OE in the Prosβ5 RNAi background tended to increase heart beats and restored a more regular and stable heart rhythm (number of beats/sec) (Figure 3h,i, Videos S1–S3), improving thus cardiac function. Further, heart‐specific Atg8a OE mitigated Prosβ5 KD‐mediated systemic developmental defects and growth retardation (Figure S4D); it also tended to restore physiological thickness and dimensions of the heart tube's conical chamber (Figure S4E) and increased Prosβ5 RNAi flies' neuromuscular (locomotion) activity and lifespan (Figure 3j,k, Table S1). Hence, the toxic effects of proteasome dysfunction in the heart can be partially rescued by heart‐targeted Atg8a OE, underlying the protective role of ALP upon proteasome dysfunction.
Since caloric restriction (CR) promotes autophagy (Bagherniya et al., 2018), we subjected Prosβ5 RNAi flies to low protein intake, which has demonstrated healthspan/lifespan benefits in a variety of model organisms (Mirzaei et al., 2016). We selected PR (reduced amino acid and protein availability) for this intervention since several studies have suggested that it contributes to about half of the lifespan extension mediated by CR (Pamplona & Barja, 2006). By using an Atg8a/GABARAP antibody for staining the fly Atg8a protein (Chang et al., 2020) in +/GFP‐Lamp1, Gal4TinCΔ4 flies, we found that PR augmented Atg8a‐Lamp1 co‐localization, indicating increased fusion of lysosomes with autophagosomes and hence enhancement of autophagy flux in heart tissues after PR (Figure S5A1); PR also resulted in marginal p‐Ampka and significant foxo accumulation in flies' somatic tissues (Figure S5A2). Furthermore, Prosβ5 RNAi (or Prosβ1 RNAi; not shown) flies fed with low protein medium displayed (vs. controls) increased mitochondria number in the heart tissue (Figure S5B) and improved heart functionality (Figure S5C, Videos S2 and S4). Moreover, low protein intake partially suppressed heart‐targeted proteasome KD‐mediated acceleration of aging (Figure S5D, Table S1), further supporting the beneficial effects of autophagy activation via systemic dietary restriction on heart‐targeted proteasome dysfunction‐mediated toxicity.
Given the cardiotoxicity of therapeutic PIs in both the clinic (Cornell et al., 2019) and the fly model (Tsakiri et al., 2017), we then asked whether pharmacological systemic activation of autophagy could ameliorate this severe AE. We thus exposed wild‐type flies to BTZ (1 μM) or CFZ (50 μM) for 7 days and combined (or not) the treatment with administration of the autophagy inducer RAP (100 μM); at the used concentrations, BTZ and CFZ inhibit the proteasomal CT‐L activity by ~20%–30% (Tsakiri et al., 2017). We found that RAP did not affect the PI‐mediated proteasome inhibition (Figure S6A). It also increased (vs. flies exposed solely to PIs) mitochondrial number in the cardiac tube (Figure S6B) and restored a more physiological heart function (Figure S6C,D). We then examined the anti‐glycemic MET (an FDA and EMA‐approved drug), which is also considered as an autophagy activator (Kulkarni et al., 2020). After screening a broad range of MET concentrations in young flies, we selected the concentration of 1 mM for our studies, as this concentration upregulated foxo, p‐Ampka, and Atg8a‐I (unlipidated form), Atg8a‐II (lipidated form) protein expression levels, parallel to ref(2)P/p62 downregulation in flies' hearts (Figure 4a). Immunofluorescence staining of Atg8a/GABARAP in flies expressing GFP‐Lamp1 and treated with MET showed that MET led to increased autophagosome and lysosome co‐localization (Figure S7A). Furthermore, staining with LysoTracker and an Atg8a/GABARAP antibody revealed that co‐administration of MET with either BTZ or CFZ, increased (vs. solely BTZ or CFZ treatment) lysosome and autophagosome numbers in the heart tissue of treated flies (Figure 4b). It also largely normalized cathepsins activity in heart tubes of CFZ or BTZ‐treated flies (Figure 4c), suggesting enhanced autophagic flux (Xu et al., 2021). Consistently, treatment of flies with 1 mM MET decreased BTZ‐ or CFZ‐mediated ref(2)P/p62 and Atg8a accumulation in larvae and adult flies' tissues (Figure S7B,C) indicating enhanced autophagic flux (Klionsky et al., 2016). Co‐administration of MET did not increase proteasome activities in PI‐treated flies' heart tissue (Figure 4d). Yet, it mitigated proteotoxic and oxidative stress, especially in combination with CFZ, as was evident by reduced accumulation of carbonylated (but not ubiquitinated) proteins (Figure 4e) and tended to suppress PI‐mediated redox imbalance (Figure 4f). Furthermore, we found that MET administration increased (vs. solely CFZ or BTZ‐treated flies) mitochondria number (Figure 5a) in PI‐treated flies' heart tube; it also augmented the expression level of the srl/PGC1‐a gene (Figure 5b) and mitochondria respiratory state (ST3:ST4) (Figure 5c) of PI‐treated flies, suggesting that MET treatment partially normalized tissue energetics. In support, although lipid levels were not further reduced (vs. solely MET or PIs treatment) in MET and CFZ or BTZ co‐treated flies' fat body (Figure S8A), exposure to MET tended to normalize (mostly vs. solely CFZ‐treated flies) the expression levels of the triglyceride lipase Atgl/bmm and of the lipid droplets regulating Lsd‐1, Lsd‐2 (Figure S8B) genes, indicating metabolic rebalancing. MET also restored a more physiological heart functionality, as it largely normalized flies' heart beats (Figure 5d, Videos S5 and S6) and decreased arrhythmia events presented after CFZ or BTZ administration (Figure 5e). Finally, MET co‐administration suppressed the PI‐related pro‐aging phenotypes, as it was found to improve flies' locomotion activity (Figure 5f) and lifespan/healthspan of young (Figure 5g, Table S1) and middle‐aged (not shown) flies. Overall, pharmacological inducers of autophagy (e.g., RAP or MET) can alleviate therapeutic PI‐mediated cardiotoxicity.
Proteasome is central to maintenance of PN functionality, representing a key regulator of cell growth and survival in eukaryotic cells. Whereas the decline of its activity during aging (particularly in post‐mitotic tissues) contributes to age‐related phenotypes and degenerative diseases (Kaushik & Cuervo, 2015; Tsakiri & Trougakos, 2015), certain tumors become addicted to high proteasome activities likely due to excessive proteome instability and oxidative damage (Sklirou et al., 2018). Consistently, although PIs have revolutionized the therapy of hematologic malignancies (Dimopoulos et al., 2015), their use in the clinic is marked by severe AEs, such as peripheral neuropathies and/or heart failure (Dimopoulos et al., 2016). Neurons and cardiomyocytes share common features, as they are terminally differentiated cells, characterized by limited ability to dilute accumulating proteome damage (Rujano et al., 2006). Particularly, cardiomyocytes are highly specialized cells with elevated metabolic demands and constant exposure to increased proteotoxic, oxidative, and mechanical stress; therefore, they are highly dependent on proper PN functionality (Fan et al., 2020; Gupta & Robbins, 2016). In support, we found that heart‐targeted proteasome KD in young flies correlated with heart failure, phenocopying the cardiovascular complications seen in the clinic by therapeutic PIs (Cornell et al., 2019). Similarly to either genetic‐ or PI‐mediated proteasome dysfunction at the whole‐animal level (Tsakiri, Gumeni, Vougas, et al., 2019); targeted proteasome KD in flies' cardiac tissues triggered oxidative, proteotoxic, and energetic stress. Reportedly, proteasome disruption has been shown to trigger the activation of autophagy (Pan et al., 2020); yet, in line with previous findings in tumor cells (Kao et al., 2014) or in fly myocytes (Zirin et al., 2015), we observed that sustained targeted proteasome inhibition in cardiac tissues disrupted autophagy, decreased lysosomal number, and disrupted pH in the lysosomal lumen. Given that lysosomes generate and maintain their pH gradients, by using the activity of a proton‐pumping V‐type ATPase, which uses metabolic energy in the form of ATP to pump protons into the lysosome lumen (Ishida et al., 2013; Todkar et al., 2017), our findings indicate a rather generalized collapse of proteostatic machineries, probably due to impaired cellular energetics. Notably, proteasome malfunction resulted in significantly decreased mitochondrial number consistent with studies showing that loss of mitostasis and PRKN‐mediated ubiquitination of the outer mitochondrial membrane proteins recruits SQSTM1/p62 to mitochondria, where it is thought to promote mitophagy via its capacity to directly interact with the MAP1LC3/LC3 (Tanaka et al., 2010). Mitochondria maintain cellular energetics through oxidative phosphorylation, while dysfunctional mitochondria increase ROS production and even promote cell death upon excessive cell damage (Giacomello et al., 2020). Given the mitochondrial evolution and the physical isolation of their contents, regulation of mitostasis was thought to be independent of UPP. Nonetheless, it was shown that numerous mitochondrial proteins are subjected to ubiquitination (Jeon et al., 2007; Peng et al., 2003), as well as that UPP mediates the degradation of inter‐ and outer‐membrane (Kowalski et al., 2018; Metzger et al., 2020) mitochondrial proteins, and of proteins involved in mitochondrial dynamics and motility (Giacomello et al., 2020). Interestingly, Prosβ5 (and to a lesser extend Prosβ1; not shown) KD in flies' heart tissues led to downregulation of genes involved in mitochondria quality control system (e.g., Pink1/park) and also of the master regulator of mitochondrial biogenesis srl/PGC1‐a (Dorn 2nd et al., 2015), suggesting that proteasome KD is also likely accompanied by reduced mitophagy and/or mitochondrial biogenesis. Consistently, a number of recent studies have shown that mitochondrial perturbation greatly impacts on cardiomyocytes functionality having a crucial role in the progression of cardiovascular diseases (reviewed in, Ajoolabady et al., 2022). On the contrary, either daw (a TGFB‐INHB/activin‐like protein) KD or rictor (a TORC2 subunit) OE in fly hearts promoted cardiac autophagic flux and enhanced lifespan (Chang et al., 2020). Notably, we also observed that heart‐targeted proteasome KD triggers systemic toxicity, as it results in developmental defects, growth retardation [indicative of defective insulin/IGF‐like signaling and metabolic deregulation (Zhang et al., 2009)], and aging acceleration; future studies will elucidate whether systemic signaling of cardiac dysfunction is mediated by specific (currently unknown) mediators in the hemolymph. Similar systemic responses in the fly model have been observed after nervous or muscle‐targeted disruption of the PN (Tsakiri, Gumeni, Iliaki, et al., 2019; Tsakiri, Gumeni, Vougas, et al., 2019) or mitostasis (Gumeni et al., 2019). Similarly, unfolded protein stress in different cellular compartments of the neuron, for example, cytoplasm (Prahlad et al., 2008), mitochondria (Durieux et al., 2011), or the ER (Taylor & Dillin, 2013) transmit the respective stress responses to distal tissues, while expression of an aggregation‐prone protein in Caenorhabditis elegans neurons elicits a stress signal that affects whole‐animal physiology (Berendzen et al., 2016). As mentioned, the highly integrated intracellular and/or external components of the circuit that maintains organismal proteostasis by signaling molecular perturbations across different tissues and organs remain to be identified. Our therapeutically relevant observations indicate that damage accumulation in heart tissues due to suppression of proteasome peptidases activity can be mitigated by heart‐specific (e.g., Atg8a/LC3 OE), as well as by (systemic) dietary (e.g., PR) or pharmacological (e.g., RAP or MET) activation of autophagy. Specifically, concomitant upregulation of autophagy in heart tissues expressing low proteasome activities partially restored autophagy flux and proteome stability, upregulated heart mitochondrial number (likely via srl/PGC1‐a induction) and respiratory capacity, improved cardiac activity, and also increased flies' longevity. Thus, preservation of proteome homeostasis is crucial for cardiac functionality. Atg8a OE in a Prosβ5 RNAi (or Rpt6 RNAi) background (Tsakiri, Gumeni, Vougas, et al., 2019) has also been found to alleviate the toxic effects of proteasome deregulation in both muscle tissue and whole body, indicating a generalized rather than a tissue‐specific effect. Supportively, transgenic expression of Atg8a in the fly brain enhanced autophagy in neurons, extended flies' longevity, and increased resistance to oxidative stress (Simonsen et al., 2008), while modest heart‐specific OE of foxo [an autophagy inducer (Cheng, 2019)] in the fly model maintained cardiac proteostasis and was cardioprotective (Blice‐Baum et al., 2017). Furthermore, OE of GFP‐LC3B improved mitochondrial function and extended proliferation in HUVEC endothelial cells via activation of mitophagy (Mai et al., 2012), while mild enhancement of mitophagy can offer therapeutic benefits against cardiovascular disorders without damaging mitochondrial functionality and hence cardiomyocytes health (Ajoolabady et al., 2022). Also, suppression of activin signaling, a negatively regulator of cardiac autophagy, improved cardiac health during aging in Drosophila (Chang et al., 2020), and autophagy activation during fasting periods induced mitochondrial biogenesis through elevated expression of srl/PGC1‐a (Kapahi et al., 2010; Lee et al., 2008). The autophagy inducer RAP or dietary restriction have been found to increase mitochondrial biogenesis in hearts of aged animals via srl/PGC1‐a upregulation (Chiao et al., 2016) and to extend longevity (Fontana & Partridge, 2015), while restricted diet delayed accelerated aging, improved neuronal function, and alleviated genomic stress in DNA repair‐deficient mice (Vermeij et al., 2016). It is assumed that restoration of mitostasis supplies cells with energy, which is particularly important for cardiomyocytes that are densely packed with mitochondria (Kubli & Gustafsson, 2014). Yet, as caloric restriction cannot be recommended for long life periods, an alternative autophagy‐activating approach would be physical exercise, which reportedly increases mitochondrial function and autophagic rates (Hayes et al., 2014; He et al., 2013). Our finding that treatment of flies being exposed to BTZ or CFZ with MET (an FDA and EMA approved drug) partially restores proteostasis and mitostasis leading to largely normalized cardiac activity (as is evident by reduced bradycardia and arrhythmia) is of particular interest. The biguanide MET is an 5’ AMP‐activated protein kinase (AMPK) and autophagy inducer in cells (including cardiomyocytes) and is the first drug to be tested for its age‐targeting effects in a large clinical trial (TAME; targeting aging by MET) (Kulkarni et al., 2020). Our recent studies in mice showed that CFZ administration leads to increased activation of PP2A (upstream suppressor of both Akt and AMPKα) and subsequent reduction in phosphorylation of AMPKα, an effect, which is mitigated when MET is co‐administrated (Efentakis et al., 2019). Also, MET enhanced autophagy and was cardioprotective in δ‐sarcoglycan deficiency‐induced dilated cardiomyopathy (Kanamori et al., 2019). Consistently, MET was cardioprotective in a rat myocardial infarction model and in H9c2 cardiomyoblasts during oxygen–glucose deprivation injury by promoting autophagic flux through the AMPK pathway (Wu et al., 2021). Also, prolonged MET administration in mice decreased oxidative stress resulting in lower levels of chronic inflammation (Martin‐Montalvo et al., 2013); thus, adaptation of cells to prolonged MET administration is likely important for its beneficial effects on mitostasis. Treatment of diabetic mice with MET restores autophagy in cardiac tissue, reduces cardiomyocyte apoptosis, and protects against the development of diabetic cardiomyopathy (He et al., 2013; Xie et al., 2011). Interestingly, we found that MET administration concomitant to PI‐mediated KD of proteasome activities upregulated Pink1, park, and srl/PGC1‐a genes expression in flies' heart tissues; similarly, MET administration upregulated the PINK1 and PRKN mRNAs expression in human mononuclear cells (Bhansali et al., 2020). Our on‐going studies with a suitable cardiac cell line, namely rat H9c2 cardiomyoblasts, have confirmed all findings shown herein in the fly model, including our observation that MET can partially restore proteasome inhibition‐mediated loss of proteostasis and mitostasis (unpublished data). Overall, the cardioprotective role of MET against therapeutic PIs is likely achieved by enhanced autophagy and mitostasis, as well as by restoring metabolic energy levels. Interestingly, we found that MET mildly reduced cathepsins activity. Early studies have demonstrated that MET targets hepatic mitochondria and modestly reduces ATP synthesis through inhibition of the respiratory chain complex I (Vial et al., 2019). Additional recent studies showed that low doses of MET can inhibit lysosomal v‐ATPase (Ma et al., 2022). We assume that partial inhibition of complex I and/or v‐ATPase activities by MET likely affects the acidophilic cathepsins activity due to lysosomal pH fluctuations; this notion highlights the necessity for a rigorous design of MET dosing regimens. Taken together, our findings indicate that heart‐targeted proteasome dysfunction disrupts cardiac activity and triggers systemic toxicity via increased proteome, mitochondrial and metabolic instability. These data provide mechanistic explanations for the reported cardiotoxicity of therapeutic PIs in the clinic and highlight the critical threshold that has to be reached in order to gain the therapeutic anti‐tumor effect of the PIs on one hand and to avoid PN collapse on the other, especially in aged post‐mitotic tissues that express reduced proteasomal activities (Trougakos, 2019). Our observation that proteasome inhibition‐mediated cardiac dysfunction in the fly model can be alleviated by autophagic inducers (e.g., MET) is thus a relevant preclinical insight for mitigating the proteasome inhibition‐induced AEs and preventing therapy discontinuation.
Fly stocks were maintained at ~25°C, 60% relative humidity on a 12 h light: 12 h dark cycle and were fed with standard medium. PR was performed for 7 days in young (7–10 days old) flies fed with standard medium containing 50% of the dry yeast extract (protein intake source) amount used in standard medium. The transgenic strains UAS‐mito‐GFP (#8443), w1118 (#5905), Gal4Mef2 (#27390), UAS Prosβ5 RNAi (#34810), and UAS GFP‐Atg8a (#51656) were obtained from the Bloomington Stock Center. The heart‐specific (Figure S9) Gal4TinCΔ4 driver was kindly donated by Prof. M. Frasch (Friedrich‐Alexander‐Universität, Germany). The UAS ref(2)P‐GFP and the UAS GFP‐Lamp1 flies were a gift from Prof. G. Juhász (Eötvös Loránd University, Hungary).
All used drugs, that is, BTZ (Cayman Chemical, 179,324–69‐7), CFZ (Cayman Chemical, 868,540–17‐4), MET (Metformin; Merck, 317,240), or RAP (Rapamycin; Cayman Chemical, 53,123–88‐9) were added in flies culture medium. Young adult flies (7–10 days old) were treated with MET for 14 days and with RAP for 7 days. Larvae were grown in medium containing the respective drug until the 2nd‐3rd instar larval stage (Figure S7B). Doses (including duration) of drugs used for flies or larvae treatment are indicated in figure legends. Neuromuscular activity (locomotion) and longevity assays were performed as described previously (Tsakiri, Gumeni, Iliaki, et al., 2019). For survival curves and statistical analyses, the Kaplan–Meier procedure and log‐rank (Mantel‐Cox) test were used; significance was accepted at p < 0.05. Statistical analyses and the number of the flies used for lifespan experiments are presented in figure legends and Table S1. Experiments were performed in (≥20) young adult flies and (≥20) 3rd instar larvae unless otherwise indicated. For all shown experiments, equal number of female and male flies was used. Aged flies were selected based on control flies' longevity curves and previous reported protocols for studying aging in the fly model (Piper & Partridge, 2016; Tsakiri, Gumeni, Iliaki, et al., 2019).
RNA extraction, conversion to cDNA and Q‐Real time‐PCR analysis was done as described (Tsakiri, Gumeni, Vougas, et al., 2019). Primers used (Drosophila genes) were the following: Pink1‐F: ACAGCTGGTCTACAACATCC, Pink1‐R: ACTGTAGGATCTCCGGACTG; park‐F: TTCTGCCGCAATTGTCTGCAGG, park‐R: GCATGCAACCGCCATCTCGCTC; ATPsynB‐F: CCCGTGGTGTGCAGAAAATC, ATPsynB‐R: AAACGCTGAATCTTGCGAGC; srl/PGC1‐a‐F: TGTGAGGTTAAAGCAGACGG, srl/PGC1‐a‐R: GTAACTTCTGAGCTTCCGTT; Atg8a‐F: ACGCCTTCGAGAAGCGTCGC, Atg8a‐R: CCAAATCACCGATGCGCGCC; Prosβ5‐F: GCCATCTACCATGCCACCTT, Prosβ5‐R: TTACCCAGCCGTCCTCCTTA; Lsd‐1‐F: ATCAGACCGATGGCCCACAG, Lsd‐1‐R: CTTCAGTTTGCGGGAGAAGC; Lsd‐2‐F: CCGAGCGCCTCCTTGAATAC, Lsd‐2‐R: GGAACTGGCATGTCATTTTCAGA; bmm/Atgl‐F: TTCACGCTCTATGACCAGCC, bmm/Atgl‐R: AGGATTGAAACACGGGGTCC. The RpL32/rp49 gene expression was used as a normalizer.
Isolated heart (highly enriched) or whole‐body flies' tissues were homogenized on ice and processed for SDS‐PAGE and immunoblotting, as described previously (Tsakiri, Gumeni, Iliaki, et al., 2019); primary and secondary antibodies were applied for 1 h at RT. Blot quantitation was performed by scanning densitometry and ImageJ software (National Institutes of Health) (Figures S10 and S11). Protein carbonyl groups were detected with the OxyBlot protein oxidation detection kit (Merk KGaA, s7150) as per the manufacturer's instructions. ROS were assayed, as previously described (Tsakiri, Gumeni, Vougas, et al., 2019); the emitted fluorescence was measured using the Spark® microplate reader (Tecan Trading AG) at excitation/emission wavelengths of 490/540 nm, respectively.
Measuring of proteasome peptidases or cathepsins activity in flies' tissues was done as described before (Tsakiri, Gumeni, Iliaki, et al., 2019). In either proteasome or cathepsins assays, the hydrolysis of the fluorogenic peptides was recorded using the Spark® microplate reader at excitation/emission wavelengths of 360/440 nm, respectively. For BAF treatment, semi‐intact hearts, or intact larvae were incubated with 100 nM of BAF (Cayman Chemical, 11,038) in artificial hemolymph (108 mM Na+, 5 mM K+, 2 mM Ca2+, 8 mM MgCl2, 1 mM NaH2PO4, 4 mM NaHCO3, 10 mM sucrose, 5 mM trehalose, 5 mM HEPES, pH 7.1) and in Broadie and Bate's buffer (135 mM NaCl, 5 mM KCl, 4 mM MgCl2, 2 mM CaCl2, 5 mM TES, 36 mM Sucrose; pH 7.15), respectively, for 2 h at room temperature prior to downstream assays.
Mitochondria respiration rate was determined using a Clark‐type O2 electrode connected to a computer‐operated Oxygraph control unit (Hansatech Instruments), as described before (Gumeni et al., 2019). Temperature was maintained at 25°C, and the total reaction volume was 300 μl. The respiratory control ratio (RCR) was calculated as the ratio of State 3–State 4 (ST3/ST4).
To visualize the Drosophila beating heart, young flies were dissected as described (Vogler & Ocorr, 2009); the entire procedure was performed in an oxygenated, artificial hemolymph solution at RT. Recordings of heart activity were acquired using a BMS (Breukhoven microscopy systems/3 MB) digital microscope camera mounted on a BMS microscope. Measurements (heart beats recording) were normalized to a 30 sec period per sample; for representative movies see, Videos S1–S6.
Young flies were dissected, and flies' tissues were isolated in PBS; dissected larvae tissues were also used. Flies or larval tissues were fixed with 4% formaldehyde in PBS and permeabilized with 0.2% Triton X‐100. After blocking (3% FBS in PBS), samples were incubated with primary and secondary antibodies. Fat body and dissected heart tubes were stained with Bodipy 493/503 (Molecular Probes™/Thermo Fisher Scientific Inc., D3922) and LysoTracker Red DND‐99 (Molecular Probes™/Thermo Fisher Scientific Inc., L7528), respectively, as per manufacturer's instructions. DAPI (Molecular Probes™/Thermo Fisher Scientific Inc., D1306) and Rhodamine Phalloidin (Molecular Probes™/Thermo Fisher Scientific Inc., R415) staining (1 h at RT) were used for nuclei and F‐actin visualization, respectively. Visualization of samples was done by using a Digital Eclipse C1 Nikon (Melville) CLSM equipped with 20 × 0.50 NA differential interference contrast (DIC), 60 × 1.40 NA DIC Plan Apochromat objectives, using the EZC1 acquisition and analysis software (Nikon). Z‐stacks with a step size of 1 μm were taken using identical settings. Each stack consisted of 26 plane images. Image J (National Institutes of Health) was used to determine fluorescence intensities.
Drosophila specimens were fixed in 4% formaldehyde and gradually dehydrated to 96% ethanol for 3 days. Subsequently, specimens were stained using 1% iodine dissolved in 96% ethanol (for the complete staining protocol see, Metscher, 2009) to increase the contrast between the soft tissues. Drosophila scans were performed with a SkyScan 1172 micro‐tomograph (Bruker). The scanner uses a tungsten source and is equipped with an 11 PM CCD camera (4000 × 2672 pixels). Samples were scanned at a voltage of 50 kV and a current of 198 μA without filter and a pixel size of ~2.90 μm for a half rotation of 180°. Projection images were reconstructed into cross sections using SkyScan's NRecon software (Bruker), which employs a modified Feldkamp's back‐projection algorithm. 3D analysis was performed for each scan using the software CT Analyser v.1.14.4.1 (Bruker) to calculate the thickness of the heart's conical chamber. Structure thickness calculation is based on the sphere‐fitting algorithm (Hildebrand & Rüegsegger, 1997). To study the dimensions of the studied flies' conical chamber, five different slices per sample (n = 7) were analyzed; measurements in x and y axes were taken using identical settings.
To study the effects of proteotoxic stress on development, 15 young, mated female flies were placed in Petri dishes containing 1.5% agar dissolved in sour cherry juice and allowed to lay eggs for 24 h. The embryos per dish were then counted and collected using PBS; 30 embryos from each population were then transferred in fresh standard medium and monitored for 14 days. Cultures were photographed at different developmental stages, and 20 larvae, pupae, and flies were weighted to record body weight. The numbers of pupae and adult flies were also measured.
The primary antibodies used were the anti‐Ubiquitin (Santa Cruz Biotechnology, Inc., sc‐8017), anti‐foxo (COSMO BIO CO, CAC‐THU‐A‐DFOXO), anti‐26S proteasome α (Santa Cruz Biotechnology, Inc., sc‐65,755), anti‐26S Proteasome p54 (Rpn10) (Santa Cruz Biotechnology, Inc., sc‐65,746), anti‐pAMPK (Cell Signaling Technology, #2535), anti‐GABARAP (Atg8a) (Cell Signaling Technology, #13733), anti‐Lamp1 (Abcam, ab30687), anti‐ATP5A (Abcam, ab14748), anti‐Actin (Cell Signaling Technology, #8457), and anti‐Gapdh (Sigma‐Aldrich, G9545). The anti‐ref(2)P/p62 antibody was kindly donated from Prof. G. Juhász. The secondary antibodies Peroxidase AffiniPure Donkey anti‐Mouse IgG (715‐035‐150) and Peroxidase AffiniPure Donkey anti‐Rabbit IgG (711‐035‐152) were purchased from Jackson ImmunoResearch Laboratories, Inc. The anti‐Rabbit‐IgG Alexa Fluor 647 (711‐605‐152), anti‐Rabbit‐IgG Alexa Fluor 488 (111‐545‐003), anti‐Mouse‐IgG Rhodamine (TRITC) AffiniPure (715‐025‐151), anti‐Mouse‐IgG Alexa Fluor 488 (115‐545‐003), and anti‐Mouse IgG DyLight™ 405 (715‐475‐151) antibodies were also from Jackson ImmunoResearch Laboratories, Inc. Ponceau S solution (6226–79) was from Sigma‐Aldrich.
Experiments were performed at least in triplicates (for each biological replicate, n ≥ 3; unless otherwise indicated). For statistical analysis, the GraphPad Prism 8.0, the MS Excel, and the Statistical Package for Social Sciences (IBM SPSS; version 25.0 for Windows) were used. Statistical significance was evaluated using unpaired t test or one‐way ANOVA test followed by Kruskal–Wallis test (see, figure legends). Data points correspond to the mean of the independent experiments and error bars denote standard deviation (SD); significance at p < 0.05 or p < 0.01 is indicated in graphs by one or two asterisks, respectively. For flies' survival curves and statistical analyses, the Kaplan–Meier procedure and log‐rank (Mantel‐Cox) test were used (significance was accepted at p < 0.05). Statistics for the longevity experiments are reported in figure legends and in Table S1.
IPT designed and supervised the study; EDP, SG, ADS, AR, and IPT conducted experiments or interpreted the data; KK performed the μ‐CT‐Scan analyses; ET, EK, KS, GPS, and MAD generated or contributed reagents, materials, or analysis tools; EDP and IPT wrote the manuscript. All authors discussed the results and commented on the manuscript.
None declared.
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PMC9649607 | 36176234 | Julia A. Loose,Francis R. G. Amrit,Thayjas Patil,Judith L. Yanowitz,Arjumand Ghazi | Meiotic dysfunction accelerates somatic aging in Caenorhabditis elegans | 29-09-2022 | aging,C. elegans,germ cells,germline,healthspan,lifespan,meiosis,proteostasis,reproduction,soma‐germline signaling | Abstract An expanding body of evidence, from studies in model organisms to human clinical data, reveals that reproductive health influences organismal aging. However, the impact of germline integrity on somatic aging is poorly understood. Moreover, assessing the causal relationship of such an impact is challenging to address in human and vertebrate models. Here, we demonstrate that disruption of meiosis, a germline restricted process, shortened lifespan, impaired individual aspects of healthspan, and accelerated somatic aging in Caenorhabditis elegans. Young meiotic mutants exhibited transcriptional profiles that showed remarkable overlap with the transcriptomes of old worms and shared similarities with transcriptomes of aging human tissues as well. We found that meiosis dysfunction caused increased expression of functionally relevant longevity determinants whose inactivation enhanced the lifespan of normal animals. Further, meiotic mutants manifested destabilized protein homeostasis and enhanced proteasomal activity partially rescued the associated lifespan defects. Our study demonstrates a role for meiotic integrity in controlling somatic aging and reveals proteostasis control as a potential mechanism through which germline status impacts overall organismal health. | Meiotic dysfunction accelerates somatic aging in Caenorhabditis elegans
An expanding body of evidence, from studies in model organisms to human clinical data, reveals that reproductive health influences organismal aging. However, the impact of germline integrity on somatic aging is poorly understood. Moreover, assessing the causal relationship of such an impact is challenging to address in human and vertebrate models. Here, we demonstrate that disruption of meiosis, a germline restricted process, shortened lifespan, impaired individual aspects of healthspan, and accelerated somatic aging in Caenorhabditis elegans. Young meiotic mutants exhibited transcriptional profiles that showed remarkable overlap with the transcriptomes of old worms and shared similarities with transcriptomes of aging human tissues as well. We found that meiosis dysfunction caused increased expression of functionally relevant longevity determinants whose inactivation enhanced the lifespan of normal animals. Further, meiotic mutants manifested destabilized protein homeostasis and enhanced proteasomal activity partially rescued the associated lifespan defects. Our study demonstrates a role for meiotic integrity in controlling somatic aging and reveals proteostasis control as a potential mechanism through which germline status impacts overall organismal health.
Abbreviations ANM age‐at‐natural‐menopause DEG differentially expressed genes DSB double strap break HR homologous recombination POI premature ovarian insufficiency WT wild type
The impact of increasing maternal age on fertility decline is well documented (Broekmans et al., 2009) but how germline integrity influences organismal aging remains poorly understood. In model organisms, sterility is often associated with increased longevity leading to the dogma of an antagonistic relationship between reproductive‐ and somatic‐health (Flatt, 2011). However, studies in worms, flies, mice and species in the wild have revealed that sterility per se does not confer longevity, and reproductive signals also promote lifespan and health (reviewed in Amrit & Ghazi, 2017). Emerging clinical and epidemiological data indicate that reproductive defects augur detrimental long‐term health consequences in both sexes (Cedars et al., 2017). In women, early loss of gonadal function, due to premature ovarian insufficiency (POI) or early age‐at‐natural‐menopause (ANM), is linked to greater susceptibility to cardiovascular disease, diabetes, dementia, osteoporosis and death (Muka et al., 2016; Tsiligiannis et al., 2019). Conversely, women with late ANM exhibit younger “epigenetic aging” profiles, individuals with familial history of longevity show delayed reproductive aging and there are increasing evidences of “rejuvenating” effects of pregnancy (Falick Michaeli et al., 2015; Levine et al., 2016; Perls et al., 1997). Thus, a compelling body of clinical evidence demonstrating that germline status influences organismal senescence is transforming the germline–soma relationship from a domain of evolutionary theory to a topic of significant biomedical relevance. However, correlative human studies neither test causality nor reveal the mechanisms by which the immortal germline may alter the aging of the mortal somatic tissues. Previous studies, including ours, have used the nematode, Caenorhabditis elegans, to dissect the impact of germline signals on lifespan, predominantly relying on sterile, long‐lived mutants (Amrit & Ghazi, 2017). Here, we chose to perturb the function of genes involved in meiosis, a germline‐specific process critical in the sexual life cycle of all eukaryotes for the production of haploid gametes (Hillers et al., 2017), to directly test a causal relationship between germline integrity and somatic aging. Our data show that loss‐of‐function mutations in meiotic genes shortened lifespan, impaired individual aspects of healthspan and proteostasis and induced transcriptional profiles reminiscent of aging worms and human tissues. Enhancing proteostasis partially rescued the lifespan reduction driven by meiosis dysfunction providing mechanistic insights into the influence of meiotic status on somatic health.
We measured the lifespans of 38 strains carrying mutations in genes operating at various stages of meiosis (Hillers et al., 2017). Thirty‐one exhibited a statistically significant lifespan reduction in at least one trial and 13 were short‐lived in at least two trials (Figure 1a,b, Table S1). Since many genes involved in double‐stranded break (DSB) induction and homologous recombination (HR) during meiosis also act in HR and DNA repair in mitotic somatic cells (Marcon & Moens, 2005), C. elegans is uniquely suited for addressing their meiotic roles as its adult somatic tissues are post‐mitotic and many, though not all, meiotic genes exhibit germline‐enriched or germline‐specific expression (Han et al., 2019; Hillers et al., 2017). Even so, we asked if this explained the meiotic mutants' short lifespans. But, 9/13 genes had no roles in DNA repair and the others showed disparate effects on DSB induction as measured by DNA localization of the RAD‐51 recombinase (Alpi et al., 2003; Mets & Meyer, 2009); RAD‐51 foci were absent, or normal or displayed altered temporal dynamics in different short‐lived mutants. Similarly, no unifying correlation was seen between germline apoptosis or the extent of fertility defects with lifespan reduction (Table S2). We chose three genes for detailed investigation based on their meiosis‐specific roles and degree of lifespan effects: spo‐11 (encodes ortholog of human SPO11 and initiator of meiotic DSBs; Dernburg et al., 1998), htp‐3 (encodes conserved HORMA‐domain protein that links DSB formation to homolog pairing and synapsis; Goodyer et al., 2008) and dsb‐2 (encodes protein with structural homology to human REC‐114 that is required for efficient DSB induction; Rosu et al., 2013). Multiple spo‐11 alleles as well as htp‐3 and dsb‐2 mutants exhibited an average lifespan reduction of 25%, 33% and 20%, respectively, over independent trials (Figure 1c–e, Table S1). Germline‐restricted RNAi (Zou et al., 2019) of dsb‐2, htp‐3, and spo‐11 throughout life was sufficient to shorten lifespan significantly, whereas, soma‐specific RNAi (Tijsterman et al., 2002) had no effect (Figure 1f, g, Table S3A,B) nor did RNAi during adulthood only (Figure S1A, Table S3C). Using the temperature‐sensitive sterile, glp‐1, mutant (Arantes‐Oliveira et al., 2002), we found that dsb‐2 mutation expectedly did not shorten glp‐1 lifespan but htp‐3 mutation induced a small reduction and glp‐1;spo‐11 showed a biphasic lifespan curve with higher early deaths (Figure 1h–j, Table S3D). spo‐11, dsb‐2 and htp‐3 have been reported to be germline restricted (Dernburg et al., 1998; Goodyer et al., 2008; Reinke et al., 2004; Rosu et al., 2013) and we did not detect any somatic expression of spo‐11 either (Figure S1B). Thus, while somatic roles of spo‐11 and htp‐3 cannot be overruled, spo‐11, htp‐3 and dsb‐2 inactivation in the germline was sufficient to shorten lifespan.
Young spo‐11 mutants resembled aging wild‐type (WT) worms in appearance (Garigan et al., 2002; Figure S1C,D) hence we examined the healthspan of meiotic mutants (Keith et al., 2014). Age‐linked mobility loss (measured as the rate of reduction in the animal “thrashing” in liquid) occurred significantly earlier and was more pronounced in spo‐11 and htp‐3 mutants compared to WT, but not in dsb‐2 (Figures 1k–m). Age‐related loss of muscle function (measured as the rate of reduction in pharyngeal‐muscle pumping) was significantly accelerated in htp‐3 mutants between Days 2 and 5 compared to WT, while spo‐11 and dsb‐2 mutants showed similar but smaller effects (Figure 1n–p). In C. elegans, age‐related neurological dysfunction is measured by assessing the progressive decline in associative learning capacity with age (Kauffman et al., 2010; Vohra et al., 2018). By Day 5, while ~50% of WT adults retained learning capacity, none of the dsb‐2 mutants did; htp‐3 mutants performed a little worse than WT while spo‐11 behaved normally (Figure 1q–s). We tested for premature loss of proteostasis, a conserved molecular hallmark of aging (Lopez‐Otin et al., 2013), by examining protein aggregation dynamics in a strain expressing an RFP‐tagged protein, PAB‐1, that shows age‐linked aggregation (Figure 1t–yii; Lechler et al., 2017). spo‐11 and htp‐3 mutants manifested significantly higher and earlier protein aggregation while dsb‐2 had a modest impact (Figure 1w–yii). We detected a small fraction of WT adults wherein fluorescence appeared diffused rather than as distinct punctae. While this was rarely seen before Day 10 in WT, a major fraction of the three meiotic mutants showed it at earlier ages (Figure 1v,w,x,y). Using another proteostasis marker, a temperature‐sensitive unc‐52 mutant that undergoes whole‐body paralysis at high temperature and is an established reporter of protein folding efficiency (Ben‐Zvi et al., 2009), we found that meiotic genes' inactivation increased paralysis considerably (Figure S1F). Thus, no healthspan feature tested was affected in all three mutants and none showed deficits in every feature tested. Altogether, these results indicated a compromised protein homeostasis environment in meiotic mutants and impairment of individual aspects of healthspan.
We compared the transcriptional profiles of Day 1 adult dsb‐2, htp‐3, and spo‐11 mutants with age‐matched WT animals. 7654 genes were upregulated (SPO11‐UP) and 3584 downregulated (SPO11‐DOWN) in spo‐11 mutants. 4730 genes were upregulated (HTP3‐UP) and 1666 downregulated (HTP3‐DOWN) in htp‐3 mutants. In dsb‐2 mutants, 106 genes were upregulated (DSB2‐UP) and 91 downregulated (DSB2‐DOWN; Figure S2A–D, Table S4A–F). Notably, htp‐3 mutants shared 86% of their transcriptome with spo‐11 mutants (4069/4730 UP, 1430/1666 DOWN; p < 0.0001; Figure S2A,B), and despite the small number of differentially expressed genes (DEGs) in dsb2, a striking 63 of 106 DSB2‐UP genes (59%, p < 0.0001) were also upregulated in both spo‐11 and htp‐3 mutants (Figure S2C; Table S4G). In light of their proteostasis phenotypes, it was notable that genes downregulated in the meiotic mutants included proteostasis factors known to promote longevity including (i) rpn‐6.1, encoding a 19S proteasome subunit that enhances lifespan in germline‐less animals (Vilchez et al., 2012) and (ii) cct‐8 and cct‐2, chaperone‐encoding genes that modulate somatic and germline proteostasis (Noormohammadi et al., 2016; Samaddar et al., 2021; Vilchez et al., 2012). RPN‐6.1 overexpression partially rescued spo‐11 mutants' short lifespan but CCT‐2 or CCT‐8 overexpression did not (Figure 2a, Figure S3A, Table S5). Thus, enhancing proteasomal activity mitigated some negative impacts of meiotic dysfunction suggesting that premature loss of somatic proteostasis may partially underlie their longevity deficits. The upregulated DEGs were enriched for genes encoding transmembrane and cytoskeletal proteins, stress response factors and genes involved in somatic longevity pathways such as insulin/IGF1 (irld‐35, and irld‐53), TGFβ (unc‐2), TOR (F39C12.1) and DAF‐12 signaling (Y19D10A.5; Templeman & Murphy, 2018; Figure S3B, Table S4G). We tested if these DEGs were functionally relevant or simply biomarkers of aging. Indeed, whole‐life (but not adult‐only) RNAi of irld‐53 (encoding a putative insulin binding protein) and Y19D10A.5 (encoding a putative transmembrane protein) significantly enhanced the lifespan of WT animals (Figure 2b, Table S6), whereas RNAi of C01B4.7 (encoding a sugar transporter) had significant but inconsistent lifespan enhancements across trials (Table S6). spo‐11 and htp‐3 also showed premature upregulation of chaperone genes operating in specific stress‐response pathways (Higuchi‐Sanabria et al., 2018): hsp‐16.2 and hsp‐6, representing the heat‐shock response and mitochondrial unfolded protein response, respectively, were upregulated, but hsp‐4 representing the endoplasmic reticulum unfolded protein response, was not (Figure 2c,d, Figure S3C).
Based on their aging phenotypes, we hypothesized that the meiotic mutants may exhibit a prematurely aged transcriptional profile, i.e., during young adulthood show high expression of genes normally upregulated in old WT animals. We used spo‐11 and htp3 DEGs to test this given their high overlap and the small number of dsb‐2 DEGs. Comparing the transcriptomes of Day 1 spo‐11 and htp‐3 mutants with those of middle‐aged (Day 5) and aging (Day 10) WT animals (Rangaraju et al., 2015) revealed a striking overlap. 66% of genes upregulated on Day 10 in WT were upregulated in Day 1 spo‐11 mutants (2037/3113, p < 2.4e−163), 41% were upregulated in htp‐3 (1274/3113, p < 6.1e−80) and 38% were elevated in both (1182/3113, p < 5.0e−96; Figure 2e, Table S7A). The overlap with Day 5 WT transcriptome was also highly significant: 55% (1413/2548, p < 5.1e−40) and 42% (1069/2548, p < 3.4e−72) for SPO11‐UP and HTP3UP gene lists, respectively (Figure S4A–C, Table S7A). Similarly, genes downregulated in Day 1 spo‐11 and htp‐3 mutants strongly overlapped with genes whose expression diminished by Day 5 and Day 10 in WT animals (Figure S4D–I, Table S7A). We obtained similar results upon comparisons with other studies elucidating aging worm transcriptomes. A 50% (574/1164, p < 1.8e−05) of genes that Golden et al. found to be upregulated throughout lifespan up to Day 24 were included in the SPO11‐UP class, 33% (380/1164, p < 3.4e−06) in the HTP3‐UP class and 31% (358/1164, p < 3.145e−10) in both (Figure 2f, Table S7B; Golden et al., 2008). Thus, meiotic mutants' lifespan reduction appeared to be accompanied by signs of a premature somatic aging transcriptional profile. Recently, Sutphin et al. (2017) screened the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium's expression datasets to enumerate the top 125 genes with the highest magnitude of age‐related differential expression in blood, and identified 87 C. elegans genes orthologous to this group. Remarkably, 51/87 were included in spo‐11 or htp‐3 DEGs (Figure 2g, Table S7C). This led us to ask if the meiotic mutants showed similarities with human‐aging transcriptomes from other tissues as well. Previously, Chatsirisupachai et al. examined data from 26 human tissues in the Genotype‐Tissue Expression (GTEx) project to enumerate genes upregulated with age in 10 tissues (Chatsirisupachai et al., 2019). We analyzed these data to look for genes shared between multiple aging tissues and identified 286 genes (“Aging‐DEGs”) upregulated in at least 3 of the 10 tissues (Figure S5, Table S7D). We found 318 C. elegans genes orthologous to 135 of the 286 (the larger number of worm genes is due to widespread gene duplications seen in the worm genome; Kim et al., 2018; Woollard, 2005). Of the 318 worm orthologs, 188 were upregulated in spo‐11 (59%, p < 1.0e−08), 124 in htp‐3 (39%, p < 1.3e−06) and 116 in both (36%, p < 4.0e−08; Figure 2h, Table S7D). Lastly, we examined the 99 genes comprising the human aging “Matreotype” (age‐linked expression profile of extracellular matrix or “matrisome” genes; Statzer et al., 2021) and found 78 (orthologs of 59 genes) encoded in the worm genome. Of these, 50 (64%, p < 1.8e−04), 35 (45%, p < 4.5e−04) and 34 (44%, p < 4.8e−05) were upregulated in spo‐11, htp‐3 or both mutants, respectively (Figure 2i, Table S7E). Thus, the transcriptional profiles of meiotic mutants showed similarities with multiple gene‐expression profiles associated with human aging. Altogether, using the unique strengths of C. elegans, our study provides direct evidence for the impact of meiosis on the health and longevity of the whole organism. This not only substantiates the close links between reproductive and somatic fitness but also the broader influence of germline integrity on organismal aging. Many of the genes we examined have human homologs with roles in mammalian meiosis (Hillers et al., 2017; Kim et al., 2018) and have been implicated in ANM and/or POI, including 5/13 factors whose mutants showed shortened lifespan (EXO1, HELQ1, CHEK2, RAD‐54, and RAD‐51; Jiao et al., 2018). The transcriptional similarities we identified between meiotic mutants and aging human tissues suggest avenues to unravel potential evolutionarily conserved mechanisms underpinning the meiotic control of health and longevity.
AG conceived the project. AG, JLY and JAL designed the experiments; JAL, FRGA, TP and AG performed the experiments; AG and JAL wrote the manuscript with input from the other authors.
The authors declare no conflict of interest.
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PMC9649608 | 36219531 | Christopher A. Denaro,Yara I. Haloush,Samuel Y. Hsiao,John J. Orgera,Teresa Osorio,Lindsey M. Riggs,Joshua W. Sassaman,Sarah A. Williams,Anthony R. Monte Carlo,Renata T. Da Costa,Andrey Grigoriev,Maria E. Solesio | COVID ‐19 and neurodegeneration: The mitochondrial connection | 11-10-2022 | Alzheimer's disease,bioenergetics,COVID‐19,inflammation,mitochondria,neurodegeneration,Parkinson's disease,SARS‐CoV‐2 | Abstract There is still a significant lack of knowledge regarding many aspects of the etiopathology and consequences of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection in humans. For example, the variety of molecular mechanisms mediating this infection, and the long‐term consequences of the disease remain poorly understood. It first seemed like the SARS‐CoV‐2 infection primarily caused a serious respiratory syndrome. However, over the last years, an increasing number of studies also pointed towards the damaging effects of this infection has on the central nervous system (CNS). In fact, evidence suggests a possible disruption of the blood–brain barrier and deleterious effects on the CNS, especially in patients who already suffer from other pathologies, such as neurodegenerative disorders. The molecular mechanisms behind these effects on the CNS could involve the dysregulation of mitochondrial physiology, a well‐known early marker of neurodegeneration and a hallmark of aging. Moreover, mitochondria are involved in the activation of the inflammatory response, which has also been broadly described in the CNS in COVID‐19. Here, we critically review the current bibliography regarding the presence of neurodegenerative symptoms in COVID‐19 patients, with a special emphasis on the mitochondrial mechanisms of these disorders. | COVID ‐19 and neurodegeneration: The mitochondrial connection
There is still a significant lack of knowledge regarding many aspects of the etiopathology and consequences of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection in humans. For example, the variety of molecular mechanisms mediating this infection, and the long‐term consequences of the disease remain poorly understood. It first seemed like the SARS‐CoV‐2 infection primarily caused a serious respiratory syndrome. However, over the last years, an increasing number of studies also pointed towards the damaging effects of this infection has on the central nervous system (CNS). In fact, evidence suggests a possible disruption of the blood–brain barrier and deleterious effects on the CNS, especially in patients who already suffer from other pathologies, such as neurodegenerative disorders. The molecular mechanisms behind these effects on the CNS could involve the dysregulation of mitochondrial physiology, a well‐known early marker of neurodegeneration and a hallmark of aging. Moreover, mitochondria are involved in the activation of the inflammatory response, which has also been broadly described in the CNS in COVID‐19. Here, we critically review the current bibliography regarding the presence of neurodegenerative symptoms in COVID‐19 patients, with a special emphasis on the mitochondrial mechanisms of these disorders.
The severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) was first reported in 2020 (Zhou et al., 2020). SARS‐CoV‐2 infection is the primary cause of the coronavirus disease 2019 (COVID‐19) pandemic (Zhu et al., 2020). The SARS‐CoV‐2 virus is a large, enveloped RNA virus (V'Kovski et al., 2021) with a ~30 Kb genome composed of two major segments. The open reading frame 1ab (ORF1ab) of ~20 Kb contains non‐structural proteins (Nsps), and a 10 Kb segment downstream of ORF1ab encodes structural proteins such as the nucleocapsid (N), spike (S), and membrane (M) proteins (Astuti & Ysrafil., 2020), and protein products of several other ORFs. The canonical mode of SARS‐CoV‐2 infection involves the spike protein attaching to the ACE2 receptor in the host cell membrane. While ACE2 was typically found in the heart, kidney, and gastrointestinal system; a recent study showed ACE2 activity in human neurons and cerebrospinal fluid (Xu & Lazartigues, 2022), as well as in areas of the brain involved in cardiorespiratory function, including the hypothalamus and the brainstem (Netland et al., 2008). The invasion of the host cell begins when the spike is cleaved into two major components, S1 and S2, by the furin protease and type II transmembrane serine protease. S2 then enables the fusion of the virus and cell membranes, the virus genome enters the host cell and viral proteins are translated from the Plus‐strand RNA and subgenomic mRNAs, produced later (Astuti & Ysrafil., 2020). Other modes of infection (e.g., involving cell–cell fusion leading to the formation of syncythia (Braga et al., 2021)) have also been described. When the first cases of COVID‐19 were reported, the prevailing view confined the most severe clinical disease outcomes to the respiratory system. However, recent work has shown the neuroinvasive potential of SARS‐CoV‐2. Neurological alterations have been reported already in the acute phase of COVID‐19, and in some cases as long‐term sequelae (Filatov et al., 2020). Increased rate of cell death within the central nervous system (CNS) has been observed as the infection progressed (Song et al., 2021). Further, it has been reported that the invasion of neural cells by SARS‐CoV‐2 could disrupt the stability of the blood–brain barrier (BBB) (Krasemann, Haferkamp, et al., 2022b; Rhea et al., 2021; Song et al., 2021; Zhang et al., 2021). This may allow either the virus or some viral proteins/their fragments to cross the BBB to reach microglia cells (Jeong et al., 2022) (via a mechanism that is not yet totally understood, but possibly involving trans‐cellular passage (Zhang et al., 2021)). While some authors support that SARS‐CoV‐2 physically crosses the BBB (Hansen et al., 2018; Jeong et al., 2022; Mazza et al., 2021; Netland et al., 2008; Xu & Lazartigues, 2022; Zhou et al., 2021), this still remains controversial. Detection of the virus even in the most detailed studies (Krasemann, Haferkamp, et al., 2022b; Matschke et al., 2020) is based on PCR amplification of short sections of genomic RNA and on some of the proteins of the virus, but not supported at the level of subgenomic mRNA evidence, which would indicate active viral transcription. The debates continue (Krasemann, Glatzel, & Pless, 2022a; Vavougios et al., 2022) and it has been proposed that in mice and in vitro models, not the whole virus but just the S protein (or its cleaved‐off part, S1) is able to cross the BBB (Buzhdygan et al., 2020; Rhea et al., 2021). S1 may bind ACE2 in brain neurons and elevate levels of angiotensin II, inducing microglial activation and leading to tissue damage, within the paraventricular nucleus in the brain (Rodriguez‐Perez et al., 2015). SARS‐CoV‐2 infection in the CNS increases the expression of proteases, cytokines, and clotting factors; interferes with the tight junction proteins located between the endothelial cells of the BBB; and upregulates leukocyte trafficking in this tissue (Erickson et al., 2021). The infection also leads to mitochondrial dysfunction and rewiring of multiple pathways of the host cells (Medini et al., 2021; Nagu et al., 2021; Scozzi et al., 2021). All these events are probably the underlying cause of increased inflammation, demyelination, and decreased oxygen saturation of neuronal tissue of some COVID‐19 patients, with a clear similarity to neuronal aging (Erickson et al., 2021; Nagu et al., 2021). The deleterious effects of SARS‐CoV‐2 infection on mitochondria have been already described. For example, it increases the generation of mitochondrial reactive oxygen species (ROS), and the expression of genes associated with glycolysis‐associated enzymes (Violi et al., 2020). This leads to a rewiring of the infected cell metabolism from the mitochondrial oxidative phosphorylation (OXPHOS) towards the cytosolic glycolysis. Increased expression of the pentose phosphate pathway (which is typically in equilibrium with glycolysis, and thus, elevated when glycolysis is increased) allows the virus to elevate production of nucleotides for replication (Icard et al., 2021). This rewiring triggered by the virus will ultimately contribute to a lower availability of ATP (Medini et al., 2021), leading to higher levels of ROS and oxidative damage in the host cell (Mohiuddin & Kasahara, 2021), further increasing cell death. Dysregulated bioenergetics has also been observed in cellular aging (Baltanas et al., 2013; Traxler et al., 2022). Infection by RNA viruses can lead to increased generation of ROS and their release into the cytoplasm, which supports the observation that SARS‐CoV‐2 infection is accompanied by elevated ROS, a typical consequence of OXPHOS dysregulation (Gatti et al., 2020; Li et al., 2021; Schwarz, 1996). Cytochrome B (MT‐CYB), encoded in mitochondrial DNA (mtDNA), is a component of complex III of the Electron Transfer Chain (ETC). SARS‐CoV‐2 infection increases blood levels of circulating mtDNA and its fragments coding for MT‐CYB in patients hospitalized with COVID‐19, as well as a close relationship between these levels and the rate of fatalities or increased risk of intensive care unit admission (Scozzi et al., 2021). It is well known that mutations in mtDNA can contribute to the etiopathology and evolution of the main neurodegeneration (Maynard et al., 2015). These mutations can also deleteriously affect mitochondrial dynamics and mitophagy. In the context of the SARS‐CoV‐2 infection, ORF9b appears to facilitate the degradation of Drp1, deleteriously affecting the elongation of mitochondria and likely the entire fusion/fission balance (Ganji & Reddy, 2020; Shi et al., 2014; Srinivasan et al., 2021). Impaired mitophagy has been also described in SARS‐CoV‐2 infection (Shang et al., 2021). Damaged mtDNA, dysregulated fission and mitophagy have been broadly described in neuronal aging, and in the main neurodegeneration (Angiulli et al., 2018; Guitart‐Mampel et al., 2022; Patro et al., 2021; Pickrell & Youle, 2015; Solesio et al., 2012; Solesio, Prime, et al., 2013a; Solesio, Saez‐Atienzar, et al., 2013b). Elevated inflammation accompanies SARS‐CoV‐2 infection, especially in severe cases of the disease (Ji et al., 2020). Neuroinflammation is also a hallmark of many of the most common neurodegeneration and neuronal aging (Guzman‐Martinez et al., 2019; Sparkman & Johnson, 2008). Mitochondria are both involved in the regulation of inflammation and deleteriously affected by inflammation. For example, one of the main mitochondrial mechanisms of the host response to viral infection through the regulation of inflammation is exerted via activation of the mitochondrial antiviralsignaling protein (MAVS). Mitochondrial exposure to viral SARS‐CoV‐2 proteins, probably via the interaction of ORF9b with the poly (C)‐binding protein 2 (PCBP2) and AIP4 (a E3 ubiquitin protein ligase) (Shi et al., 2014; Srinivasan et al., 2021), decreases the production of type‐I interferons IFN‐I (Shi et al., 2014). Thus, ORF9b seems to suppress MAVS signaling pathway (Singh et al., 2020), and, as described below, also interacts with mitochondrial import receptor protein TOM70. Lastly, some authors have found that ORF10 may induce degradation of mitochondria (including MAVS) via mitophagy, by targeting MAVS and the INF‐I signaling pathway (Li et al., 2022). Yet others have questioned if ORF10 is a protein‐coding gene (Figure 1). SARS‐CoV‐2 can interact with the innate immune system using a mechanism involving mitochondrial regulation. Specifically, during viral infection, neutrophils produce and release ROS to degrade the invading virus (Laforge et al., 2020). Under pathological conditions, neutrophils may produce excess ROS, causing the local inflammation to become systemic (Mortaz et al., 2018), via a mechanism which involves neutrophil extracellular traps and apoptosis‐associated speck‐like proteins that contain a caspase recruitment domain. This process of local inflammation turning into systemic inflammation is common in severe COVID‐19 cases (Aymonnier et al., 2022), as demonstrated by a high neutrophil‐to‐lymphocyte ratio in these patients (Seyit et al., 2021). The activation of the inflammasomes, among other effects, further contributes to increased ROS generation, and hence, to mitochondrial dysfunction (Abais et al., 2015). Increased ROS is a crucial contributor towards the production of damage‐associated molecular patterns, integral to human innate immune response as they bind to intracellular receptors, contributing to the development of an adaptive immune response (Roh & Sohn, 2018). Further involvement of mitochondria in the inflammatory response in COVID‐19 is suggested by the increased production of IL‐6 that can have broad consequences on mitochondrial dynamics. Specifically, it has been demonstrated that higher levels of IL6, a protein present in the SARS‐CoV‐2 infection and implicated in its severity (Patra et al., 2020), correlate with lower levels of TFAM, a protein that is involved in the regulation of the dynamics of the organelle, negatively affecting this process (Coomes & Haghbayan, 2020; Skuratovskaia et al., 2021). TFAM can bind to mtDNA and is essential for its maintenance (Kang et al., 2007; Skuratovskaia et al., 2021). SARS‐CoV‐2 also disrupts IFN‐I signaling via interfering with TOM70, a mitochondrial import receptor protein (Jiang et al., 2020). TOM70 has gained attention in the SARS‐CoV‐2 infection, as it has been demonstrated that ORF9b is associated to this protein, even if the studies show that this association is mainly with the cytosolic segment of the mitochondrial protein (Gao et al., 2021). Notably, in HEK293 cells the binding of ORF9b and TOM70 antagonized the innate immune activation, while TOM70 was required for the activation of MAVS (Thorne et al., 2022). A crucial component of the inflammatory response closely related to the status of mitochondrial physiology is the activation of the hypoxia‐inducible factor 1α (HIF‐1α). HIF‐1α is an important activator in key pathways, such as glycolysis and OXPHOS (Clough et al., 2021; Kim et al., 2006); probably through its regulatory role in hypoxia and ROS generation (Codo et al., 2020; Semenza, 2011), or even via the regulation of the reverse electron transport (RET) in the ETC, a well‐known source of ROS (Clough et al., 2021; Scialo et al., 2017). Released in response to increased oxidative stress, even before the appearance of inflammation (Ke & Costa, 2006), HIF‐1α regulates the expression of many genes that are implicated in maintaining oxygen homeostasis as well as glucose uptake (Ziello et al., 2007). Within the CNS, recent studies have found increased production of ROS and HIF‐1α expression in microglia when exposed to SARS‐CoV‐2 (Clough et al., 2021). The elevated ROS production in these cells could lead to increased oxidative stress, further impacting immune function and inflammatory response, as well as increased mitochondrial dysfunction and cellular dyshomeostasis (Ruan et al., 2020; Wing et al., 2021). Hypoxia, which has been described in COVID‐19 patients and is the main cause of increased HIF‐1α (Serebrovska et al., 2020), may per se trigger mitochondrial dysregulation, acidosis, altered mitochondrial membrane permeability, and eventually, insufficiency of ATP biosynthesis; ultimately inducing cell death (Bargiela et al., 2018). It is important to note that some of these results seem to indicate a SARS‐CoV‐2‐specific mechanism, as when similar experiments were conducted with H1N1 (a subtype of influenza A) and respiratory syncytial virus, the changes in glycolysis were not observed (Codo et al., 2020). Dysregulated apoptosis is present in the etiopathology of many human diseases, including the main neurodegeneration (Tait & Green, 2010; Xu et al., 2019), and in aging (Higami & Shimokawa, 2000). As previously mentioned, SARS‐CoV‐2 affects apoptosis in host cells, also within the CNS (Astuti & Ysrafil., 2020; Chan, 2020; Douaud et al., 2022; Ferreira et al., 2021; Iroegbu et al., 2020; Missiroli et al., 2020). [Correction added on 27 October 2022, after first online publication: Few incorrect reference citations in the above sentence were removed in this version]. In fact, increased apoptosis has been broadly described after SARS‐CoV‐2 infection, even if the exact mechanism that induce this effect still remains controversial and might be cell type‐dependent. For example, in HEK293 cells, it has been shown that SARS‐CoV‐2 increases apoptosis via the interaction of protein ORF7a of the virus with the Bcl‐XL protein, a well‐known anti‐apoptotic protein (Tan et al., 2007). Further, in the case of the pulmonary edema which is found in some COVID‐19 patients, the viral M protein interacted with the Bcl‐2 and ovarian killer protein (BOK, a non‐canonical pro‐apoptotic member of the Bcl‐2 family) (Yang et al., 2022), mediating apoptosis. Other research groups have shown the importance of the ORF3a protein on the activation of apoptosis in several mammalian cell lines (Ren et al., 2020). ORF3a is a transmembrane protein that induces the activation of caspase‐8, which then cleaves Bid (a pro‐apoptotic protein (Billen et al., 2008)) to tBID and triggers the release of Cytochrome C. The apoptotic effects of other SARS‐CoV‐2 proteins have also been investigated. The S protein of SARS‐CoV‐2 upregulated intracellular ROS generation in ACE2‐expressing human bronchial epithelial and microvascular endothelial cells (Li et al., 1867). The result of this rise in ROS generation is the suppression of the PI3K/AKT/mTOR signaling pathway, crucial in apoptosis signaling, and the consequent activation of autophagy, apoptosis, and inflammatory responses (Li et al., 1867). Increased ROS could also deleteriously directly impact mitochondrial physiology, further increasing apoptosis. ORF7b, an accessory protein of SARS‐CoV‐2 also affected the expression of several inflammatory cytokines, such as TNFα, IL‐6, and interferon β (IFN‐β) (Yang et al., 2021). Interestingly, under pathological conditions, TNFα is detected in other cells, such as neurons, where it is involved in the activation of the caspase cascade (Badiola et al., 2009). As mentioned, there are some common cellular features of SARS‐CoV‐2 on the CNS and those of neurodegeneration (especially age‐related), including the presence of mitochondrial dysfunction. Increased dementia is seen in COVID‐19 patients, even if the mechanism(s) explaining this observation remain poorly understood. Some authors have proposed that this could be mediated by the overlapping neuroinflammation and microvascular injury present in both AD and COVID‐19, maybe also involving the dysregulation of the antiviral defense genes (Zhou et al., 2021). A recent study reported a rapid (ten days) worsening of the symptoms of Parkinson's Disease (PD) after SARS‐CoV‐2 infection, which ultimately led to an increased rate of death (Hainque & Grabli, 2020). This suggests that the SARS‐CoV‐2 infection can also affect motor abilities of patients, and even some nonmotor symptoms also worsened in PD patients infected by the virus (Brown et al., 2020). However, the low numbers of patients included in these studies have limited the statistical power of the findings and complicated the interpretation and extrapolation of the results. In Genome‐Wide Association Studies, several immunity‐ and inflammatory‐linked genes have been associated with Alzheimer's Disease (AD) (Tosto & Reitz, 2013 ), and regulation of some of them changes in response to SARS‐CoV‐2 (Wang et al., 2022). For example, clusterin, also known as ApoJ, is one of the main apolipoproteins and an important chaperone in the human brain, where it has several functions. These include the regulation of diverse pro‐inflammatory cytokines, transport of lipids throughout the brain; and possibly its participation in the clearance of the amyloid β (Aβ) (Foster et al., 2019). Apart from AD, clusterin is also involved in the etiopathology of other major neurodegenerative disorders, such as amyotrophic lateral sclerosis, multiple sclerosis, and Huntington's disease (Foster et al., 2019). Its expression is promoted by stress‐related transcription factors (Foster et al., 2019) and it inhibits overall apoptosis (Zhang et al., 2005). This mechanism is negatively affected in neurodegeneration, where clusterin seems to be elevated (Lidström et al., 1998). Interestingly, similar effects have been found in COVID‐19 patients (Singh et al., 2021). While Aβ amyloids are characteristically present in the brain of AD patients, they also seem to be present in the cortexes of COVID‐19 patients. In a recent pre‐print publication, the authors showed increased presence of aggregated Aβ (and of hyperphosphorylated Tau, p‐Tau, another crucial amyloid in AD (Busche & Hyman, 2020)) in brains of both autistic patients and individuals with no underlying neuropsychiatric conditions (Shen et al., 2022). The authors stated that these aggregates were not found in age‐matched brains from both autistic and neurotypical individuals, who were tested negative for COVID‐19. While the number of individuals included in this study was again rather low, the authors conducted further studies using cellular samples to test their hypothesis. Specifically, they infected neurons derived from stem cells with SARS‐CoV‐2 virus and found that the deposition of Aβ and p‐Tau aggregates increased. Their data also suggested higher levels of apoptosis in these infected samples, evident by a rise in levels of caspase‐3 (Shen et al., 2022). In a study conducted using 3D human brain organoids, increased presence and redistribution of p‐Tau in cortical neurons was found in SARS‐CoV‐2 samples compared with the controls (Ramani et al., 2020). The authors reported that even if the virus seems to not to actively proliferate in neurons, its presence in the CNS elevates cell death rates. Corroborating these findings, increased p‐Tau (similar to that found in AD) was found after infection with SARS‐CoV‐2 (Reiken et al., 2022). This study used human tissues obtained from hearts, lungs, and brains of COVID‐19 patients and control group, and reported elevated oxidative stress, as well as dysregulated calcium‐handling proteins in patients compared with controls. However, the levels of the amyloid precursor protein (APP) were not affected in these samples. The authors proposed that the mechanism explaining the observed effects could involve the presence of leaky ryanodine receptor 2 (RyR2), after SARS‐CoV‐2 infection. RyR channels are closely related to calcium homeostasis in the human brain (Datta et al., 2021; Meinhardt et al., 2021). Mitochondria are a crucial regulator of calcium levels in mammalian cells. These levels are closely related to the status of bioenergetics and the presence of oxidative stress, as we and others have demonstrated (Borden et al., 2021; Solesio et al., 2020; Solesio, Demirkhanyan, et al., 2016a; Solesio, Elustondo, et al., 2016b). In agreement with these findings, using HCN‐2 cells (a pediatric human cerebral cortical cell line) and transcriptomic techniques, some authors have shown a dysregulation of the antioxidant response, after SARS‐CoV‐2 infection, in HCN‐2 cells. They also linked the effects of SARS‐CoV‐2 infection and the induction of senescence (Valeri et al., 2021). The presented studies have clear limitations. For example, those conducted in humans do not state the individuals' vaccination status, which may have an important impact on the viral load of the patients. In the studies conducted on animal and/or cellular models, for obvious reasons, the effects of the vaccine on the observed outputs cannot be evaluated. Additionally, pharmacological treatments against psychiatric or other symptoms may have an effect on certain infection routes, but they are not considered in many of the studies on patients. Lastly, increasing the sample size of the clinical studies will help to set the matter on a more reliable statistical footing.
Writing this review was the central objective of a graduate level Rutgers University course (taught by MES) during the Spring semester of 2022. All the students listed as authors in this manuscript contributed equally to this work during the semester. However, the students listed as first authors, voluntarily extended their work into the summer semester, participating in the final writing and review before submission. Several students also participated in the Coronavirus course (taught by AG) at Rutgers University during the Spring semesters of 2021 and 2022. In that course, the current aspects of SARS‐CoV‐2 genomics and lifecycle, infection, and disease were discussed. RTDC is a postdoctoral researcher at Solesio Laboratory, who contributed to the review after the semester was over. AG contributed to the final writing and reviewed the manuscript before submission. MES contributed to the selection of the manuscripts that the students discussed, structured the review, coordinated, and led the writing process, and reviewed the manuscript before submission.
The authors declare no conflict of interest. |
PMC9649609 | 36199214 | Chao Zhai,Nan Zhang,Xi‐Xia Li,Xi Chen,Fei Sun,Meng‐Qiu Dong | Fusion and expansion of vitellogenin vesicles during Caenorhabditis elegans intestinal senescence | 05-10-2022 | Aging,C. elegans,Senescence,Vesicle fusion,Vitellogenin,Yolk | Abstract Some of the most conspicuous aging phenotypes of C. elegans are related to post‐reproductive production of vitellogenins (Vtg), which form yolk protein (YP) complexes after processing and lipid loading. Vtg/YP levels show huge increases with age, and inhibition of this extends lifespan, but how subcellular and organism‐wide distribution of these proteins changes with age has not been systematically explored. Here, this has been done to understand how vitellogenesis promotes aging. The age‐associated changes of intestinal vitellogenin vesicles (VVs), pseudocoelomic yolk patches (PYPs), and gonadal yolk organelles (YOs) have been characterized by immuno‐electron microscopy. We find that from reproductive adult day 2 (AD 2) to post‐reproductive AD 6 and AD 9, intestinal VVs expand from 0.2 to 3–4 μm in diameter or by >3000 times in volume, PYPs increase by >3 times in YP concentration and volume, while YOs in oocytes shrink slightly from 0.5 to 0.4 μm in diameter or by 49% in volume. In AD 6 and AD 9 worms, mislocalized YOs found in the hypodermis, uterine cells, and the somatic gonadal sheath can reach a size of 10 μm across in the former two tissues. This remarkable size increase of VVs and that of mislocalized YOs in post‐reproductive worms are accompanied by extensive fusion between these Vtg/YP‐containing vesicular structures in somatic cells. In contrast, no fusion is seen between YOs in oocytes. We propose that in addition to the continued production of Vtg, excessive fusion between VVs and mislocalized YOs in the soma worsen the aging pathologies seen in C. elegans. | Fusion and expansion of vitellogenin vesicles during Caenorhabditis elegans intestinal senescence
Some of the most conspicuous aging phenotypes of C. elegans are related to post‐reproductive production of vitellogenins (Vtg), which form yolk protein (YP) complexes after processing and lipid loading. Vtg/YP levels show huge increases with age, and inhibition of this extends lifespan, but how subcellular and organism‐wide distribution of these proteins changes with age has not been systematically explored. Here, this has been done to understand how vitellogenesis promotes aging. The age‐associated changes of intestinal vitellogenin vesicles (VVs), pseudocoelomic yolk patches (PYPs), and gonadal yolk organelles (YOs) have been characterized by immuno‐electron microscopy. We find that from reproductive adult day 2 (AD 2) to post‐reproductive AD 6 and AD 9, intestinal VVs expand from 0.2 to 3–4 μm in diameter or by >3000 times in volume, PYPs increase by >3 times in YP concentration and volume, while YOs in oocytes shrink slightly from 0.5 to 0.4 μm in diameter or by 49% in volume. In AD 6 and AD 9 worms, mislocalized YOs found in the hypodermis, uterine cells, and the somatic gonadal sheath can reach a size of 10 μm across in the former two tissues. This remarkable size increase of VVs and that of mislocalized YOs in post‐reproductive worms are accompanied by extensive fusion between these Vtg/YP‐containing vesicular structures in somatic cells. In contrast, no fusion is seen between YOs in oocytes. We propose that in addition to the continued production of Vtg, excessive fusion between VVs and mislocalized YOs in the soma worsen the aging pathologies seen in C. elegans.
All metazoan animals synthesize in large quantities a tiny number of highly conserved, specialized proteins as provisions of nutrients to their progeny. These include vitellogenins (Vtg), the precursors of yolk proteins (YP) that are deposited in the eggs of nearly all oviparous animals (Sun & Zhang, 2015). In viviparous mammals, which have no Vtg genes (Zhou et al., 2021), this functional role is taken up in a sense by casein, the major protein component of milk. Interestingly, unlike casein proteins, Vtgs are not merely nutrients allocated to the young. For example, Vtg affects the division of labor in honey bees, that is, hive bees vs foragers (Amdam et al., 2003). In addition, Vtgs can scavenge free radicals, carry metal ions, and exert immunological activities in insects, fish, and corals (Du et al., 2017; Leipart et al., 2022; Sun & Zhang, 2015). There are reports of Vtg/YP participating in intergenerational signal transduction. In C. elegans, maternal age and early‐life starvation experience of the mother affect maternal provision of YPs to the progeny, which in turn affects growth, fecundity, and several other physiological traits of the progeny (Jordan et al., 2019; Perez et al., 2017). Another way by which YPs could influence intergenerational inheritance in C. elegans is to act as carriers of double‐stranded RNAs and deposit these messenger molecules from the mother to the progeny (Marré et al., 2016). There is also a connection between Vtg and aging in perennial social insects. In ants and honey bees, the production of Vtg is negatively correlated with that of juvenile hormone (Amdam et al., 2004; Amsalem et al., 2014), which prevents precocious metamorphosis during development and promotes aging of adults (Jindra et al., 2013). Reducing the honey bee Vtg protein levels by RNA interference (RNAi) elevates juvenile hormone and shortens lifespan (Nelson et al., 2007). Studies of the nematode C. elegans have found repeatedly that expression of the Vtg genes affects adult lifespan in a negative way. There are six Vtg genes in C. elegans, from vit‐1 to vit‐6. RNAi of one or more Vtg genes has been shown to extend the lifespan of wild‐type (WT) C. elegans by ~20% or less (Ezcurra et al., 2018; Murphy et al., 2003; Seah et al., 2016). A loss‐of‐function mutation of ceh‐60, a transcription factor that activates the expression of all six Vtg genes, extends lifespan by 40% (Dowen, 2019). Conversely, overexpression of vit‐2::gfp suppresses the longevity phenotype of daf‐2, glp‐1, and eat‐2 mutants, although it has no effect on WT lifespan (Seah et al., 2016). Mechanistic explanation of this negative relationship between Vtg expression and C. elegans lifespan is provided by a series of in‐depth investigations in recent years (Ezcurra et al., 2018; Kern et al., 2020; Murphy et al., 2003; Sornda et al., 2019; Wang et al., 2018). It is shown that C. elegans does not shut down Vtg production in the intestine after the worm lays the last eggs, which happens typically by AD 5 under the standard culture condition at 20°C. In fact, the yolk protein levels continue to increase up till AD 14, accompanied by atrophy of the intestine, growth of the so‐called uterine tumors, and a notable increase of pseudocoelomic yolk patches (previously called pseudocoelomic lipoprotein pools, renamed because these “pools” are too small in young adults) (Ezcurra et al., 2018; Kern et al., 2020; Sornda et al., 2019; Wang et al., 2018). Knocking down the Vtg transcripts is shown to ameliorate all aging pathologies described above, and to extend lifespan (Ezcurra et al., 2018; Sornda et al., 2019; Wang et al., 2018). Therefore, post‐reproductive vitellogenin production promotes senescent pathologies and accelerates aging (Ezcurra et al., 2018). Interestingly, it was recently found that this seemingly self‐harming act of Vtg production by post‐reproductive hermaphrodites is actually beneficial to the reproductive fitness of C. elegans, for the yolk vented by old worms can be consumed by larvae, and thus, promote larval growth (Kern et al., 2021). Although the senescent pathologies related to Vtg/YP have been investigated in detail in the C. elegans system by means of genetics or molecular biology, they have not been examined systematically using immuno‐electron microscopy (immuno‐EM). In the previous EM studies of C. elegans yolk proteins, the lipid membrane structures were not preserved in the best way, and the somatic tissues were missed as the focus was placed on the gonad and the pseudocoelom (Britton & Murray, 2004; Hall et al., 1999; Herndon et al., 2002; Paupard et al., 2001). Here, using high‐pressure freezing to preserve membrane structures and immuno‐gold labeling, we inspected age‐dependent changes of vitellogenin vesicles (VVs), pseudocoelomic yolk patches (PYPs), and yolk organelles (YOs) in multiple tissues. We find that in post‐reproductive hermaphrodites of AD 6 and AD 9, intestinal VVs, which are 0.2 μm in diameter on AD 2, fuse with one another at high frequencies and form VVs that are 3–4 μm in median diameter. Occasionally, intestinal VVs of AD 6 and AD 9 worms can exceed 10 μm in diameter and fill up the cytoplasmic space of intestinal cells. For PYPs, we identified two subtypes based on the density of anti‐YP170B gold particles. Only the high‐density ones accumulate in post‐reproductive animals. YOs in oocytes become slightly smaller, from ~0.5 μm in diameter on AD 2 to ~0.4 μm on AD 6 and AD 9. Unexpectedly, YOs, which should be limited to oocytes, are found mislocalized in the hypodermis, uterine cells, and the gonad sheath in post‐reproductive worms. Both YOs and the membrane‐less yolk are seen in high abundance in the tumor‐like masses or oocyte clusters in the uterus, confirming the notion that YP complexes fuel the growth of uterine tumors. Graphical summary and Table 1 summarize the age‐dependent changes of Vtg/YP‐containing structures as found in this and the earlier EM studies. The immuno‐EM documentation of Vtg/YP‐related senescent pathologies in this study confirms and extends earlier studies (Ezcurra et al., 2018; Wang et al., 2018) at the ultrastructural level. Our data indicate that Vtg/YP‐related senescent pathologies affect more tissues than previously thought and that vesicular fusion is a prominent and previously unknown aspect of those pathological phenotypes. The increase of VV‐occupied regions in the intestine and the accumulation of PYPs suggest that gut‐to‐yolk biomass conversion occurs both inside and outside of the intestine.
We examined the age‐associated morphological changes of vitellogenin/yolk protein (Vtg/YP) containing structures in wild‐type C. elegans by immuno‐EM. Specifically, we used an anti‐VIT‐1/2 antibody and indirectly conjugated colloidal gold particles to label vitellogenin vesicles (VVs), PYPs, and yolk organelles (YOs). We compared these three types of Vtg/YP structures seen in reproductive young adults (adult day 2 or AD 2) with their counterparts in post‐reproductive adults (AD 6 and AD 9) and quantified comprehensively the ultrastructural changes. For context, we illustrate the known developmental relationships between these three Vtg/YP structures in Figure 1. Briefly, vitellogenins are synthesized in the adult intestine, packed into VVs, and then exocytosed out of the intestine to become PYPs (Zhai et al., 2022). Through openings in the gonad sheath, oocytes take up pseudocoelomic yolk and store it in YOs (Hall et al., 1999). The characteristics of VVs, PYPs, and YOs in AD 2 hermaphrodites are presented in Figure 1, as a reference for comparison later with the same structures in older worms. Figure 2 displays the micrographs of VVs on AD 2, AD 6, and AD 9. On AD 2, VVs are 0.2 μm in median diameter (Figure 2a,b,e). On AD 6 and AD 9, the median diameter of VVs expanded to 3 and 4 μm, respectively (Figure 2c–e). These data indicate that the volume of VVs has enlarged by 3000–8000 times going from AD 2 to AD 6 and AD 9. Using lipid droplets in the same micrographs as a visual reference, AD 2 VVs look minuscule, whereas AD 6 and AD 9 VVs appear gigantic (Figure 2a–d). In the extreme case, AD 6 and AD 9 VVs can reach above 10 μm across (Figure 2e) and fill up almost the entire cellular space of an intestinal cell (Figure 2f,g). We also examined the size of VVs of the three gfp‐ and mCherry‐tagged vits strains (vit‐1::mCherry vit‐2::gfp, vit‐2::gfp vit‐3::mCherry, vit‐2::gfp; vit‐6::mCherry) via fluorescent microscopy. The size of VVs increased dramatically with age (from AD 1 to AD 9) (Figure S1a–o). For example, in vit‐1::mCherry vit‐2::gfp KI worms, the median diameter of VVs increased from 1.4 μm at AD 2 to 12.9 μm at AD 9 (Figure S1p), and the latter ones occupied almost the space of the intestinal cell (Figure S1m).
Fusion events between two or multiple VVs are readily detectable in post‐reproductive worms (Figure 3a–d). Quantification of the occurrence frequency of VVs caught in the middle of a fusion event indicates that on AD 6 and AD 9, 49.4% and 29.7% of the VVs captured in micrographs are, respectively, in the act of coalescing with one another (Figure 3e). In comparison, this number is only 6.2% for the VVs captured in micrographs on AD 2 (Figure 3e). These findings suggest that intestinal VVs grow by fusion in older worms.
Turning from the intestine to the pseudocoelom, we found that AD 2 PYPs were categorically distinct from the AD 6 and AD 9 counterparts. Although gold particle labeling is found throughout the pseudocoelom regardless of the age of the adult worm, the density of immuno‐gold particles attached onto AD 2 PYPs is markedly lower compared with AD 6 and AD 9 PYPs (median value: 19, 130, and 90 gold particles per μm2 for AD 2, AD 6, and AD 9, respectively) (Figure 4a–d). This suggests that the concentration of yolk proteins of AD 6 and AD 9 PYPs is more than four times as much as that of AD 2 PYPs. This confirms the previous observations of pseudocoelomic yolk accumulation in old worms by fluorescence microscopy (Ezcurra et al., 2018; Garigan et al., 2002; Herndon et al., 2002). Supporting this conclusion, analysis of the pseudocoelom by conventional EM showed that they have relatively low electron density (expressed as gray value in micrograph) on AD 2 and high electron density on AD 6 and AD 9 (median value 13.1, 31.3, and 31.6, respectively) (Figure 4e–h). Drawing a cutoff of 50 gold particles/μm2 for immuno‐EM (Figure 4d) or a gray value of 20 for conventional EM (Figure 4h), we classified PYPs into two categories: the low‐density ones are predominant on AD 2 and the high‐density ones are predominant on AD 6 and AD 9. To characterize the dynamic process of pseudocoelomic yolk accumulation with age, we examined PYPs in worms expressing VIT‐2::GFP and mCherry‐tagged VIT‐1/3/6. From AD 1 to AD 4, most PYPs look like droplets, and they can fuse to become bigger ones (Figure S2a–i, and Video S1). Video S1 shows that fusion is fast and dynamic. In post‐reproductive worms, PYPs are milk‐like and dispersed throughout the pseudocoelom (Figure S2j–o). Video S2 shows that milk‐like PYPs slosh back and forth as the worm moves. As more PYPs accumulate in the pseudocoelom of older animals, the pseudocoelomic space expands. Using longitudinal EM sections, we quantified the pseudocoelomic area relative to the area occupied by the worm and found that from AD 2 to AD 6 and AD 9, the relative pseudocoelomic area increased from 4% to 20% and 26%, respectively (Figure 4i–j). To summarize, in post‐reproductive C. elegans, while the intestine continues to produce vitellogenins and secret YP complexes to the pseudocoelom, large amounts of high‐density PYPs accumulate in and expand the pseudocoelom.
In contrast to the dramatic changes of VVs in the intestine and of yolk in the pseudocoelom, YOs found in oocytes remain unchanged by and large. The diameter of YOs decreases only very slightly, from an average of 0.5 μm on AD 2 to 0.4 μm on both AD 6 and AD 9 (Figure 5a–d). Post‐reproductive worms frequently develop uterine tumors, which originate from oocytes (Wang et al., 2018). We detected in uterine tumors immuno‐gold labeling in two types of structures: those that looked exactly like YOs and those that were amorphous and not enclosed by a membrane, resembling PYPs (Figure 5e–g). Although speculative, it seems plausible that these amorphous patches may originate from YOs after membrane rupture.
Apart from pseudocoelom, we observed that yolk substances also accumulated in the oviduct, which suggested that the yolk flood was overwhelming or the yolk endocytic capacity of old oocytes was compromised in old worms (Figure 6d,e). In AD 6 and AD 9 but not AD 2 hermaphrodites, we observed YO‐like structures in the hypodermal cells (Figure 6a–c), the gonad sheath cells (Figure 6e), and uterine cells (Figures 6f, 7d,e). We verified that these mislocalized Vtg/YP structures are enclosed by a lipid bilayer membrane (Figure 6c). These ectopic YOs in the hypodermis and uterine cells resemble the YOs found in oocytes, but can be much larger, sometimes reaching several micrometers in diameter (Figure 6a,b, Figure 7a,b,d,e).
We observed fusion of YOs not only in the intestine, but also in hypodermal cells and uterine cells (Figure 7a,b,d,e). As worms age, the frequency of YO appearing in hypodermal cells increases, as does the frequency of fusion of hypodermal YOs, from 0% on AD 2 to 20% on AD 6 and then to 27% on AD 9 (Figure 7c). The frequencies of YO appeared in uterine cells and sheath cells are low, and there are not enough images for quantification. Yolk organelles are abundant in the oocytes of post‐reproductive adult worms, but fusion between oocyte YOs was not observed. Among all the cell types examined, it seems that oocytes have a mechanism to prevent YO fusion, while somatic cells do not.
Intestinal atrophy during aging was previously measured by the relative intestinal width, that is, subtracting the width of the intestinal lumen from the width of the intestine and then normalizing it against the width of the worm body (Ezcurra et al., 2018; Kern et al., 2020). Here, using stereological analysis, we quantified age‐associated changes of the intestinal volume in both absolute and relative terms (Figure 8b). Stereology is a methodology for quantifying three‐dimensional characteristics by examining evenly spaced, two‐dimensional sections that sample through an entire three‐dimensional object (Ferguson et al., 2017). We take advantage of the Cavalieri principle to quantify the absolute volume of the tissue of interest. By analyzing the micrographs of 16–17 cross‐sections that were evenly spaced from the head to the tail of a worm, we measured the absolute volume of the body, the pseudocoelom, and the intestine (excluding the luminal space). Two worms each were examined on AD 2, AD 6, AD 9, and AD 18 (Figure 8a,b). As shown, the absolute volumes of the worm body (with or without the pseudocoelomic space subtracted), the pseudocoelom, and the intestine all peaked on AD 6 (Figure 8a,b). The relative volume of the intestine kept declining, from 24% on AD 2 to 12% on AD 18 (Figure 8b). Hence, intestinal atrophy is evident after AD 6, but arguable from AD 2 to AD 6 because the absolute volume of the intestine increases (from 6.1E5 to 7.3E5 μm3) whereas the relative volume decreases. Knowing that intestinal VVs grow dramatically from AD 2 to AD 6, with a 15‐fold increase in diameter or >3000‐fold increase in volume (Figure 2), we wondered whether this underlies the increase of the absolute volume of the intestine. From a random selection of immuno‐EM sections, we quantified the total intestinal area and the summed area of VVs in each section and calculated the relative VV‐free area. On AD 2, AD 6, and AD 9, the mean value of the percentage of VV‐free intestinal area is 99.4%, 86.6%, and 84.8%, respectively (Figure 8c). If the intestinal volume is corrected with the percentage of VV‐free intestinal area, then the enlargement of the intestine on AD 6 becomes marginal (6.1E5 and 6.3E5 μm3 for AD2 and AD 6, respectively). This suggests that the apparent enlargement of the intestine on AD 6 can be accounted for by the expansion of VVs. In other words, the external enlargement reflects internal deterioration. Some of the EM sections recorded impressive examples of intestinal deterioration. In Figure 8d, a representative EM section of an AD 2 worm shows the normal structures, and the top right, of an AD 6 worm, displays the increased intestinal area in a cross‐section compared with the one in AD 2. At the bottom left, a cross‐section of AD 6 indicated the VV enlargement counted for the age‐related increment in the absolute intestinal volume, while the VV‐free volume did not get increase. The AD 6 micrograph features two large VVs and an expanded pseudocoelom (Figure 8d). The two VVs almost fill up the entire cross‐section of the intestine. At the bottom right, the intestine shrinks dramatically in worms at AD 18. What is shown in the EM images of Figure 8d is consistent with the quantified results (Figure 8a–c).
The nematode C. elegans employs a precise mechanism to turn on the vit genes, so that their expression starts exactly at the beginning of adulthood and is usually limited to the intestine of a worm with a female gonad (Kimble & Sharrock, 1983; Klass et al., 1979). This makes sense because the purpose of vit genes is to generate a nutrient supply for the progeny, but this is costly for the mother. Analogously, expression of vitellogenins of mated C. elegans males may promote post‐mating death of those animals (Shi et al., 2017). Another intriguing phenomenon is that C. elegans hermaphrodites do not turn off these genes after the task of reproduction is completed. Post‐reproductive mothers continue to make yolk at the cost of intestinal atrophy and shortened lifespan, which has been characterized in detail (Ezcurra et al., 2018; Sornda et al., 2019), albeit not at the EM level. Here, using immuno‐EM, we observed a previously unreported aspect of this intestine‐to‐yolk biomass conversion: the intestinal atrophy starts internally before the intestine shrinks visibly. It has been shown that the relative intestinal width decreases by about one‐third from AD 1 to AD 7 (by ~22% from AD 1‐AD 4, and by ~25% from AD 1‐AD 8) (Sornda et al., 2019), and by half or more after AD 11 (Ezcurra et al., 2018). The intestinal atrophy occurs internally in a concealed manner in addition to the visible shrinkage. In other words, the intestinal atrophy is worse than how it looks on the outside, as VVs grow huge from ~0.2 to 3–4 μm across and occupy more and more space inside the intestine (Figure 8c). In contrast, YOs in oocytes are able to maintain a nearly constant size, with a diameter of 0.5 μm on AD 2 and 0.4 μm on AD 6 and AD 9. This seems to be a unique property of oocytes, because mislocalized YOs in AD 6 and AD 9 hypodermal cells or uterine cells can be several micrometers across, almost as big as the VVs in old intestinal cells. We find that VVs can grow bigger by fusion with one another (Figure 3). Fusion between mislocalized YOs in somatic tissues was also seen (Figure 7). In contrast, no fusion events were detected for YOs in oocytes, nor for YOs in uterine tumors, which originate from oocytes. We thus conclude that oocytes have a mechanism to prevent fusion between YOs, which is worth investigating in the future. RME‐2 is the only yolk protein receptor so far identified in C. elegans (Grant & Hirsh, 1999). Only oocytes express rme‐2 and only late‐stage oocytes have an abundance of RME‐2 on the cell surface (Grant & Hirsh, 1999). YOs form through RME‐2‐mediated endocytosis of yolk from the pseudocoelom. Normally, YOs are present only in oocytes and after fertilization, in embryos. It is unclear how the somatic cells of old worms acquire YOs. It could result from misexpression of RME‐2 in the hypodermis, gonad sheath, and uterine cells of old worms, or through an RME‐2 independent mechanism. Compared with the dramatic aging pathologies associated with Vtg/YP, there is only a modest lifespan extension of 20% or so by knocking down the expression of all six vitellogenin genes (Ezcurra et al., 2018; Murphy et al., 2003; Seah et al., 2016; Sornda et al., 2019). This could suggest that post‐reproductive production of yolk proteins may be less detrimental to the mother than one might expect from the associated, severe‐looking aging pathologies. Alternatively, we speculate that post‐reproductive production of yolk proteins, although costly to the mother, might also benefit the mother in some way. It has been shown that Vtgs scavenge oxidants, and enhance immunity in honeybees (Park et al., 2018). Although knocking down the vitellogenin genes did not make worms more resistant to oxidants (Sornda et al., 2019), in those experiments, RNAi started at L4 and the treated worms were assayed on adult day 1 (Sornda et al., 2019). Mutations of multiple vit genes have been shown to cause Vtg accumulation and ER stress in the intestine and also sensitivity to pathogenic P. aeruginosa (Singh & Aballay, 2017), but the immunity defects are likely a secondary phenotype of ER stress. In any case, it remains to be tested whether post‐reproductive Vtg production affords protection to the mother from oxidants or pathogens.
Caenorhabditis elegans was fed with E. coli OP50 on nematode growth medium (NGM) plates and cultured at 20°C. To produce synchronized cohorts of worms, 25 gravid hermaphrodites were put on a plate and allowed to lay eggs for 4 h before being taken away. Worms were regarded as one‐day‐old within 24 h after reaching sexual maturity. In this study, five worm strains were used, including wild‐type (N2), BCN9071 vit‐2(crg9070[vit‐2::gfp]) X, MQD2798 vit‐1(hq503[vit‐1::mCherry]) vit‐2(crg9070[vit‐2::gfp]) X, MQD2775 vit‐2(crg9070[vit‐2::gfp]) vit‐3(hq485[vit‐3::mCherry]) X, MQD2774 vit‐6(hq486[vit‐6::mCherry]) IV; vit‐2(crg9070[vit‐2::gfp]) X.
The rat polyclonal anti‐VIT‐2 antibody (diluted 1:100 for immuno‐EM labeling) was kindly provided by Dr. Xiao‐Chen Wang (Institute of Biophysics, Chinese Academy of Sciences, Beijing, China) (Liu et al., 2012). The epitope of the antibody is a recombinant protein VIT‐2 (83–620 amino acid)::6xHIS. The rabbit‐derived second antibody (anti‐rat) conjugated with 10‐nm colloidal gold (Sigma) is available as a commercial product.
The immuno‐EM workflow including sample preparation, sectioning, immuno‐labeling, and transmission electron microscopy (TEM) imaging are described clearly before (Zhai et al., 2022).
Methods of conventional EM sample preparation were developed by Li et al., (2017). Based on the differences in worm samples, we adjusted the methods slightly. High‐pressure freezing and freeze‐substitution were used for sample fixation and dehydration as described before (Zhai et al., 2022). The one difference is that the component of the substitution solution contains 1% OsO4, 0.1% uranyl acetate (UAc), and 98.9% acetone. After that, samples were put into 1 ml UAc saturated acetone solution and stained for 3.5 h at room temperature on a shaker. After dehydration, samples were infiltrated in SPI‐PON 812 resin mixture. The pure resin mixture was made by mixing 19.5 g SPI‐PON 812 (SPI‐CHEM), 10 g DDSA (SPI‐CHEM), 12 g NMA (SPI‐CHEM), and 1.5% (v/v) BDMA (SPI‐CHEM). Pure resin mixture and acetone were mixed as 1:3, 1:1, 3:1, and pure resin for sample filtration for 2 h, overnight, 24 h, and 48 h. The worm cakes were taken out from carriers carefully using a pair of needles on a 1 ml syringe under a stereo microscope. Then, nematodes were separated, and individually transferred into a cell of the embedding plate that was already filled with the pure resin mixture. Polymerization was performed in a 60°C oven for 48 h.
Serial sectioning and scanning electron microscopy (SEM) imaging were conducted according to the methods developed by Li et al., (2017). Before SEM imaging, the tapes carrying sections were adhered to SEM Cylinder Specimen Mounts (Electron Microscopy China, Cat. #DP16232) by carbon conductive double‐faced adhesive tape (NISSHIN EM Co. Ltd, Japan). The specimen mounts carrying samples were transferred under the SEM (FEI Helios NanoLab 600i) equipped with a CBS detector. Images were acquired by the software xT microscope control (FEI, version 5.2.2.2898) and iFast (FEI) with parameter settings of 2 kV accelerating voltage, 0.69 nA current, and 5 μs dwell time.
All quantitative data came from the manual measurement of cellular structures by ImageJ software. The pixel size of the TEM images was calibrated using a standard sample (diffraction grating replica with latex spheres, TED PELLA, INC, prod. #673) at different magnifications, and the pixel size has been reported before (Zhai et al., 2022). The quantitative data were analyzed by GraphPad Prism 8.4.3.
The reconstructed intestine and VVs in Figure 2g were based on 200 serial 70 nm‐thick sections. The methods for image alignment were described before (Li et al., 2017), and the aligned continuous images were processed with Imaris (version 9.0.1) for 3D reconstruction.
The methods of collecting whole worm serial SEM sections and SEM images were described by Li et al., (2017). For adults of different ages, each worm can be cut into over 10,000 serial sections in 50‐ to 70‐nm thick (the exact thickness of every sample and images analyzed are in Table S1 and File S1). Worms at AD 6 and AD 9 could even be cut into about 20,000 serial sections. For stereological analysis, 16 or 17 SEM images (attached in File S1) were selected using the systematic random sampling method. The equation was based on the Cavalieri principle: Volume = T × Areapoint × ∑points; where T means the interval thickness of every adjacent two sections sampled. T values of every worm are in Table S1. Stereological Analyzer (version 4.3.3) software was used to show an evenly distributed point grid covered on an EM image of a cross‐cut section. The Areapoint in the equation means the absolute area of each point represented. Here, Areapoint is set as 21.34 μm2 for worm body volume, and 5.34 μm2 for intestinal and pseudocoelomic volume. ∑points means the total number of points hits on the cellular structures of interest. Relative volume equals the absolute volume over the volume of the whole worm body.
M.‐Q.D. and F.S. supervised the project. M.‐Q.D. and C.Z. conceived the project, designed the experiments, interpreted data, and drafted this manuscript. C.Z., N.Z., and X.‐X.L. performed the EM sample preparation. C.Z. and N.Z. constructed worm strains. C.Z. performed electron microscopy imaging, light microscopy imaging, Western blotting, and data analysis. X.C aligned the continuous images of serial EM sections, and then N.Z reconstructed the 3D model of intestinal cellular structures in Figure 2g. All authors read and approved the final manuscript.
This work was funded by National Natural Science Foundation of China (NSFC‐ISF 32061143020 to M.‐Q.D., 31925026 to F.S., and 31501160 to X.‐X.L.), and The Ministry of Science and Technology of the People's Republic of China (institutional grants to NIBS, Beijing, a fund of the National High‐Level Talents Special Support Program to M.‐Q.D.), Beijing Municipal Science and Technology Commission (institutional grants to NIBS, Beijing and a fund for cultivation and development of innovation base to M.‐Q.D.).
No conflicts of interest exist in the submission of this manuscript.
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PMC9649611 | 36165462 | Ming‐Li Li,Shi‐Hao Wu,Bo Song,Jing Yang,Li‐Yuan Fan,Yang Yang,Yun‐Chao Wang,Jing‐Hua Yang,Yuming Xu | Single‐cell analysis reveals transcriptomic reprogramming in aging primate entorhinal cortex and the relevance with Alzheimer's disease | 27-09-2022 | aging,alzheimer’s disease,entorhinal cortex,primate,single cell | Abstract The entorhinal cortex is of great importance in cognition and memory, its dysfunction causes a variety of neurological diseases, particularly Alzheimer's disease (AD). Yet so far, research on entorhinal cortex is still limited. Here, we provided the first single‐nucleus transcriptomic map of primate entorhinal cortex aging. Our result revealed that synapse signaling, neurogenesis, cellular homeostasis, and inflammation‐related genes and pathways changed in a cell‐type‐specific manner with age. Moreover, among the 7 identified cell types, we highlighted the neuronal lineage that was most affected by aging. By integrating multiple datasets, we found entorhinal cortex aging was closely related to multiple neurodegenerative diseases, particularly for AD. The expression levels of APP and MAPT, which generate β‐amyloid (Aβ) and neurofibrillary tangles, respectively, were increased in most aged entorhinal cortex cell types. In addition, we found that neuronal lineage in the aged entorhinal cortex is more prone to AD and identified a subpopulation of excitatory neurons that are most highly associated with AD. Altogether, this study provides a comprehensive cellular and molecular atlas of the primate entorhinal cortex at single‐cell resolution and provides new insights into potential therapeutic targets against age‐related neurodegenerative diseases. | Single‐cell analysis reveals transcriptomic reprogramming in aging primate entorhinal cortex and the relevance with Alzheimer's disease
The entorhinal cortex is of great importance in cognition and memory, its dysfunction causes a variety of neurological diseases, particularly Alzheimer's disease (AD). Yet so far, research on entorhinal cortex is still limited. Here, we provided the first single‐nucleus transcriptomic map of primate entorhinal cortex aging. Our result revealed that synapse signaling, neurogenesis, cellular homeostasis, and inflammation‐related genes and pathways changed in a cell‐type‐specific manner with age. Moreover, among the 7 identified cell types, we highlighted the neuronal lineage that was most affected by aging. By integrating multiple datasets, we found entorhinal cortex aging was closely related to multiple neurodegenerative diseases, particularly for AD. The expression levels of APP and MAPT, which generate β‐amyloid (Aβ) and neurofibrillary tangles, respectively, were increased in most aged entorhinal cortex cell types. In addition, we found that neuronal lineage in the aged entorhinal cortex is more prone to AD and identified a subpopulation of excitatory neurons that are most highly associated with AD. Altogether, this study provides a comprehensive cellular and molecular atlas of the primate entorhinal cortex at single‐cell resolution and provides new insights into potential therapeutic targets against age‐related neurodegenerative diseases.
Improvements in public health and medical treatment have greatly contributed to longer human life spans. However, susceptibility to a host of diseases, including diabetes (Kalyani et al., 2017), stroke (Yousufuddin & Young, 2019), cancer (Aunan et al., 2017), and neurodegeneration (Hou et al., 2019), increases with age. How to achieve healthy aging and delay functional degeneration has become an important issue. The entorhinal cortex is situated in the medial temporal lobe, below the cerebral cortex near the hippocampus (Garcia & Buffalo, 2020). It forms circuits with different brain regions (Schultz et al., 2015), such as the hippocampus, amygdaloid nucleus, and neocortex (Gerlei et al., 2021). It processes information generated by the cerebral cortex and sends it to the hippocampus and amygdala, and vice versa. Thus, the entorhinal cortex is the “interface” for continuous information exchange between the hippocampus and neocortex (Sirota et al., 2003), and plays a crucial role in the acquisition, retrieval, and extinction of many forms of learning and memory (Coutureau & Di Scala, 2009; Eichenbaum et al., 2007). Pathological changes in the entorhinal cortex are associated with a variety of neurological diseases, particularly Alzheimer's disease (AD) (Khan et al., 2014). The entorhinal cortex is one of the first cortical brain regions to exhibit neuronal loss in AD (Braak & Braak, 1995; Leng et al., 2021).In addition, entorhinal cortex is among the first cortical fields to accumulate formation of β‐amyloid (Aβ) and neurofibrillary tangles (NFTs) in AD brains (Huijbers et al., 2014; Knopman et al., 2019). Therefore, a comprehensive understanding of the mechanisms underlying aging in the entorhinal cortex could provide insight into disease mechanisms and lead to therapeutic strategies. Non‐human primates (NHPs), such as cynomolgus monkeys, are similar to humans in terms of entorhinal cortex structure, anatomical location, and function (Garcia & Buffalo, 2020), Therefore, analysis of the entorhinal cortex isolated from monkeys will help to better understand the etiology of aging‐related memory loss and cognitive decline(M.L. Li et al., 2019). Given the cellular heterogeneity of the entorhinal cortex(Kim & Park, 2021), the application of single‐cell/nucleus RNA sequencing (scRNA‐seq/snRNA‐seq) could expand our understanding of how cell types are affected during entorhinal cortex aging (J. Li et al., 2021; H. Zhang et al., 2021; W. Zhang et al., 2020). Here, we obtained a single‐nuclear transcriptome atlas of the monkey entorhinal cortex as well as clarified gene and pathway alterations in a cell‐type‐specific manner during entorhinal cortex aging. Moreover, we integrated multiple neurodegenerative disease datasets based on single‐cell transcriptome data to clarify the correlation between disease and entorhinal cortex aging. This study advances our understanding of entorhinal cortex aging at the single‐cell level and elucidates potential therapeutic targets for interventions against neurodegenerative diseases in humans.
We collected the entorhinal cortex from young (7–8 years old) and aged (16–18 years old) cynomolgus monkeys (Macaca fascicularis) (Figure 1a; Table S1). The aged entorhinal cortices were characterized by higher senescence‐associated β‐galactosidase (SA‐β‐Gal) staining (Figure 1b), a common feature of senescent cells (Rodrigue et al., 2012). In addition, the accumulation of amyloid‐β (Aβ) deposits (immunostained by pan‐specific anti‐Aβ (4G8)) were significantly increased in aged entorhinal cortex (Figure S1 a) and overall neuronal density was significantly decreased in the aged entorhinal cortex (Figure S1 b). To analyze cell populations and molecular characteristics, we performed snRNA‐seq on the entorhinal cortex of the cynomolgus monkeys (Figure 1a). After cell quality control and filtering, 76,839 single cells were retained for downstream analyses. Using unbiased clustering and uniform manifold approximation and projection (UMAP) analysis, we identified seven cell types in the entorhinal cortex based on classic cell‐type‐specific markers (Figure 1c; Table S2), including excitatory neurons (ExN, 48,687), inhibitory neurons (InN, 15,178), oligodendrocytes (5859), oligodendrocyte precursor cells (OPCs, 2283), astrocytes (2066), microglia (2047), and endothelial cells (ECs, 719) (Figure 1d). Gene Ontology (GO) enrichment analysis of cell‐type‐specific marker genes revealed the characteristics of each cell type. For example, the axonogenesis pathway was enriched in ExN genes, chemical synapse transmission pathway was enriched in InN genes, neuron projection development pathway was enriched in oligodendrocyte genes, and inflammatory response‐related pathway was enriched in microglial genes (Figure 1e). These results revealed the cellular heterogeneity in entorhinal cortex. Furthermore, we identified the upstream regulators that drive cell differentiation in the entorhinal cortex. For example, the regulons of CREM and MEIS2, which are involved in cell differentiation and neurodegeneration (Mantamadiotis et al., 2002), were crucial regulators for neuronal lineage differentiation (ExN/InN) (Figure 1f, Table S3). The regulons of FLI1, which regulate inflammation‐associated genes (B. Chen et al., 2022), were identified as upstream regulators of microglia (Figure 1f, Table S3). We also identified several transcription factors (TFs), including TEAD4, SOX10, SOX8, and OLIG2, that regulate oligodendrocyte lineage differentiation (Figure 1f, Table S3). Together, our results clarify the cellular characteristic in the entorhinal cortex, providing the first single‐nucleus transcriptomic map of the entorhinal cortex in NHPs.
We next examined cell type‐specific transcriptional changes in the entorhinal cortex during aging. Comparing the relative cell proportions between young and aged NHP entorhinal cortices by multivariate test (Smillie et al., 2019), we found no significant changes in any cell types (Figure 2a; Figure S2). Next, we analyzed differentially expressed genes (DEGs) between young and aged entorhinal cortices according to cell type. The highest number of DEGs was observed in the neuronal lineage (Figure 2b). Moreover, by assessing the gene set scores of aging‐related genes across cell types in the entorhinal cortex (Aging Atlas, 2021), we found aging‐related genes were activated in multiple cell types derived from the aged entorhinal cortex, particularly neurons (Figure 2c). Together, our results suggest that neurons are most affected by entorhinal cortex aging.
DEGs analysis revealed significant changes in several key genes during entorhinal cortex aging (Figure 2d, Table S4). For instance, OLFM, which regulates neural progenitor maintenance and axon growth (Nakaya et al., 2012), was the most significantly up‐regulated gene in the ExNs, suggesting abnormal neurogenesis in the aged entorhinal cortex. APOE, which plays a role in lipid metabolism, Aβ aggregation, and tau damage (Yin & Wang, 2018), was up‐regulated in the astrocytes. B2M, which is a component of the MHC‐I molecule and accumulates during inflammation (Batista Muñoz et al., 2019), was up‐regulated in the microglia, thus suggesting elevated inflammation in the aged entorhinal cortex. These dysregulated genes may underlie the progressive functional decay of entorhinal cortex cells during aging. GO enrichment analysis of DEGs revealed the cellular pathways involved in entorhinal cortex aging (Figure 2e). The synapse signaling pathway was down‐regulated in all cell types, while pathways associated with up‐regulated genes exhibited diversity across cell types. For example, the ExN‐up‐regulated genes were primarily involved in protein folding in the endoplasmic reticulum and cellular chemical homeostasis, suggesting dysregulation of homeostasis in aged ExNs (Estébanez et al., 2018). Neuronal projection organization was up‐regulated in the oligodendrocytes and OPCs, and inflammation‐related pathways were up‐regulated in the microglia. Together, our result clarified the profile of transcriptomic reprogramming in aging primate entorhinal cortex. We next used single‐cell regulatory network inference and clustering (SCENIC) to map the gene regulatory networks governing entorhinal cortex aging (Aibar et al., 2017). FOXN3 is a key regulator of gene expression changes in microglia during entorhinal cortex aging (Figure 3a). GO enrichment analysis indicated that downstream DEGs targeted by FOXN3 were mainly involved in lipid metabolism and immune system processes (Figure 3b). In addition, we identified a series of TFs (ZMAT4, POU2F1, CLK2, TCF4, MEF2A, and SOX6) that regulate gene expression changes in the neuronal lineage (Figure 3a). GO enrichment analysis indicated that DEGs targeted by major hub TFs in neurons were mainly involved in chemical synaptic transmission and brain development (Figure 3b). These analyses identified the upstream regulons that drive cell‐type‐specific state transitions toward aging. We next investigated changes in intercellular communication during entorhinal cortex aging. Based on a comprehensive intercellular network of ligand–receptor interactions (Efremova et al., 2020), our results showed that interactions between cell types were globally decreased in the aged group compared with the young group (Figure 3c), indicating weakened intercellular communication in the aged entorhinal cortex. We also found the receptor–ligand pairs specifically present in aged entorhinal cortices were mainly involved in the neuroactive (neuroactive ligand–receptor interactions), pro‐inflammatory (regulation of leukocyte migration), and cell adhesion (positive regulation of cell adhesion) pathways (Figure 3d, Table S5). Thus, these pathways are proposed as mediators of abnormal crosstalk between cell types in the aged entorhinal cortex. Given the similar functions and frequent information exchange between the entorhinal cortex and hippocampus (Ku, Ku et al., 2021), we next asked whether similar aging mechanisms exist between these two brain regions. By performing comparative analysis of our results and recently published single‐cell hippocampal data from young and old cynomolgus monkeys (Hui Zhang et al., 2021), we found aging‐related DEGs exhibited significant overlapping rate in all cell types between the entorhinal cortex and hippocampus (Figure 3e), suggesting a convergent aging mechanism between these two regions in monkeys.
Entorhinal cortex aging is a major risk factor for cognitive and memory deficits (Hou et al., 2019). However, how the specific cell types are involved in neurological diseases remains unclear. Based on single‐cell data, we examined cell‐type‐specific expression of genes implicated in AD, Parkinson's disease (PD), and learning and memory disorders (LD, MD) (Aging Atlas, 2021). Results showed that AD‐related genes were significantly up‐regulated in all cell types of the aged entorhinal cortex (Figure 4a), suggesting that cell types in aged entorhinal cortices are widely associated with AD. In addition, genes implicated in PD, LD, and MD were significantly elevated in the aged entorhinal cortex neurons (Figure 4a). We next constructed a network integrating all cell types, aging‐related DEGs, and risk genes of AD, PD, LD, and MD to identify hub genes in the network (Figure 4b). We identified several disease‐risk genes that were abnormally expressed in specific cell types. Notably, APOD, a specific risk gene of LD, showed dysregulated expression in OPCs, while MAP2, a specific risk gene for PD, showed dysregulated expression in oligodendrocytes. In addition, several co‐risk genes of diseases were abnormally expressed in multiple cell types. For example, NPY (co‐risk gene for AD and MD) showed abnormal expression in both neurons and oligodendrocytes. These results demonstrate the complex network of neurological disease and entorhinal cortex aging at cell level. Given the key role of entorhinal cortex aging in AD, we performed an integrated analysis of AD‐associated DEGs (AD DEGs) from the human entorhinal cortex (obtained from previous snRNA‐seq data [Grubman et al., 2019]) and aging‐related DEGs from the entorhinal cortex in our study. We identified 166 shared genes between the AD DEGs and aging‐related DEGs (Table S6). These overlapping DEGs were primarily enriched in neurons (Figure 4c), suggesting that the neuronal lineage in the aged entorhinal cortex is more prone to AD. GO analysis showed that the up‐regulated overlapping DEGs were primarily related to synaptic signaling, whereas the down‐regulated overlapping DEGs were mainly associated with neurogenesis (Figure 4d).
The entorhinal cortex is one of the brain regions in which Aβ and NFTs are first detected in old age, both with and without mild cognitive impairment (Thaker et al., 2017). Accumulated Aβ peptides are the main component of senile plaques and are derived from the proteolytic cleavage of the large glycoprotein amyloid precursor protein (APP) (O'Brien & Wong, 2011). Several APP cleavage products are considered as potential contributors to AD, leading to neuronal dysfunction (G.f. Chen et al., 2017). The microtubule‐associated protein tau (MAPT) is responsible for encoding the tau protein, which is strongly implicated in the maintenance of microtubule and axonal transport functions (Strang et al., 2019). Hyperphosphorylated tau protein participates in the formation of NFTs, which characterize many neurodegenerative disorders, termed tauopathies (C.C. Zhang et al., 2016). Here, using our data, we assessed APP and MAPT expression in the aged entorhinal cortex at cell level. Results showed that APP and MAPT were widely expressed across all cell types in the entorhinal cortex (Figure 4e, f), but with more APP‐ and MAPT‐positive cells in the neuronal lineage relative to other cell types (Figure S3). Comparing the proportions of APP‐ and MAPT‐positive cells between the young and old groups, we found no significant change in the proportion of cells during entorhinal cortex aging (Figure 4g, h), but the expression levels of APP and MAPT were significantly elevated in most cell types in the aged entorhinal cortex (Figure 4i, j). Furthermore, we evaluated the expression levels of 270 proteins co‐localized with Aβ plaques and 543 proteins co‐localized NFTs based on laser capture microdissection (LCM) and label‐free quantitative (LFQ) proteomic analysis (Drummond et al., 2017; Drummond et al., 2020; Table S7). Results showed that the expression levels of proteins co‐localized with Aβ plaques and NFTs increased significantly in most cell types in the aged entorhinal cortices (Figure 4k, l). Thus, the elevated expression of APP and MAPT, rather than the number of positive cells expressing APP and MAPT, was likely the major cause of Aβ deposition and NFT formation in the aged entorhinal cortex.
To determine correlations between cell types and AD phenotypes and identify key cell types relevant to AD, we used Single‐Cell Identification of Subpopulations with Bulk Sample Phenotype Correlation (Scissor) (Sun et al., 2021), which can identify cell subpopulations associated with a given phenotype from single‐cell data. Scissor integrates phenotype‐associated bulk expression and single‐cell data by quantifying similarity between each single cell and each bulk sample, then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations (Sun et al., 2021). We applied Scissor to the scRNA‐seq data from the aged entorhinal cortex with bulk transcriptomes from AD and non‐AD entorhinal cortices (Jia et al., 2021) (Figure 5a). Results show that aged entorhinal cortical ExNs were more prone to AD than the other cell types (Figure 5b, c). Selective vulnerability is a fundamental feature of neurodegenerative diseases, in which different neuronal populations show a gradient of susceptibility to degeneration. ExNs are heterogeneous and include multiple subpopulations with distinct molecular and projection properties (Erwin et al., 2021). Therefore, we applied Scissor, guided by bulk samples with AD, to identify aggressive ExNs cell subpopulations within 31,617 ExNs from the scRNA‐seq dataset of the aged entorhinal cortex (Jia et al., 2021). These cells were separated into 9 clusters (Figure 5d, e), which demonstrated the heterogeneous nature of the ExNs. Scissor identified 1370 cells in the ExNs associated with the patients with AD (defined as Scissor_AD ExNs thereafter; Figure 5f). The Scissor_AD ExNs were mainly from clusters 4, 0, and 5 (Figure 5g). To understand the underlying transcriptional patterns of Scissor_AD ExNs, we compared the gene expressions of those cells with all other cells. As a result, 196 up‐regulated genes and 16 down‐regulated genes were differentially expressed in Scissor_AD ExNs over all other cells, respectively (Table S8). Notably, functional enrichment analysis also confirmed that the synaptic signaling and adenosine triphosphate (ATP) metabolic processes were activated in Scissor_AD ExNs (Figure 5h). To further demonstrate the phenotypic associations of the cell subpopulations identified by Scissor, we constructed molecular signatures based on the DEGs in Scissor‐identified cell subpopulations and used independent AD datasets to evaluate the functions of these signatures (Jia et al., 2021). As a result, the enrichment scores of the corresponding molecular signatures in A Scissor_AD ExNs were significantly higher in patients with AD than in normal controls (Figure 5i). Thus, this Scissor_AD ExNs subpopulation could play a vital role in AD progress. Taken together, Scissor analysis identified ExNs subpopulations that are most highly associated with AD, which could contribute to comprehending the underlying pathogenesis of AD and might facilitate disease diagnosis and therapy.
The entorhinal cortex plays a key role in cognition and memory and is an information exchange center for multiple brain areas (Gerlei et al., 2021). Abnormal entorhinal cortex function is implicated in multiple neurodegenerative diseases (Reagh et al., 2018). However, this brain region has received less attention than other regions such as the hippocampus and prefrontal cortex. In the current study, we used cynomolgus monkeys to construct a single‐cell map of the entorhinal cortex and identify age‐associated transcriptional changes. Our findings suggested widespread transcriptional changes across multiple cell types during entorhinal cortex aging, thus highlighting potential therapeutic targets for aging‐related neurodegenerative disorders. Our results showed that the synapse signaling‐related pathway was widely down‐regulated across cell types in the aged entorhinal cortex. Cell communication characterized by ligand–receptor interactions was also globally decreased during entorhinal cortex aging, suggesting an abnormal cell microenvironment in the aged entorhinal cortex. Inactive synapse signaling and weak cell communication in the aged entorhinal cortex would likely delay information transfer across cell types and contribute to eventual cognitive decline and memory loss. We also observed acute changes in the neuronal lineage during entorhinal cortex aging. Specifically, we found the highest number of aging‐related DEGs was observed in the neuronal lineage; genes associated with aging, AD, PD, MD, and LD were significantly more active in the neuronal lineages; and the overlap in aging‐DEGs and AD DEGs was most notable in the neuronal lineage. Therefore, our results confirmed that the neuronal lineage was more vulnerable to aging in the entorhinal cortex and more susceptible to neurological disease. We systematically explored the association between entorhinal cortex aging and AD (Reagh et al., 2018). Integrative analysis reveals a huge overlap between aging DEGs and AD DEGs across cell types. A hallmark of AD pathology is the accumulation of Aβ and phosphorylated tau (Iwata et al., 2014). In our study, APP and MAPT gene expression levels, as well as their coexistence, were significantly increased in most cell types in the aged entorhinal cortex, which is likely an important inducement of early AD. Furthermore, based on integration of bulk transcriptome data of AD, we identified ExNs subpopulations that are involved in synaptic signaling and ATP metabolic pathways are most highly associated with AD, which provide potential therapies for the diagnosis and treatment of AD.
The use of cynomolgus monkey's entorhinal cortex in this study was from the Jing J Kang biotechnology company (Approval number: SCXK 2018–0002). The source animals of these tissues were confirmed to have no disease history and natural death. Entorhinal cortex were stored in −80°C and washed in pre‐cooled PBSE (PBS buffer containing 2 mM EGTA) before the start of the experiment. Nucleus isolation was carried out using GEXSCOPE® Nucleus Separation Solution (Singleron Biotechnologies, Nanjing, China) refer to the manufacturer's product manual. Isolated nuclei were resuspended in PBSE to 106 nuclei per 400 μl, filtered through a 40 μm cell strainer, and counted with Trypan blue. Nuclei enriched in PBSE were stained with DAPI (1:1000) (Thermo Fisher Scientific, D1306). Nuclei were defined as DAPI‐positive singlets. Nuclear isolation was carried out using GEXSCOPE® Nucleus Separation Solution (Singleron Biotechnologies, Nanjing, China) per the manufacturer's product manual. Isolated nuclei were resuspended in PBSE to 106 nuclei/400 μl, filtered through a 40‐μm cell strainer, and counted with Trypan blue. Nuclei enriched in PBSE were stained with DAPI (1:1000; Thermo Fisher Scientific, D1306). Nuclei were defined as DAPI‐positive singlets.
The concentration of the single‐nucleus suspension was adjusted to 3 ~ 4 × 105 nuclei/mL in PBS and then loaded onto a microfluidic chip (GEXSCOPE® Single Nucleus RNA‐seq Kit, Singleron Biotechnologies). The snRNA‐seq libraries were constructed according to the manufacturer's instructions (Singleron Biotechnologies). The resulting snRNA‐seq libraries were sequenced on an Illumina HiSeq X10 instrument to a sequencing depth of at least 50,000 reads per cell with 150‐bp paired‐end (PE150) reads.
Raw reads were processed to generate gene expression matrices with scopetools (https://anaconda.org/singleronbio/scopetools). First, reads without polyT tails were filtered; then, cell barcodes and unique molecular identifiers (UMIs) were extracted. Adapters and polyA tails were trimmed before aligning reads to the pre‐mRNA reference (Ensemble, Macaca_fascicularis_6.0). Second, reads with the same cell barcode, UMI, and gene were grouped together to count the number of UMIs per gene per cell. Cell number was then determined according to the “knee” method, a standard single‐cell RNA‐seq quality control approach used to determine the threshold at which cells are considered valid for experimental analysis. High‐quality barcodes are located to the left of the inflection (“knee”) point and retained for further analysis, while low‐quality barcodes (i.e., relatively low numbers of reads) are located to the right and excluded from further analysis.
We removed cells that had either <200 or >4000 expressed genes. Low‐quality/dying cells often exhibit extensive mitochondrial contamination. Therefore, we applied the “PercentageFeatureSet” function in the Seurat R package (version = 4.0) to calculate the percentage of counts originating from mitochondrial genes (Figure S4), with cells showing a mitochondrial ratio greater than 1.5% discarded. Finally, 76,839 cells were obtained for downstream analysis. Harmony was used as the batch effect removal method to reduce heterogeneity among cells of an individual. We used Seurat v4.0 to normalize expression matrices using the NormalizeData and ScaleData functions. The FindVariable function was then applied to select the top 200 variable genes and perform principal component analysis (PCA). The first 10 principal components (PCs) and resolution 1.3 were used with the FindClusters function to generate 32 cell clusters. To assign one of the seven major cell types to each cluster, we scored each cluster by the normalized expression levels of the following canonical markers: astrocytes (AQP4, ADGRV1, GPC5, RYR3), ECs (CLDN5, ABCB1, EBF1), ExNs (CAMK2A, CBLN2, LDB2), InNs (GAD1, LHFPL3, PCDH15), microglia (C3, LRMDA, DOCK8), oligodendrocytes (MBP, PLP1, ST18), and OPCs (PDGFRA, MEGF11, OLIG1). The clusters assigned to the same cell type were grouped together for the following analyses. The results were manually examined to ensure the correctness of the results and visualized using UMAP. Marker genes for each cell type were identified using the “FindAllMarkers” function with an adjusted p < 0.05 and |logFC| > 1 cutoff.
DEGs for every cell type between young and old samples were identified with the “FindMarkers” function in Seurat R package using the Wilcoxon test (adjusted p < 0.05 and |logFC| > 0.25 threshold).
we used the method reported by Smillie et al. (Smillie et al., 2019), to identify changes in cell proportions between young and aged NHP entorhinal cortex. We applied Dirichlet‐multinomial regression model, which tests for differences in cell composition between young and aged NHP entorhinal cortex. This regression model and its associated p values were calculated using the “DirichReg” function in the DirichletReg R package.
Transcription factor regulatory network analysis was performed using the pySCENIC workflow (v1.1.2.2) with default parameters. We downloaded hg19 TFs using RcisTarget (v1.6.0) as a reference. Gene regulatory networks were inferred with GENIE3 (v1.6.0).
The clusterProfiler R package and Metascape were used to perform GO term analysis (http://metascape.org/gp/index.html) (v3.5) (Zhou et al., 2019). Results were visualized using the ggplot2 R package (https://ggplot2.tidyverse.org/) (v3.2.1).
Cell–cell communication analysis was performed using Cell‐PhoneDB (v1.1.0) (Efremova et al., 2020). Only receptors and ligands expressed in more than 10% of cells of any type from either young or old samples were further evaluated. Only those with p < 0.01 were used for cell–cell communication prediction between any two cell types.
Gene sets related to aging‐related diseases were previously reported (Aging Atlas, 2021). Gene set scores were acquired by analyzing the transcriptome of each input cell against the aforementioned gene sets using the Seurat function “AddModuleScore.” Changes in scores between young and old samples were analyzed using the ggpubr R package via the Wilcoxon test (https://github.com/kassambara/ggpubr) (v0.2.4).
Three data sources are used for Scissor input: that is, single‐cell expression matrix, bulk expression matrix, and phenotype of interest. Given the above inputs, we used Scissor to select the phenotype‐associated cell subpopulations, which were fit by a binomial regression model (family = “binomial”). We set the parameter alpha (α) = 0.01 to choose AD‐related cells.
Yuming Xu leads the project. Yuming Xu and Ming‐Li Li and Shi‐Hao Wu designed and conceived the study. Ming‐Li Li, Bo Song, Jing Yang and Li‐Yuan Fan. Jing‐Hua Yang drafted the manuscript. Ming‐Li Li performed data analysis. Shi‐Hao Wu, Yang and Yun‐Chao Wang performed the functional experiments.
The authors declare no competing financial interest.
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PMC9649613 | Florian Krach,Judith Stemick,Tom Boerstler,Alexander Weiss,Ioannis Lingos,Stephanie Reischl,Holger Meixner,Sonja Ploetz,Michaela Farrell,Ute Hehr,Zacharias Kohl,Beate Winner,Juergen Winkler | An alternative splicing modulator decreases mutant HTT and improves the molecular fingerprint in Huntington’s disease patient neurons | 10-11-2022 | Huntington's disease,Molecular neuroscience,Gene regulatory networks | Huntington’s disease (HD) is a neurodegenerative disorder caused by poly-Q expansion in the Huntingtin (HTT) protein. Here, we delineate elevated mutant HTT (mHTT) levels in patient-derived cells including fibroblasts and iPSC derived cortical neurons using mesoscale discovery (MSD) HTT assays. HD patients’ fibroblasts and cortical neurons recapitulate aberrant alternative splicing as a molecular fingerprint of HD. Branaplam is a splicing modulator currently tested in a phase II study in HD (NCT05111249). The drug lowers total HTT (tHTT) and mHTT levels in fibroblasts, iPSC, cortical progenitors, and neurons in a dose dependent manner at an IC50 consistently below 10 nM without inducing cellular toxicity. Branaplam promotes inclusion of non-annotated novel exons. Among these Branaplam-induced exons, there is a 115 bp frameshift-inducing exon in the HTT transcript. This exon is observed upon Branaplam treatment in Ctrl and HD patients leading to a profound reduction of HTT RNA and protein levels. Importantly, Branaplam ameliorates aberrant alternative splicing in HD patients’ fibroblasts and cortical neurons. These findings highlight the applicability of splicing modulators in the treatment of CAG repeat disorders and decipher their molecular effects associated with the pharmacokinetic and -dynamic properties in patient-derived cellular models. | An alternative splicing modulator decreases mutant HTT and improves the molecular fingerprint in Huntington’s disease patient neurons
Huntington’s disease (HD) is a neurodegenerative disorder caused by poly-Q expansion in the Huntingtin (HTT) protein. Here, we delineate elevated mutant HTT (mHTT) levels in patient-derived cells including fibroblasts and iPSC derived cortical neurons using mesoscale discovery (MSD) HTT assays. HD patients’ fibroblasts and cortical neurons recapitulate aberrant alternative splicing as a molecular fingerprint of HD. Branaplam is a splicing modulator currently tested in a phase II study in HD (NCT05111249). The drug lowers total HTT (tHTT) and mHTT levels in fibroblasts, iPSC, cortical progenitors, and neurons in a dose dependent manner at an IC50 consistently below 10 nM without inducing cellular toxicity. Branaplam promotes inclusion of non-annotated novel exons. Among these Branaplam-induced exons, there is a 115 bp frameshift-inducing exon in the HTT transcript. This exon is observed upon Branaplam treatment in Ctrl and HD patients leading to a profound reduction of HTT RNA and protein levels. Importantly, Branaplam ameliorates aberrant alternative splicing in HD patients’ fibroblasts and cortical neurons. These findings highlight the applicability of splicing modulators in the treatment of CAG repeat disorders and decipher their molecular effects associated with the pharmacokinetic and -dynamic properties in patient-derived cellular models.
Huntington’s disease (HD) is a progressive neurodegenerative disorder caused by CAG-repeat expansion in the coding region of the Huntingtin (HTT) transcript, leading to an elongated polyglutamine (poly-Q) stretch in HTT. While individuals with 40 and above CAG-repeats in one allele will develop HD, a repeat length below 36 is non-pathogenic. Intermediate repeat lengths in the range between 36 and 39 may cause HD with incomplete penetrance. The elongated poly-Q, mutant HTT (mHTT) is suspected to exhibit a toxic gain of function resulting in neuronal toxicity. Clinically, HD is characterized by the triad of motor dysfunction, psychiatric symptoms, and cognitive deficits. Motor symptoms, most prominently chorea, are related to the degeneration of striatal medium spiny neurons and subsequently cortical projection neurons. Interestingly, cortico-striatal projections are affected years prior to disease onset. Specifically, degeneration of pyramidal neurons in the primary motor cortex and anterior cingulate cortex lead to more pronounced motor and mood impairments, respectively. So far, there is no causal therapy to improve or even halt the disease course of HD. However, a number of recent clinical trials focus on small compounds lowering HTT levels. One of the latest clinical trials investigates the effectiveness of the alternative splicing (AS) modulator Branaplam (previously called LMI070, NVS-SM1) in HD (NCT05111249). Branaplam was initially designed for spinal muscular atrophy (SMA) and promotes inclusion of exon 7 in the SMN2 transcript via stabilization of the pre-mRNA - U1 snRNP complex. Here, we describe Branaplam’s mechanism of action and effects in HD. We develop a HD patient-derived cellular platform to investigate mHTT levels using a validated HTT assay. We demonstrate that Branaplam is able to reduce mHTT levels. Furthermore, Branaplam restores mHTT-induced aberrant splicing, an important molecular feature of HD, in transcripts that are not directly targeted by Branaplam. We evaluate the precise pharmacokinetic and -dynamic properties of Branaplam in primary fibroblasts and iPSC-derived cortical neurons of Ctrl and HD patients. Lastly, we explore the small molecule’s potential to revert molecular phenotypes of HD patients’ neurons.
We chose to use various patient-derived non-neuronal and neuronal cells to model HD in vitro (Fig. 1a). Besides four Ctrls, we recruited four HD patients originating from three distinct families (Table 1 and Supplementary Fig. 1a). The CAG repeats on the affected allele ranged from 39 to 57 (Table 1). Clinically, all patients presented motor symptoms. Hence, we reprogrammed fibroblasts into iPSC and subsequently differentiated them into cortical neurons using a previously published dual-SMAD-inhibition-based protocol. At day 25 of differentiation, NESTIN+/PAX6+ cortical progenitors were observed (Fig. 1b and Supplementary Fig. 1b, c), and from day 35 on deep layer cortical neurons positive for CTIP2 were apparent (Fig. 1c, d and Supplementary Fig. 1d, e). No differences in differentiation potential between Ctrl and HD were detected (Fig. 1b, c). Next, we determined the total (tHTT) and mHTT levels in cellular homogenates. We used a mesoscale discovery (MSD) assay with an electrochemiluminescent readout. MSD assays are applied to semi-quantitatively determine changes in protein levels. An N-terminally-binding HTT antibody (2B7) captures HTT and an antibody binding a central part of HTT downstream of the poly-Q tract (D7F7) or an antibody with preferred binding to elongated poly-Q (MW1) is used to quantify tHTT (2B7/D7F7) and mHTT (2B7/MW1) via a SULFO-TAG, respectively. The resultant HTT signal values are back-calculated to a standard of recombinant HTT with 23 (Q23) and 73 (Q73) glutamines (Fig. 1e). Importantly, the obtained signal values of the tHTT assay (2B7/D7F7) and mHTT assay (2B7/MW1) in a given sample cannot be directly set into relation with each other (e.g., no mHTT/tHTT ratio calculations possible) due to different properties of both assays (explained in detail in “Methods”). While tHTT levels were unchanged in all analyzed cell types, a substantial increase in signal in the mHTT assay was observed in HD-patient iPSC, cortical progenitors, and cortical neurons (Fig. 1f–i). Hence, our cellular platform is suitable to detect changes in mHTT levels in non-neuronal and neuronal cells.
Aberrant AS events are present in patients’ primary motor cortex and striatum. Therefore, we investigated whether HD-patients’ cells also exhibit similar molecular fingerprints by performing deep long read RNA-sequencing of HD and Ctrl fibroblasts (four patients vs. four healthy controls) and iPSC-derived cortical neurons (3 patients vs. 3 healthy controls) (Fig. 2a). We identified 306 and 1437 significantly (FDR < 0.05; inclusion level difference > 0.1) differentially spliced exons in fibroblasts (Fig. 2b) and iPSC-derived cortical neurons of HD patients (Fig. 2c), respectively, consisting mostly of cassette exons (SE) (Fig. 2d, e). Eleven included and excluded events were shared between HD fibroblasts and HD cortical neurons (Fig. 2f). Next, we asked which upstream AS factors may regulate these changes. Therefore, we made use of the ENCODE-enhanced cross-linking and immunoprecipitation (eCLIP) sequencing experiments, providing RNA-binding profiles of a large number of RNA-binding proteins (RBPs). We investigated whether binding of these proteins to the RNA of HD AS sites encompassing the region from the upstream exon start site to the downstream exon end was enriched over background. HD-AS events in fibroblasts and iPSC-cortical neurons were enriched for distinct RBPs (Fig. 2g, h). Interestingly, HD-AS events in iPSC-cortical neurons were enriched for binding sites of RBFOX2, TIA1, and U2AF2 (Fig. 2h). Importantly, identical RBPs were previously implicated in aberrant AS in HD postmortem tissue. Next, we asked whether the enriched RBPs exhibited a change in gene expression. Elevated levels of HLTF and a reduction of PCBP2 were found in fibroblasts, whereas a reduction of U2AF1 and U2AF2 was detected in cortical neurons of HD patients (Supplementary Fig. 2a, b). A reduction of U2AF2 was previously reported in HD. Protein insolubility is an important feature in neurodegenerative diseases and we have recently shown that changed biochemical solubility properties of certain RBPs are associated with aberrant AS in amyotrophic lateral sclerosis (ALS). We investigated the soluble and insoluble protein fractions of Ctrl and HD cortical neurons of five candidate RBPs that are enriched in cortical neuron HD AS events (ILF3, QKI, U2AF2, RBFOX2, and TIAL1) (Supplementary Fig. 3a–f). ILF3, U2AF2, RBFOX2, and TIAL1 did not exhibit significant changes in the level of solubility. A modest but significant increase in soluble QKI was observed in HD cortical neurons (Supplementary Fig. 3c). This suggests that aberrant RNA missplicing is present in HD patients’ fibroblasts and to a larger extent in iPSC-derived cortical neurons. These changes may be due to altered gene expression and biochemical properties of RBPs. In conclusion, HD patients’ iPSC cortical neurons recapitulate aberrant AS as a major molecular pattern of HD.
Therapeutic strategies in HD aim at lowering HTT levels to reduce mHTT toxicity. A recently initiated phase II study repurposes the small molecule AS modulator Branaplam (LMI070, NCT05111249). Hence, we sought to investigate its effects to specifically reduce mHTT levels and its impact on Ctrl and HD-derived cells (Fig. 3a). Branaplam exhibited dose-dependent effects in lowering HTT levels in fibroblasts and iPSC (Fig. 3b, c, tHTT). Similar trends were visible for mHTT in HD patients’ cells (Fig. 3b, c, mHTT). Interestingly, Branaplam treatment did not induce cellular toxicity in fibroblasts and iPSC (Fig. 3b, c, toxicity). In order to investigate the pharmacokinetic properties of Branaplam in vitro, we performed a dose–response experiment in human cortical progenitor cells. The half-maximal inhibitory concentration (IC50) of Branaplam was consistently below 10 nM, reducing total as well as mHTT levels without affecting cellular toxicity (Fig. 3d). A concentration of 10 nM reduced tHTT levels in iPSC-derived cortical neurons by 38.8% and mHTT levels in HD patients by 21.8% without inducing toxicity (Fig. 3e). To determine potential toxic effects of Branaplam on neuronal subtypes, we investigated Caspase-3/7 activation in deep layer CTIP2-positive neurons. Branaplam did not induce cell death in CTIP2+ and CTIP2- neurons (Fig. 3f and Supplementary Fig. 4a–c). In addition, we explored the impact of Branaplam on the proliferation of SOX2+ neural progenitor cells via EdU incorporation assay. No changes were observed in proliferation upon 3 days Branaplam treatment (Supplementary Fig. 4d, e). In summary, these findings suggest that Branaplam efficiently reduces total and mutant HTT protein levels in various Ctrl and HD patient-derived cell types without inducing toxicity and altering proliferation.
Next, we explored how the splicing modulator Branaplam leads to a reduction in HTT protein levels. We performed a streamlined AS analysis in fibroblasts and cortical neurons of controls and HD patients with and without Branaplam treatment to decipher Branaplam’s targets, sequence preferences, and effects on gene expression in an unsupervised manner (Fig. 4a). Significantly differentially spliced events upon Branaplam treatment in all four cohorts (Ctrl fibroblasts, HD fibroblasts, Ctrl cortical neurons, HD cortical neurons) were grouped using k-means, resulting in 10 distinct clusters (cluster 0–cluster 9) (Fig. 4b). Cluster 6 and cluster 9 exhibited coherent, unidirectional alternative splicing changes (exon inclusion) in all four cohorts (Fig. 4c, d and Supplementary Fig. 5a). Interestingly, more than 50% of events in both clusters were novel splice sites (novelSS) that represent previously non-annotated exons (Fig. 4c, d, pie charts). In contrast to the annotated exons, the 55 novelSS exons are predominantly excluded in untreated cells and only become apparent after Branaplam treatment (Fig. 4e and Supplementary Fig. 5b). We compared our Branaplam-induced exons (clusters 6 and 9) to Branaplam-induced exons that have been previously characterized in HEK293 cells. Interestingly, out of 25 events discovered by Monteys and colleagues to be exclusively regulated by Branaplam, 15 were also present within the novelSS exons induced by Branaplam (Supplementary Fig. 5c). This suggests a very high validity and robustness of the present analyses and therefore the identified events. Next, we analyzed if those novel, Branaplam-dependent exons exhibited enrichment of specific sequences around their 5’ and 3’ splice site by analyzing 4mer, 6mer and 8mer sequences (Fig. 4f, g). Interestingly, even at the 8mer level we identify enriched sequences at the 3’ splice site (TTCAGTTT) and 5’ splice site (AGAGTAAG) (Fig. 4g), suggesting, at least in part, a sequence-dependent mode of action of Branaplam. NovelSS exons may contain STOP codons or result in an out-of-frame transcript potentially leading to nonsense-mediated RNA decay and mRNA degradation. Therefore, we analyzed gene expression changes of the transcripts that contain the 55 newly identified Branaplam-induced exons. Three transcripts (DLGAP4, RCC1, and KDM6A) exhibited a consistent increase in gene expression (Fig. 4h). Interestingly, we identified that the levels of FHOD3, PAPD4 and also HTT were consistently reduced in all four comparisons (Fig. 4h). We further evaluated the HTT transcript and detected an inclusion of a 115b long frameshift-inducing exon with 2 STOP codons between exon 49 and exon 50 upon Branaplam treatment (Fig. 5a–c). This splice site contained the previously identified Branaplam associated 5’ splice site sequence AGAGTAAG (Fig. 5a). We further validated the integration of this exon by RT-PCR with primers annealing to the flanking exons (Fig. 5a, blue arrows). A consistent integration of this exon in all analyzed Ctrl and HD patient fibroblasts and cortical neurons was observed (Fig. 5d–g), leading to reduced HTT mRNA levels (Fig. 5h, i). In summary, these findings suggest that Branaplam promotes novelSS exon inclusion with distinct sequence preferences. This is present in HTT transcripts leading to a nonsense-mediated RNA decay isoform and a profound reduction of HTT levels.
We have identified aberrant AS in HD fibroblasts and iPSC-derived cortical neurons as a molecular HD fingerprint (Fig. 2). Next, we investigated if Branaplam treatment, and the accompanied reduction in mutant HTT levels, improves the AS deficiency in HD (Fig. 6a). Branaplam significantly reduced the absolute inclusion level differences of HD AS events by 27.6% in fibroblasts and 28.6% in iPSC-derived cortical neurons (Fig. 6b, c). In total, 53.2% of HD AS events in fibroblasts (Fig. 6d–f) and 47.9% of HD AS events in iPSC-derived cortical neurons (Fig. 6g–i) exhibited an absolute inclusion level difference below our threshold of 0.1 upon Branaplam treatment. This suggests that Branaplam ameliorates a prominent molecular signature in HD iPSC-derived cortical neurons. Branaplam itself did not directly target the HD-specific AS events (Supplementary Fig. 6a, b). The rescued AS events were preferentially bound by certain RBPs (Supplementary Fig. 6c, d), e.g., rescued events in cortical neurons were more prominently bound by QKI than non-rescued events (Supplementary Fig. 6d). This suggests that Branaplam may revert HD AS events indirectly by reduction of mHTT RNA and protein levels consequently altering RBPs’ functions.
This study describes the reduction of mHTT levels in HD patients’ fibroblasts and iPSC-derived cortical neurons by application of the splicing modulator Branaplam without inducing cellular toxicity. Specifically, we show that aberrant AS is ameliorated following Branaplam treatment. Various approaches that were and still are under clinical development for the treatment of HD focus on lowering HTT levels. This includes antisense oligonucleotides (ASOs) (Generation HD 1: NCT03761849; Precision HD-1: NCT03225833; Precision HD-2: NCT03225846) and adeno-associated virus (AAV)-mediated gene therapeutic delivery of RNAi-based machineries (NCT04120493), currently both on hold. Both approaches are dependent on repeated intrathecal or stereotactic injections. In contrast, Branaplam is an orally available small molecule, much easier applicable in HD patients. Here, we delineated the mechanism of action of Branaplam in two distinct cell types in Ctrl and HD patients. Branaplam was originally designed to promote inclusion of exon 7 in the SMN2 transcript as an intervention for SMA. We reveal that Branaplam also induces inclusion of multiple non-annotated novel exons, preferentially exons with AGAGTAAG sequences at their 5’ splice site. Among these, there is a frameshift-inducing exon in the HTT transcript, leading to a profound lowering of tHTT and mHTT levels in Ctrl and HD patient cells. A recent study identifies a similar mechanism of action of Branaplam in a permanent neuroblastoma cell line of human origin. Furthermore, we confirmed the mechanism of action in multiple Ctrl and HD patient cell types, including iPSC-derived cortical neurons. Additionally, we precisely defined the pharmacokinetic properties of the small molecule AS modifier using validated quantitative assays for of tHTT and mHTT that showed an IC50 consistently below 10 nM in Ctrl and HD patient cells. We did not observe toxic effects of Branaplam in vitro. This included no change human cortical progenitor proliferation, confirming previous studies that investigated proliferation in the subventricular zone of dogs and rats upon Branaplam administration. Furthermore, the efficacy of Branaplam for HD was recently demonstrated by phenotype improvements in a HD mouse model upon administration. We further underscore the effectiveness of this molecule by providing compelling evidence that Branaplam ameliorates a molecular fingerprint in a human HD in vitro model. However, it is of speculative nature if rescue of these AS events contributes to the clinical efficacy of the drug. On a broader scope, we emphasize the applicability of AS modulators to alter pathological protein levels by integration of non-annotated exons and restore molecular fingerprints using primary fibroblasts of HD patients and patient iPSC-derived cortical neurons. Our iPSC-based model recapitulates aberrant AS as a feature of HD that has been previously observed in postmortem tissue. Six proposed candidate AS events in postmortem tissue (within the transcripts of CCDC88C, KCTD17, SYNJ1, VPS13C, TRPM7, SLC9A5) are not recapitulated in our iPSC-based neuronal dataset. However, there appears to be a similarity of the RBPs driving aberrant AS in the present iPSC neuronal and previously published postmortem tissue, suggesting the possibility of shared changes in RBP function and RNA processing in both systems. Aberrant AS is an interesting phenotype in the context of neurodegenerative diseases and has been most frequently studied in ALS patients. Widespread AS changes are observed in postmortem tissue. This phenotype was also observed in ALS iPSC models that are driven by biochemical and cellular alterations in specific RBPs. Our findings give only a glimpse into AS in HD, but show that iPSC cortical neurons may be a powerful model to study this distinct HD-associated phenotype. However, there is an urgent need for future studies thoroughly dissecting the origin of aberrant AS in HD.
The generation and use of human iPSCs were approved by the Institutional Review Board (Nr. 4120 and 259_17B: Generierung von humanen neuronalen Modellen bei neurodegenerativen Erkrankungen). Formal informed consent was obtained from all subjects. Four patients from three different families and age-matched controls without history of neurological disorders were recruited. CAG repeats of fibroblasts and iPSCs were measured by the center of Human Genetics at the University Hospital Regensburg (Ute Hehr, MD).
Fibroblasts were resuspended in fibroblast growth medium (FGM, 75% DMEM, 15% FCS, 2 mM l-glutamine, 100 µg/ml penicillin/streptomycin, 2 ng/ml fibroblast growth factor 2) and plated on polystyrene cell culture flasks. Medium was changed twice a week. Fibroblasts were split by removing FGM, adding Trypsin supplemented with 0.05% ethylenediaminetetraacetic acid (EDTA), and incubating at 37 °C until cells detach. FGM was added to the detached cells, the cell suspension was transferred to a centrifugation tube and processed for 5 min at 300 × g RT. The supernatant was removed, cells were resuspended in FGM and plated on a new polystyrene cell culture flask.
For iPSC generation, skin biopsies of study participants were obtained. iPSCs were generated from fibroblasts using the CytoTune iPS 2.0 Sendai Reprogramming Kit (Thermo Fisher Scientific) according to the manufacturer’s instructions. Therefore, cell lines were transduced with Sendai virus containing four reprogramming factors c-MYC, KLF4, OCT3/4, and SOX2. After generation, iPSCs were cultured in human stem cell media StemMACS iPS-Brew XF (Miltenyi Biotec) supplemented with 100 U/mL penicillin/streptomycin on 4 mg/ml Geltrex (GibcoTM) coated polystyrene cell culture plates. Medium was changed every other day. When cell cultures reached 70–80% confluency, cells were passaged. Afterward, iPSCs were washed once with DMEM/F12 (GibcoTM) and incubated with Gentle Cell Dissociation Reagent (Stemcell technologies) for 5 min at room temperature (RT). Gentle Cell Dissociation Reagent was aspirated and StemMACS iPS-Brew XF supplemented with 100 U/mL penicillin/streptomycin was added. Corning® Cell Lifter was used to detach hiPSCs from the cell culture plate. iPSCs were transferred to a new Geltrex-coated plate.
iPSCs were differentiated into cortical neurons using a previously reported protocol. In brief, iPSCs were maintained as described above. iPSCs were dissociated into a single-cell suspension upon 70–80% confluency. Cells were washed once with PBS w/o Mg2+/Ca2+ and were incubated with Accutase for 5 min at 37 °C. Cells were washed with DMEM/F12, centrifuged for 3 min at 300×g at RT, and resuspended in StemMACS iPS-Brew XF supplemented with 10 μM ROCK inhibitor. Cells were seeded on Geltrex-coated plates with the desired density of 300,000 cells per cm2 and incubated for 24 h at 37 °C, 5% CO2. After cells reached confluency the next day, the medium was changed to neural maintenance medium (NMM: DMEM/F-12, neurobasal/B-27/N2, 100 µM GlutaMAX, 100 μM non-essential amino acids, 50 μM 2-mercaptoethanol, 1× penicillin–streptomycin) supplemented with dual-SMAD inhibitors (NIM: 10 μM SB431542, 100 nM LDN193189) to promote neural induction. On day 12, cells differentiated into a neuroepithelial sheet and were further passaged. The cell sheet was gently washed with DMEM/F-12 and incubated for 5 min with Collagenase V (2 mg/ml) at 37 °C for 5 min. The cell sheet was gently washed twice with DMEM/F-12 and finally detached with a 5 ml serological pipette in NIM and gently resuspended into smaller pieces. Cells were passaged in a 1:2 ratio on Geltrex-coated plates. Medium was changed to NMM the next day. Upon appearance of neural rosettes, medium was changed for 2 days in NMM supplemented with 20 ng/ml FGF2 to promote neural stem cell proliferation. On day 19, cells were further passaged and maintained in NMM with medium changes every second day. On day 30, cells were finallly single-cell passaged with Accutase with the desired density of 50,000 cells per cm2. Cells were maintained in NMM for neuronal differentiation with medium changes twice a week till day 35 (Fig. 1) or day 50 (Figs. 2– 6).
Branaplam was reconstituted in DMSO with a concentration of 5 M. Branaplam was supplemented to the cell culture media (FGM, StemMACS iPS-Brew XF or NMM) with a final concentration of 0.46−1000 nM and 0.002% DMSO. Supplemented medium was changed every 24 h for a total of 72 h.
Cells were fixed in 4% paraformaldehyde (PFA) for 20 min at RT and subsequently washed 3× with PBS each. The cells were permeabilized using 0.1% Triton-X-100 and in PBS for 20 min at RT. Then, cells were blocked in 0.3% Triton-X-100 and 3% donkey serum in PBS for 1 h at RT. Afterward, cells were incubated with primary antibodies (rat anti-CTIP2: ab18465, Abcam, 1:500; mouse anti beta-III-Tubulin: G7121, Promega, 1:1000; rabbit anti-PAX6, 901301, BioLegend, 1:200; mouse anti-Nestin, MAB5326, Millipore, 1:500) at 4 °C overnight. After washing, incubation with secondary antibodies and nuclei staining using 1 µg/ml DAPI was performed. The slides were mounted using ProLong(r) Antifade (Invitrogen) solution. Imaging was performed with a Zeiss Laser scanning 780 inverted confocal microscope.
For flow cytometry, cells were dissociated using Accutase for 30 min at 37 °C and resuspended in FC buffer (2% FCS, 0.01% sodium azide in PBS). Cells were dispensed into 15-ml tubes (Sarstedt) at 500,000 cells per tube. For intracellular antigens, cells were fixed and permeabilized using 100 µl BD Fixation/Permeabilization Solution (BD Bioscience) for 10 min, then 1 ml of BD Perm/Wash Buffer was added, cells were incubated for 5 min and subsequently centrifuged at 300×g for 3 min. For intracellular staining of cortical progenitors anti-PAX6-APC (130-123-267, Miltenyi Biotech, 1:100) and anti-NESTIN-PerCp-Cy5.5 (561231, BD Bioscience, 1:100) for an additional 30 min. After a wash step, cells were resuspended in 350 µl FACS buffer. For intracellular staining of neurons, cells were stained using anti-bIII-Tubulin-AF405 (NB600-1018AF405, NovusBio, 1:100) or anti-CTIP2-FITC (ab123449, Abcam, 1:100) for 30 min. Additional controls included applying an antibody solution without one antibody in the full cocktail (“minus 1 control”) and were used to determine potential bleed through of the fluorophores. The flow cytometry experiments were performed with a Cytoflex S machine (laser 405 nm, 488 nm, 561 nm and 638 nm; Beckman Coulter) and analyzed with the CytExpert 2.4 software. To determine cell death via FACS, we used a commercially available kit that uses a fluorescent 660-DEVD-FMK caspase-3/7 inhibitor reagent (ab270785, Abcam) and a fixable cell permeability dye (Live-or-Dye, 32008-T, Biotium). The caspase assay and Live-or-Dye assay reagents were dissolved in 50 µl DMSO, respectively and aliquoted and stored at −20 °C. For the assay, cortical neurons were grown in 24-well plates. At the day of analysis, media was aspirated from the plate and 150 µl DMEM/F12 + Glutamax containing 0.48 µl 660-DEVD-FMK caspase-3/7 inhibitor reagent and 0.15 μl Live-or-Dye assay were applied. After incubation for 45 min at 37 °C. Cells were dissociated, fixed, and stained as stated above. To precisely assess bleed through, single incubation controls (either with 660-DEVD-FMK caspase-3/7 inhibitor reagent or Live-or-Dye assay) were used. The number of Casp3/7+Live-or-Dye- cells vs. Casp3/7-Live-or-Dye- were determined in CTIP2+ and beta-III-Tubulin+ cells. In total, 500,000 NPCs were seeded on GELTREX-coated plates on day 26 of differentiation and treated with Branaplam or DMSO as described before. For EdU incorporation analysis, NPCs were treated with 30 µM EdU for 120 min at 37 °C in 5% CO2. Cells were dissociated using Accutase for 30 min at 37 °C and resuspended in FC buffer (2% FCS, 0.01% sodium azide in PBS). Cells were dispensed into 5 ml tubes (Sarstedt) at 500,000 cells per well. For intracellular antigens, cells were fixed and permeabilized using 100 µl BD Fixation/Permeabilization Solution (BD Bioscience) for 10 min, then 1 ml of BD Perm/Wash Buffer was added, cells were incubated for 5 min and subsequently centrifuged at 300×g for 3 min. EdU-incorporated cells were stained according to manufactures protocol (BaseClick, BCK-FC594-50), followed by staining of anti-SOX2-PerCp-Cy5.5 (BD Bioscience, 561506, 1:100) for 15 min at room temperature. After a wash step, cells were resuspended in 1000 µl FACS buffer buffer containing DAPI (1 μg/ml).
Lysis buffer (150 mM NaCl, 20 mM Tris-HCl (pH 7.5), 1 mM EDTA, 1 mM EGTA, 1% Triton-X-100, 10 mM NaF, 1 mM PMSF, 1× Phosphatase Inhibitor, and 1× Protease Inhibitor in dH2O) was added to the cells for 30 min at 4 °C. Cell lysate was transferred into a tube and centrifuged for 15 min at 2000×g at 4 °C. Supernatant was transferred to a new centrifugation tube and stored at −80 °C. Protein concentration was determined using BCA assay kit (Thermo Fisher Scientific) measuring with CLARIOStar Plus (BMG Labtech).
Meso-Scale-Discovery (MSD) assays to measure total and mutant Huntingtin protein levels were performed by Evotec SE, Hamburg. A first antibody (2B7) is used to capture HTT, and a second antibody (D7F7 or MW1) is used to detect and quantify HTT vial a SULFO-TAG. The 2B7 antibody binds to the first 17 amino acids of HTT. The D7F7 antibody binds downstream of the poly-Q tract, at a single epitope in the center of HTT. In combination, 2B7 and D7F7 can be therefore used to detect total HTT levels regardless of their poly-Q length (tHTT). The MW1 antibody is widely used to detect mutant HTT. Together with 2B7, they are currently used to detect mutant HTT in the CSF of HD patients and therefore used in HTT lowering clinical trials. MW1 has a higher avidity to mHTT due to bivalent binding of the antibody at sites with elongated poly-Q. Thus, this poly-Q-binding antibody do not specifically, but preferentially recognize mHTT and it can be expected that the 2B7/MW1 assay will result in a higher signal for mutant, expanded HTT. However, it is important to note that also non-mutant HTT with shorter poly-Q length is recognized with this antibody combination. The MSD assay plate was coated with 5ug/ml of the N-terminally binding HTT antibody 2B7 (#CH03023, Coriell) in coating buffer (15 mM Na2CO3, 35 mM NaHCO3) overnight. The next day, the plate was washed 3× in wash buffer (0.2% (v/v) Tween 20 in DBPS), blocked for 1 h at RT shaking at 350 rpm (2% (w/v) Probumin in wash buffer) and subsequently washed 3× again. The MSD plate was then incubated with the protein sample derived from the various cells (10 µl sample/well) for 1 h at RT shaking at 350 rpm. In parallel, a standard of defined concentrations of recombinant human HTT with 23Q or 73Q was applied. After incubation, the plate was washed 3x in wash buffer. Next, 10 µl of the detection antibodies were added to the MSD plates: 0.5ug/ml D7F7 antibody (#5656, Cell Signaling) for tHTT detection, or 5ug/ml MW1 antibody (#MABN2427, Sigma-Aldrich) binding the polyQ region in exon 1 for mutant HTT detection. MW1 was used directly labeled with a SULFO-Tag and incubated for 1 h at RT shaking at 350 rpm and subsequently washed 3x. For D7F7, after three washes, a SULFO-Tag-labeled anti-rabbit secondary antibody (MSD) was incubated for 1 h at RT, and the plate was subsequently washed 3×. MSD read buffer was added to the plate. If the detection antibody binds to the sample in close proximity to the MSD plate an electrochemiluminescent signal is emitted and detected at 620 nm. The total and mutant HTT levels were calculated according to the generated standard curves and normalized to protein input. The signal values were back-calculated to the standard that was run in parallel (Q23 for 2B7/D7F7 assay and Q73 for 2B7/MW1 assay), resulting in MSD signal values of a sample equivalent to a certain HTT Q23 or HTT Q73 concentration. The assay does not allow to compare the numerical results from the total HTT assay and the mutant HTT assay with each other. The numerical values from both assays cannot be compared by mathematical addition as they are two separate assays with different antibodies that have not identical binding properties (e.g., D7F7 binds once, MW1 can bind multiple times depending on poly-Q length).
Cytotoxicity was measured during Branaplam treatment using the ToxiLight Bio assay kit (Lonza) according to the manufacturer’s instructions. Therefore, the supernatant was collected after 72 h of Branaplam treatment. The positive control was a supernatant of untreated cells incubated with 10% Triton-X-100 for 20 min at 37 °C. Triplicates 20 µl/sample were transferred to a 96-well plate. In total, 100 µl of adenylate kinase detection reagent (ToxiLight Bio assay kit, Lonza) was added and incubated for 5 min at RT. The resulting luminescence was measured by the CLARIOStar Plus (BMG Labtech).
The cortical neuron samples were scraped off the plate in DBPS and transferred to a 1.5-ml Eppendorf tube and centrifuged and subsequently, dry ice flash frozen and stored at −80 °C until further processing. The cells were lyzed in 150 µl ice-cold RIPA buffer. The cell lysate was sonicated for five minutes with 30 s on/off on a low-intensity level using a Bioruptor. Afterward, the lysate was centrifuged at 100,000×g for 30 min at 4 °C. This supernatant represents the soluble protein fraction. The pellet was washed with ice-cold RIPA once and then resuspended in 75 μl urea buffer (7 M urea, 3 M thiourea, 4% CHAPS, 30 mM Tris). Subsequently, the suspension was sonicated using a Bioruptor under the same conditions as described before. The sonicated suspension is centrifuged at 100,000×g for 30 min at 4 °C again. The resulting supernatant reflects the insoluble fraction. The protein content in each fraction was quantified with bicinchoninic acid (BCA) assay. Equal concentrations were applied, and loading buffer and DTT (final concentration of 100 mM) was added. The samples were incubated at 55 °C for 30 min. The samples were then used for western blotting.
All immunoblots were run on 4–12% Bis-Tris gels with NuPAGE MOPS running buffer for 90 min at 180 V. Proteins were transferred to a PVDF membrane with 10% methanol in NuPAGE transfer buffer at 30 V overnight at 4 °C. The membrane was then blocked for 1 h in 5% dry milk in TBS-T and primary antibody (RBFOX2: A300-864A, Bethyl Laboratories Inc., 1:1000; ILF3: A303-651A-T, Bethyl Laboratories Inc., 1:2000; QKI: A300-183A-T, Bethyl Laboratories Inc., 1:2000; U2AF2: A303-667A-T, Bethyl Laboratories Inc., 1:2000; TIAL1: RN059PW, MBL international, 1:1000) was incubated overnight at 4 °C. Afterwards, the membrane was washed twice for approximately seven minutes in TBS-T and then incubated for one hour with the secondary HRP-conjugated antibody for one hour at room temperature. The membrane was washed three times with TBS-T and incubated in the dark with ECL solution. Film development was performed in the dark with various exposure times. For the quantification of western blots, densitometric analysis was performed using Fiji. The signal was normalized to the corresponding signal in the Coomassie-stained gels reflecting the total protein amount.
RNA was extracted using the RNeasy kit (Qiagen) according to the manufacturer’s instructions. RNA concentrations were measured using a NanoDrop. The GoScript Reverse Transcriptase cDNA Synthesis kit (Promega) was used to generate cDNA from fibroblasts and cortical neurons using random primers. RNA was mixed with random primers and incubated for 5 min at 70 °C and placed on ice for 5 min. The remaining reaction mix was added and incubated for 5 min at 25 °C, followed by 1 h 42 °C extension period and a 15 min 70 °C inactivation. The GoTaq 2× Mastermix (Promega) was used to amplify novel exon inclusion in HTT, amplifying 0.5 µl of the template with 1 µl of fwd primer (100 µM stock, GTCATTTGCACCTTCCTCCT) and 1 µl rev primer (100 µM stock, TGGATCAAATGCCAGGACAG), 5 µl Mastermix and 2.5 µl DNase/RNase-free water. Primer sequences were obtained from the Novartis patent (WO2021084495A1). The mix was amplified with the following conditions: 95 °C for 3 min, and 34 cycles of 95 °C for 30 s, 60 °C for 20 s, and 72 °C for 60 s. A final extension of 72 °C for 5 min was added at the end. The products were run on a 2% Agarose gel with RotiGel stain. (Carl Roth GmbH) at 125 V. A random selection of 88 bp and ~200 bp bands in Ctrl and HD was cut out and purified to verify their correct identity by Sanger sequencing.
A total of 500 ng per sample were sent for RNA sequencing to Azenta Life Sciences (Genewiz Leipzig, Germany) for 150 bp paired-end sequencing with Poly-A selection. For fibroblasts, four Ctrl samples and four HD samples with DMSO or Branaplam treatment were sent and sequenced at a depth of >20 million reads in each sample. For iPSC-derived cortical neurons, three Ctrl samples and three HD samples with DMSO or Branaplam treatment were sent and sequenced at a depth of >37 million reads in each sample. After obtaining the fastq files, adapters were trimmed using Trimmomatic and aligned to the human genome (GRCh38) using STAR. In every sample, >90% of reads mapped uniquely to the human genome. Reads were assigned to genes in the gencode annotation (version 26) using the featureCounts module within the Subread package. Reads Per Kilobase of transcript, per Million mapped reads (RPKM) were calculated from the obtained counts to normalize for gene expression.
For differential splicing rMATS (version 4.2.0) was used with the novelSS flag to identify non-annotated exons. The gencode annotation (version 26) was used to define known exons. The output files considering only the junction counts were used for further analysis. A negative value of the InclusionLevelDifference reflects an inclusion of a given exon in the samples of the target condition and a positive value of the InclusionLevelDifference reflects an exclusion of a given exon in the samples of the target condition. Subsequently, the files from the different splice types (cassette exon, A5SS, A3SS, RI and MXE) were combined into one file. All downstream analyses were performed in Python 3. Only exon junctions that were covered with at least ten counts in each sample of a given dataset were considered. A unique index was generated, referring to a specific AS event with the aim to identify the identical exon junction in separate rMATS analyses. An exon was called as differentially alternatively spliced in each dataset if the FDR was below 0.05 and the absolute value of the InclusionLevelDifference was more than 0.1. The overlap of differentially spliced events was visualized with the Venn function in matplotlib library. For k-means clustering, the k-means method from the sklearn.cluster module from sciki-lean was used (specifications: init = “ranodm”, n_clusters = 10, n_init = 10, max_iter = 300, random_state = 42). AS events significant in any of the four comparisons (fibroblasts-Ctrl DMSO vs Branaplam, fibroblasts-HD DMSO vs Branaplam, cortical neurons-Ctrl DMSO vs Branaplam, cortical neurons-HD DMSO vs Branaplam) datasets were clustered into 10 clusters according to the inclusion value differences in the respective dataset. Exon junctions that were not detected with a sufficient number of reads were masked and visualized in black. The inclusion level difference of each cluster was additionally visualized with violin plots. In order to determine the overlap of Branaplam-induced events of this study to Branaplam-induced events reported by Monteys and colleagues in HEK293 cells, pybedtool was used to compare overlap of genomic locations. To determine the effect of aberrant AS reversal upon Branaplam treatment, the individual inclusion values were used from HD-DMSO, HD-Branaplam, and Ctrl-DMSO samples in fibroblasts and cortical neurons, respectively. Only significant HD alternative splicing events (Ctrl DMSO vs. HD DMSO) in a respective cell type that were detected in all samples analyzed in a cell type (>10 reads in every single sample) were used. The reversal of aberrant AS was investigated in a quantitative and qualitative manner. For quantitative measurement, the absolute inclusion value difference was calculated by subtracting HD-DMSO or HD-Branaplam inclusion values from Ctrl-DMSO inclusion values and taking the absolute value. The statistical significance of the absolute inclusion level difference was determined using scipy.stats.ranksums. For qualitative measurement, the mean inclusion values of Ctrl-DMSO and HD-DMSO and HD-Branaplam samples in each cell type were also visualized in a scatter plot. A reversal of aberrant AS was determined if the inclusion level differences in HD-Branaplam samples dropped below an absolute value of 0.1.
In order to determine RNA-binding proteins that are enriched in alternatively spliced events in HD, we made use of the ENCODE database and their eCLIP-seq datasets. We downloaded eCLIP seq peak files aligned to GRCh38 with the Irreproducible Discovery Rate (IDR) peaks (released by November 2021). A peak was considered significant if negative log10(P value) ≥ 3 and the log2(fold change) ≥ 3. To determine if an eCLIP-seq peak was present in an exon junction in HD, the rMATS output (fibroblasts Ctrl-DMSO vs HD-DMSO or cortical neurons Ctrl-DMSO vs HD-DMSO) of interest was converted into a bed format encompassing the region starting from the upstream exon start to the downstream exon end. The rMATS bed was intersected with the significant eCLIP-seq peak file using pybedtools (-u True). The statistical significance of the enrichment was computed using hypergeometric test with all events that passed the coverage threshold as the background. In order to determine the significance of RBPs that are rather enriched in rescued HD AS events vs not rescued HD AS events by Branaplam, Fisher’s exact test was applied to compare RBP binding in the two sets of AS events.
To determine the sequence preferences of Branaplam-induced AS sites at the 3’ and 5’ splice site, we calculated kmer enrichments (4mer, 6mer, and 8mer) 5b upstream and downstream of the 3’ and 5’ splice site, respectively. As a background, we also calculated the 5b upstream and downstream of the 3’ splice site of the respective upstream exon and the 5’ splice site of the respective downstream exon. Kmers were counted with Kvector (https://github.com/olgabot/kvector), and significance was determined with Fisher’s exact test using scipy.stats.
GraphPad Prism 9 was used to visualize data and calculate statistics for pair-wise and grouped analyses (HTT protein measurements, toxilight assay, FACS quantification, densitometric quantification of HTT PCR, HTT RPKM values). DMSO samples and their respective Branaplam samples were considered as paired. Normal distribution was assessed with Shapiro–Wilk test. When comparing two conditions, Welch’s test was used if normal distribution was confirmed and Mann–Whitney test was used for non-normally distributed data. When comparing multiple groups (e.g., different Branaplam concentrations), one-way ANOVA with Geisser–Greenhouse correction was used if normal distribution was confirmed and Friedman test was used for non-normally distributed data with Dunnett’s or Dunn’s post hoc test, respectively, to identify differences between individual groups. For grouped analyses (e.g., DMSO vs. Branaplam in Ctrl vs. HD), two-way ANOVA was used. The statistical test used for calculating the significance of each graph is indicated in the figure legend. A P value ≤ 0.05 was considered as significant.
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